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BACKGROUND: The high rate of postoperative hypoproteinemia in patients undergoing hip revision is associated with severe trauma, which affects the rapid recovery of patients. OBJECTIVE: To investigate the risk factors of perioperative hypoproteinemia in patients with hip revision, and to provide guidance for early screening of highrisk patients with postoperative hypoproteinemia. METHODS: According to the inclusion and exclusion criteria, 161 patients who underwent hip revision were divided into hypoproteinemia group (76 cases) and normal group (85 cases). The rate of hypoproteinemia was 47.2%. Data such as age, gender, body mass index, osteoporosis, operation time, preoperative erythrocytes, preoperative hemoglobin, preoperative leukocytes, preoperative platelets, preoperative fibrinogen, preoperative C-reaction protein, preoperative sedimentation rate, preoperative blood calcium, preoperative albumin, postoperative drainage tube placement, American Society of Anesthesiologists score, and postoperative hypoproteinemia were collected. SPSS software was used to analyze the independent risk factors of hypoproteinemia after hip revision using multivariate binary logistic regression analysis. R software was used to construct the nomogram prediction model. Receiver operating characteristic curve and calibration curve and decision curve were drawn to evaluate the model. RESULTS AND CONCLUSION: (1) Univariate analysis results showed that body mass index, preoperative erythrocytes, preoperative hemoglobin, preoperative platelets, preoperative fibrinogen, preoperative C-reaction protein, and operation time were significantly different between the two groups (P < 0.05). (2) Multivariate binary Logistic regression analysis results showed that body mass index (OR=0.859, P=0.021), operation time (OR=1.010, P=0.002), preoperative erythrocytes (OR=0.424, P=0.036), and preoperative C-reaction protein (OR=1.043, P=0.032) levels were independent risk factors for postoperative hypoproteinemia in patients with hip revision. (3) Based on four independent risk factors: body mass index, operation time, preoperative erythrocytes and preoperative C-reaction protein, the nomogram can effectively predict the risk of hypoproteinemia after hip revision. This nomogram prediction model has good differentiation and accuracy, and may lead to better clinical net benefits for patients. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Could the risk of subsequent pregnancy loss be predicted based on the risk factors of recurrent pregnancy loss (RPL) patients? SUMMARY ANSWER A nomogram, constructed from independent risk factors identified through multivariate logistic regression, serves as a reliable tool for predicting the likelihood of subsequent pregnancy loss in RPL patients. WHAT IS KNOWN ALREADY Approximately 1–3% of fertile couples experience RPL, with over half lacking a clear etiological factor. Assessing the subsequent pregnancy loss rate in RPL patients and identifying high-risk groups for early intervention is essential for pregnancy counseling. Previous prediction models have mainly focused on unexplained RPL, incorporating baseline characteristics such as age and the number of previous pregnancy losses, with limited inclusion of laboratory and ultrasound indicators. STUDY DESIGN, SIZE, DURATION The retrospective study involved 3387 RPL patients who initially sought treatment at the Reproductive Immunology Clinic of Renji Hospital, Shanghai Jiao Tong University School of Medicine, between 1 January 2020 and 31 December 2022. Of these, 1153 RPL patients met the inclusion criteria and were included in the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS RPL was defined as two or more pregnancy losses (including biochemical pregnancy loss) with the same partner before 28 weeks of gestation. Data encompassing basic demographics, laboratory indicators (autoantibodies, peripheral immunity coagulation, and endocrine factors), uterine and endometrial ultrasound results, and subsequent pregnancy outcomes were collected from enrolled patients through initial questionnaires, post-pregnancy visits fortnightly, medical data retrieval, and telephone follow-up for lost patients. R software was utilized for data cleaning, dividing the data into a training cohort (n = 808) and a validation cohort (n = 345) in a 7:3 ratio according to pregnancy success and pregnancy loss. Independent predictors were identified through multivariate logistic regression. A nomogram was developed, evaluated by 10-fold cross-validation, and compared with the model incorporating solely age and the number of previous pregnancy losses. The constructed nomogram was evaluated using the AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Patients were then categorized into low- and high-risk subgroups. MAIN RESULTS AND THE ROLE OF CHANCE We included age, number of previous pregnancy losses, lupus anticoagulant, anticardiolipin IgM, anti-phosphatidylserine/prothrombin complex IgM, anti-double-stranded DNA antibody, arachidonic acid-induced platelet aggregation, thrombin time and the sum of bilateral uterine artery systolic/diastolic ratios in the nomogram. The AUCs of the nomogram were 0.808 (95% CI: 0.770–0.846) in the training cohort and 0.731 (95% CI: 0.660–0.802) in the validation cohort, respectively. The 10-fold cross-validated AUC ranged from 0.714 to 0.925, with a mean AUC of 0.795 (95% CI: 0.750–0.839). The AUC of the nomogram was superior compared to the model incorporating solely age and the number of previous pregnancy losses. Calibration curves, DCAs, and CICAs showed good concordance and clinical applicability. Significant differences in pregnancy loss rates were observed between the low- and high-risk groups (P  <   0.001). LIMITATIONS, REASONS FOR CAUTION This study was retrospective and focused on patients from a single reproductive immunology clinic, lacking external validation data. The potential impact of embryonic chromosomal abnormalities on pregnancy loss could not be excluded, and the administration of medication to all cases impacted the investigation of risk factors for pregnancy loss and the model's predictive efficacy. WIDER IMPLICATIONS OF THE FINDINGS This study signifies a pioneering effort in developing and validating a risk prediction nomogram for subsequent pregnancy loss in RPL patients to effectively stratify their risk. We have integrated the nomogram into an online web tool for clinical applications. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the National Natural Science Foundation of China (82071725). All authors have no competing interests to declare. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Purpose: Papillary thyroid carcinoma (PTC) frequently coexists with Hashimoto's thyroiditis (HT), which poses challenges in detecting central lymph node metastasis (CLNM) and determining optimal surgical management. Our study aimed to identify the independent predictors for CLNM in PTC patients with HT and develop a comprehensive prediction model for individualized clinical decision-making. Patients and methods: In this retrospective study, a total of 242 consecutive PTC patients who underwent thyroid surgery and central lymph node dissection between February 2019 and December 2021 were included. 129 patients with HT were enrolled as the case group and 113 patients without HT as control. The results of patients' general information, laboratory examination, ultrasound features, pathological evaluation, and BRAF mutation were collected. Multivariate logistic regression analysis was used to identify independent predictors, and the prediction model and nomogram were developed for PTC patients with HT. The performance of the model was assessed using the receiver operating characteristic curve, calibration curve, decision curve analysis, and clinical impact curve. In addition, the impact of the factor BRAF mutation was further evaluated. Results: Multivariate analysis revealed that gender (OR = 8.341, P = 0.013, 95% CI: 1.572, 44.266), maximum diameter (OR = 0.316, P = 0.029, 95% CI: 0.113, 0.888), multifocality (OR = 3.238, P = 0.010, 95% CI: 1.319, 7.948), margin (OR = 2.750, P = 0.046, 95% CI: 1.020, 7.416), and thyrotropin receptor antibody (TR-Ab) (OR = 0.054, P = 0.003, 95% CI: 0.008, 0.374) were identified as independent predictors for CLNM in PTC patients with HT. The area under the curve of the model was 0.82, with accuracy, sensitivity, and specificity of 77.5%, 80.3% and 75.0%, respectively. Meanwhile, the model showed satisfactory performance in the internal validation. Moreover, the results revealed that BRAF mutation cannot further improve the efficacy of the prediction model. Conclusion: Male, maximum diameter > 10mm, multifocal tumors, irregular margin, and lower TR-Ab level have significant predictive value for CLNM in PTC patients with HT. Meanwhile, BRAF mutation may not have a valuable predictive role for CLNM in these cases. The nomogram constructed offers a convenient and valuable tool for clinicians to determine surgical decision and prognostication for patients. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion To evaluate clinical factors prior to methotrexate (MTX) treatment for tubal ectopic pregnancy and to apply the data to a prediction model for treatment success.A retrospective cohort study was conducted during 2014–2022. Of the 808 patients with a tubal ectopic pregnancy, 372 with a β‐hCG level less than 5000 IU/L were treated with a single dose of MTX and were included in this study. Pretreatment factors, including patient characteristics, initial β‐hCG level, and sonographic parameters, were compared between those who achieved complete resolution and those who needed additional MTX or surgical intervention. A logistic regression model and multivariable analysis were used to predict success. A graphic nomogram was generated to represent the model.Complete resolution of the ectopic pregnancy was achieved in 290 (77.9%) patients after a single dose of MTX. A second dose or surgical intervention was required for 82 (22.0%): 49 (13.2%) received a second dose of MTX and 33 (8.9%) underwent laparoscopic salpingectomy. In the MTX Success group compared to the MTX Failure group, the median β‐hCG levels were lower (746 vs 1347 IU/L, P < 0.001) and the presence of a yolk sac and a fetal pole were less frequent. The predictive model, based on significant variables, includes initial β‐hCG concentration and the visibility of a yolk sac or fetal pole. Analysis with cross‐validation techniques revealed that the model was both accurate and discriminative.A predictive nomogram was developed to predict the success of single‐dose MTX treatment for tubal ectopic pregnancy. [ABSTRACT FROM AUTHOR]

Background: Cervical lymph node metastasis (CLNM) significantly impacts the prognosis of papillary thyroid carcinoma (PTC) patients. Accurate CLNM prediction is crucial for surgical planning and patient outcomes. This study aimed to develop and validate a nomogram-based risk stratification system to predict CLNM in PTC patients. Methods: This retrospective study included 1069 patients from Zhongshan Hospital and 253 from the Qingpu Branch of Zhongshan Hospital. Preoperative ultrasound (US) data and various nodule characteristics were documented. Patients underwent lobectomy with central lymph node dissection and lateral dissection if suspicious. Multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest algorithm were used to identify CLNM risk factors. A nomogram was constructed and validated internally and externally. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration plots, DeLong's test, decision curve analysis (DCA), and the clinical impact curve (CIC). Results: Six independent CLNM risk factors were identified: age, sex, tumor size, calcification, internal vascularity, and US-reported CLNM status. The model's area under the curve (AUC) was 0.77 for both the training and the external validation sets. Calibration plots and Hosmer‒Lemeshow (HL) tests showed good calibration. The optimal cutoff value was 0.57, with a sensitivity of 58.02% and a specificity of 83.43%. Risk stratification on the basis of the nomogram categorized patients into low-, intermediate-, and high-risk groups, effectively differentiating the likelihood of CLNM, and an online calculator was created for clinical use. Conclusion: The nomogram accurately predicts CLNM risk in PTC patients, aiding personalized surgical decisions and improving patient management. [ABSTRACT FROM AUTHOR]

Background: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation. Methods: Eight hundred forty-six patients with indwelling urinary catheters who were resuscitated in a post-anesthesia care unit (PACU). Trained researchers used the CRBD 4-level assessment method to evaluate the severity of a patient's CRBD. They then inputted 24 predictors into six different machine learning algorithms. The performance of the models was evaluated using metrics like the area under the curve (AUC). Results: The AUCs of the six models ranged from 0.82 to 0.89. Among them, the RF model displayed the highest predictive ability, with an AUC of 0.89 (95%CI: 0.87, 0.91). Additionally, it achieved an accuracy of 0.93 (95%CI: 0.91, 0.95), 0.80 sensitivity, 0.98 specificity, 0.94 positive predictive value (PPV), 0.92 negative predictive value (NPV), 0.87 F1 score, and 0.07 Brier score. The logistic regression (LR) model has achieved good results (AUC:0.87) and converted into a nomogram. Conclusions: The study has successfully developed a machine learning prediction model that exhibits excellent predictive capabilities in identifying patients who may develop moderate-to-severe CRBD after undergoing general anesthesia. Furthermore, the study also presents a nomogram, which serves as a valuable tool for clinical healthcare professionals, enabling them to intervene at an early stage for better patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression. Methods: All eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model's performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance. Results: 299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017–1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003–1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175–2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005–1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035–26.858; P < 0.001) and hemorrhage symptoms (OR, 3.414; 95% CI, 1.096–10.974; P = 0.035). The AUC-ROC, C-index, calibration plots, and DCA demonstrated the robust performance of the nomogram in predicting severity at admission, and Kaplan-Meier survival analysis indicated its utility in predicting 28-day mortality among SFTS patients. The dynamic nomogram is accessible at https://sfts.shinyapps.io/SFTS%5fseverity%5fnomogram/. Conclusion: This study provided a practical and readily applicable tool for the early identification of high-risk SFTS patients, enabling the timely initiation of intensified treatments and protocol adjustments to mitigate disease progression. [ABSTRACT FROM AUTHOR]

Purpose: Sarcopenia is a common complication of diabetes. Nevertheless, precise evaluation of sarcopenia risk among patients with diabetes is still a big challenge. The objective of this study was to develop a nomogram model which could serve as a practical tool to diagnose sarcopenia in patients with diabetes. Methods: A total of 783 participants with diabetes from China Health and Retirement Longitudinal Study (CHARLS) 2015 were included in this study. After oversampling process, 1,000 samples were randomly divided into the training set and internal validation set. To mitigate the overfitting effect caused by oversampling, data of CHARLS 2011 were utilized as the external validation set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were employed to explore predictors. Subsequently, a nomogram was developed based on the 9 selected predictors. The model was assessed by area under receiver operating characteristic (ROC) curves (AUC) for discrimination, calibration curves for calibration, and decision curve analysis (DCA) for clinical efficacy. In addition, machine learning models were constructed to enhance the robustness of our findings and evaluate the importance of the predictors. Results: 9 factors were selected as predictors of sarcopenia for patients with diabetes. The nomogram model exhibited good discrimination in training, internal validation and external validation sets, with AUC of 0.808, 0.811 and 0.794. machine learning models revealed that age and hemoglobin were the most significant predictors. Calibration curves and DCA illustrated excellent calibration and clinical applicability of this model. Conclusion: This comprehensive nomogram presented high clinical predictability, which was a promising tool to evaluate the risk of sarcopenia in patients with diabetes. [ABSTRACT FROM AUTHOR]

Background: Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery. Methods: A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting. Results: The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities. Conclusions: This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia. [ABSTRACT FROM AUTHOR]

Depressive symptoms (DS) have become a global public health problem. However, a risk prediction model for DS in the elderly population has not been established. The purpose of this study was to develop and validate a predictive nomogram to screen for DS in the elderly population. A cross-sectional data of 3396 participants aged 60 and over were obtained from the China Health and Retirement Longitudinal Study 2018 (CHARLS). Participants were divided into the development and validation set. Predictive factors were selected through a single-factor analysis, and then a predictive model nomogram was established. The discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, and decision curve analyses (DCA). A total of 2379 and 1017 participants were included in the development and validation set, respectively. The analysis found that gender, residence, dyslipidemia, self-rated health, and ADL disability were risk factors for DS in older adults, and were included in the final model. This nomogram showed an acceptable predictive performance as evaluated by the area under the ROC curve with values of 0.684 (95 % confidence interval (CI): 0.663–0.706) and 0.687 (95 % CI: 0.655–0.719) in the development and validation set, respectively. The calibration curve indicated that the model was accurate, and DCA demonstrated a good clinical application value. Five factors were selected to establish a nomogram for predicting DS in older adults. The nomogram has a good evaluation performance and can be used as a reliable tool to predict DS among older adults. • Developed and validated a predictive nomogram to screen for depressive symptoms in the elderly. • Identified gender, residence, dyslipidemia, self-rated health, and ADL disability as risk factors. • Model demonstrated accuracy through calibration curves and good clinical application value. • Provides a tool for quick identification of at-risk elderly, aiding targeted interventions. • Significant practical value in areas with limited professional operators and resources. [ABSTRACT FROM AUTHOR]

Background: Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis. Objective: This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model. Methods: This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model's calibration. Results: Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381–0.734, P < 0.001), Age (OR = 1.760, 95% CI: 1.251–2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454–2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154–5.346, P = 0.020), Complications (OR = 1.516, 95% CI: 1.033–2.226, P = 0.033), Anemia (OR = 2.668, 95% CI: 1.850–3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682–3.544, P < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167–0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%. Conclusion: The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments. [ABSTRACT FROM AUTHOR]

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases. Methods: From January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance. Results: The multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143–1.673), high viral load (p < 0. 001, OR = 1.496, 95% CI 1.290–1.735) and high level of serum urea (> 8.0 μmol/L) (p < 0.001, OR = 4.433, 95% CI 1.888–10.409). The AUC of the nomogram based on these four factors was 0.813 (95% CI, 0.758–0.868). The bootstrap resampling internal validation model performed well, and decision curve analysis indicated a high net benefit. Conclusions: The nomogram based on age, elevated PT, high serum urea level, and high viral load can be used to help early identification of SFTS patients at risk of fatality. [ABSTRACT FROM AUTHOR]

Background: Radioactive iodine (RAI) therapy is a widely used treatment for Graves’ Hyperthyroidism (GH). However, various factors can impact the nonremission rate of GH after single RAI therapy. This study aimed to develop an online dynamic nomogram to assist physicians in providing personalized therapy for GH. Methods: Data from 454 GH patients who received RAI therapy were retrospectively reviewed and included in the present study. The univariate and multivariate analysis were conducted to investigate and identify independent influencing factors. The nomogram was developed based on the training cohort to explore non-remission rates. Finally, the reliability and accuracy of the constructed nomogram model were verified in the validation cohort via the calibration, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: 24-hours radioactive iodine uptake (RAIU24h), effective half-life (Teff), total iodine dose (TID) and iodine dose per gram of thyroid tissue (IDPG) were independent predictors. The nomogram had a high C-index 0.922 (95% CI: 0.892–0.953), for predicting non-remission. The calibration curves demonstrated excellent consistency between the predicted and the actual probability of non-remission. ROC analysis showed that the AUC of the nomogram model and the four independent factors in the training cohort were 0.922, 0.673, 0.760, 0.761, and 0.786, respectively. The optimal cutoff value for the total nomogram scores was determined to be 155. A total score of ≥155 indicates a higher likelihood of non-remission after a single RAI therapy for GH, whereas a score below 155 suggests a greater likelihood of remission. Additionally, the DCA curve indicated that this nomogram had good clinical utility in predicting non-remission. Conclusion: An online nomogram was constructed with good predictive performance, which can be used as a practical approach to predict and assist physicians in making personalized therapy decisions for GH patients. [ABSTRACT FROM AUTHOR]

Background: Poor ovarian response (POR) is associated with decreased clinical pregnancy rates, emphasizing the need for developing clinical prediction models. Such models can improve prognostic accuracy, personalize medical interventions, and ultimately enhance live birth rates among patients with POR. Objective: This study aims to develop and validate a prognostic model for predicting clinical pregnancy outcomes in individuals with POR undergoing in vitro fertilization/ intracytoplasmic sperm injection (IVF/ICSI) cycles. Methods: A retrospective cohort of 969 patients with POR undergoing fresh embryo transfer cycles at the Reproductive Center of Fujian Maternal and Child Health Center from January 2018 to January 2022 was included. The cohort was randomly divided into model (n = 678) and validation (n = 291) groups in a 7:3 ratio. A single-factor analysis was performed on the model group to identify variables influencing clinical pregnancy. Optimal variables were selected using LASSO regression, and a clinical prediction model was constructed using multivariate logistic regression analysis. The model's calibration and discrimination were assessed using receiver operating characteristic (ROC) and calibration curves, while the clinical utility was evaluated using decision curve analysis. Results: Multivariate logistic regression analysis revealed that the age of the women (odds ratio [OR] 0.936, 95% confidence interval [CI] 0.898–0.976, P = 0.002), body mass index (BMI) ≤ 24 (OR 2.748, 95% CI 1.724–4.492, P < 0.001), antral follicle count (AFC) (OR 1.232, 95% CI 1.073–1.416, P = 0.003), anti-Müllerian hormone (AMH) (OR 1.67, 95% CI 1.178–2.376, P = 0.004), number of mature oocytes (OR 1.227, 95% CI 1.075–1.403, P = 0.003), number of embryos transferred (OR 1.692, 95% CI 1.132–2.545, P = 0.011), and transfer of high-quality embryos (OR 3.452, 95% CI 1.548–8.842, P = 0.005) were independent predictors of clinical pregnancy in patients with POR. According to the receiver operating characteristic (ROC) analysis, the prediction model exhibited an area under the curve (AUC) of 0.752 (0.714, 0.789) in the model group and 0.765 (0.708, 0.821) in the validation group. The clinical decision curve demonstrated that the model held maximum clinical utility in both cohorts when the threshold probability of clinical pregnancy ranged from 6–81% to 12–82%, respectively. Conclusion: Clinical pregnancy outcomes in patients with POR who underwent IVF/ICSI treatment were influenced by several independent factors, including the age of the women, BMI, AFC, AMH, number of mature oocytes, number of embryos transferred, and transfer of high-quality embryos. A clinical prediction model based on these factors exhibited favorable clinical predictive and applicative value. Therefore, this model can serve as a valuable tool for clinical prognosis, intervention, and facilitating personalized medical treatment. [ABSTRACT FROM AUTHOR]

Objective: This study aims to develop a predictive model for estimating the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE) and to elucidate the relationship between various factors and ACD Methods: Individuals diagnosed with SLE for at least one year were enrolled and categorized into two groups: those with ACD and those without anemia symptoms. Patients were randomly assigned to training and test sets at an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors, followed by logistic regression for modeling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both training and test sets. Results: The study included a total of 216 patients, with 172 in the training set and 44 in the test set. LASSO identified 6 variables for constructing the predictive model, resulting in an area under the curve (AUC) of 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Calibration curves indicated consistency between expected and observed probabilities. DCA indicated that the model yielded a net benefit with threshold probabilities ranging from 20% to 90% in the training set and from 10% to 80% in the test set. Conclusion: This study presents a predictive model for assessing the risk of ACD in SLE patients. The model effectively captures the underlying mechanism of ACD in SLE and empowers clinicians to make well-informed treatment adjustments. Key Points • Development of a New Predictive Model: This study introduces a new predictive model to evaluate the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE). The model utilizes routine laboratory parameters to identify high-risk individuals, addressing a significant gap in current clinical practice. • Reflection of Potential Mechanisms for ACD Development: By incorporating the factors needed to construct the predictive model, this study also sheds light on the potential mechanisms of ACD development in SLE patients. [ABSTRACT FROM AUTHOR]

Introduction: Lung cancer remains a significant global health burden, with nonsmall cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. Methods: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). Results: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. Conclusion: The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources. Methods: Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65  years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639- 0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged_los1/) was also established to facilitate clinical use. Conclusion: The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management. [ABSTRACT FROM AUTHOR]

Methods Results Conclusion The study employed a retrospective survey of 458 older individuals with T2D residing in a Chinese community, conducted between June 2020 and May 2021, to develop a predictive model for frailty. Among the participants, 83 individuals (18.1%) were diagnosed with frailty using modified frailty phenotypic criteria. The predictors of frailty in this community‐dwelling older population with T2D were determined using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression. These predictors were utilized to construct a nomogram. The discrimination, calibration, and medical usefulness of the prediction model were assessed through the C‐index, calibration plot, and decision curve analysis (DCA), respectively. Additionally, internal validation of the prediction model was conducted using bootstrapping validation.The developed nomogram for frailty prediction predominantly incorporated age, smoking status, regular exercise, depression, albumin (ALB) levels, sleep condition, HbA1c, and polypharmacy as significant predictors. Our prediction model demonstrated excellent discrimination and calibration, as evidenced by a C‐index of 0.768 (95% CI, 0.714–0.822) and strong calibration. Internal validation yielded a C‐index of 0.732, further confirming the reliability of the model. DCA indicated the utility of the nomogram in identifying frailty among the studied population.The development of a predictive model enables a valuable estimation of frailty among community‐dwelling older individuals with type 2 diabetes. This evidence‐based tool provides crucial guidance to community healthcare professionals in implementing timely preventive measures to mitigate the occurrence of frailty in high‐risk patients. By identifying established predictors of frailty, interventions and resources can be appropriately targeted, promoting better overall health outcomes and improved quality of life in this vulnerable population. [ABSTRACT FROM AUTHOR]

To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model. This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected. Univariate analysis and multivariate logistic regression were used to analyze the influencing factors of UTI in hospitalized patients with spinal cord injuries. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic curve and calibration curve. Logistic regression analysis showed that the American Spinal Cord Injury Association (ASIA)-E grade (compared with ASIA-A grade) was an independent protective factor for UTI in hospitalized patients with spinal cord injury (odds ratio < 1, P < 0.05), while white blood cell count and indwelling catheter were independent risk factors for UTI in hospitalized patients with spinal cord injury (odds ratio > 1, P < 0.05). Based on this, a nomogram risk predictive model for predicting UTI in hospitalized rehabilitation patients with spinal cord injury was constructed, which proved to have good predictive efficiency. In the training group and the verification group, the area under the receiver operating characteristic curve of the nomogram model is 0.808 and 0.767, and the 95% confidence interval of the area under the receiver operating characteristic curve of the nomogram in the training group and the verification group is 0.760∼0.856 and 0.688∼0.845, respectively, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of UTI in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has a good calibration degree in both the training group and the verification group (P = 0.329, 0.067). ASIA classification level, white blood cell count, and indwelling catheter are independent influencing factors of UTI in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of UTI in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment, and nursing strategies in time. [ABSTRACT FROM AUTHOR]

There has been no severity evaluation model for pediatric patients with hemophagocytic lymphohistiocytosis (HLH) that uses readily available parameters. This study aimed to develop a novel model for predicting the early mortality risk in pediatric patients with HLH using easily obtained parameters whatever etiologic subtype. Patients from one center were divided into training and validation sets for model derivation. The developed model was validated using an independent validation cohort from the second center. The prediction model with nomogram was developed based on logistic regression. The model performance underwent internal and external evaluation and validation using the area under the receiver operating characteristic curve (AUC), calibration curve with 1000 bootstrap resampling, and decision curve analysis (DCA). Model performance was compared with the most prevalent severity evaluation scores, including the PELOD-2, P-MODS, and pSOFA scores. The prediction model included nine variables: glutamic-pyruvic transaminase, albumin, globulin, myohemoglobin, creatine kinase, serum potassium, procalcitonin, serum ferritin, and interval between onset and diagnosis. The AUC of the model for predicting the 28-day mortality was 0.933 and 0.932 in the training and validation sets, respectively. The AUC values of the HScore, PELOD-2, P-MODS and pSOFA were 0.815, 0.745, 0.659 and 0.788, respectively. The DCA of the 28-day mortality prediction exhibited a greater net benefit than the HScore, PELOD-2, P-MODS and pSOFA. Subgroup analyses demonstrated good model performance across HLH subtypes. The novel mortality prediction model in this study can contribute to the rapid assessment of early mortality risk after diagnosis with readily available parameters. [ABSTRACT FROM AUTHOR]

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit. Article highlights The study aimed to develop a predictive model for lung metastases in patients with differentiated thyroid cancer (DTC) who are at intermediate- to high-risk. Through univariate and multivariate analyses, histological type, multifocality, tumor size, BRAFV600E mutation and ps-Tg levels were identified as independent factors influencing the development of lung metastases. A nomogram was constructed based on these risk factors, with a high C-index of 0.86, indicating strong predictive accuracy for lung metastases. Calibration curves showed excellent consistency between predicted and actual probabilities of lung metastases, further validating the nomogram's reliability. Receiver operating characteristic analysis demonstrated high discrimination ability of the nomogram, with an area under the curve of 0.865 in the training cohort and 0.845 in the validation cohort. Decision curve analysis indicated that the nomogram had better clinical utility in predicting lung metastases rates compared with other models. The nomogram provides clinicians with a tool to assess the risk of lung metastases in DTC patients, incorporating factors such as histological type, tumor size, genetic mutations and ps-Tg levels to guide treatment decisions and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The relationship between tumor and muscle layer in the vesical imaging‐reporting and data system (VI–RADS) 3 is ambiguous, and there is a lack of preoperative and non‐invasive procedures to detect muscle invasion in VI‐RADS 3. Purpose: To develop a nomogram based on MRI features for detecting muscle invasion in VI–RADS 3. Study Type: Retrospective. Population: 235 cases (Age: 67.5 ± 11.5 years) with 11.9% females were randomly divided into a training cohort (n = 164) and a validation cohort (n = 71). Field Strength/Sequence: 3T, T2‐weighted imaging (turbo spin‐echo), diffusion‐weighted imaging (breathing‐free spin echo), and dynamic contrast‐enhanced imaging (gradient echo). Assessment: 3 features were selected from the training cohort, including tumor contact length greater than maximum tumor diameter (TCL > Dmax), flat tumor morphology, and lower standard deviation of apparent diffusion coefficient (ADCSD). Three readers assessed VI‐RADS scores and the tumor morphology. Statistical Tests: Interobserver agreement was assessed by Kappa analysis. Features for final analysis were selected by logistic regression. The performance of the nomogram was evaluated by the receiver operating characteristic curve, decision curve analysis, and calibration curve. Results: TCL > Dmax, flat morphology, and lower ADCSD were the independent risk factors for muscle invasive in VI‐RADS 3. The AUCs, accuracy, sensitivity, and specificity of the nomogram 1 composed of three features for detecting muscle invasion were 0.852 (95% CI: 0.793–0.912), 0.756, 0.917, and 0.663 in the training cohort, and 0.885 (95% CI: 0.801–0.969), 0.817, 0.900, and 0.784 in the validation cohort. The nomogram 2 without ADCSD has nearly the same performance as the nomogram 1. Data Conclusion: Nomogram can be an efficient tool for preoperative detection of muscle invasion in VI–RADS 3. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression. Methods All eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance. Results 299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017–1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003–1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175–2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005–1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035–26.858; P

Abstract Background Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery. Methods A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting. Results The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities. Conclusions This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia.

Abstract Background Assessment of postoperative ambulation in osteonecrosis of the femoral head (ONFH) patients treated with total hip arthroplasty (THA) is limited. This study aimed to define the incidence and risk factors for losing walking independence (LWI) at one-year postoperatively in patients with ONFH undergoing primary THA, and to establish and validate a predictive nomogram. Methods This was a retrospective analysis of prospective collected data from patients admitted to a tertiary referral hospital with ONFH who underwent primary unilateral THA from October 2014 to March 2018. The Functional Independence Measure-Locomotion scale was used to quantify walking independence and was documented at a one-year continuous postoperative follow-up, which classified patients with a final score below 6 as LWI. Multivariate logistic regression identified independent risk factors for LWI, and a predictive nomogram was constructed based on the analysis results. The stability of the model was assessed using patients from April 2018 to April 2019 as an external validation set. Results 1152 patients were enrolled in the study, of which 810 were used in the training cohort and the other 342 for the validation cohort. The incidence of LWI was 5.93%. Multivariate analysis revealed that age 62 years or older (odd ratio (OR) = 2.37, 95% confidence interval (CI) 1.07–5.24), Charlson’s comorbidity index 3 or higher (OR = 3.64, 95% CI 1.09–12.14), Association Research Circulation Osseous stage IV (OR = 2.16, 95% CI 1.03–4.54), reduced femoral offset (OR = 2.41, 95% CI 1.16–5.03), and a higher controlling nutritional status score (OR = 1.14, 95% CI 1.01–1.30) were independent risk factors of LWI. The nomogram had a concordance index of 0.773 and a Brier score of 0.049 in the training set, with corrected values of 0.747 and 0.051 after internal validation. The receiver-operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis all performed well in both the training and validation cohorts. Conclusions This study reported a 5.93% incidence of LWI and established a risk prediction model in patients undergoing THA for ONFH, supporting targeted screening and intervention to assist surgeons in assessing ambulation capacity and managing rehabilitation.

Abstract Purpose Sarcopenia is a common complication of diabetes. Nevertheless, precise evaluation of sarcopenia risk among patients with diabetes is still a big challenge. The objective of this study was to develop a nomogram model which could serve as a practical tool to diagnose sarcopenia in patients with diabetes. Methods A total of 783 participants with diabetes from China Health and Retirement Longitudinal Study (CHARLS) 2015 were included in this study. After oversampling process, 1,000 samples were randomly divided into the training set and internal validation set. To mitigate the overfitting effect caused by oversampling, data of CHARLS 2011 were utilized as the external validation set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were employed to explore predictors. Subsequently, a nomogram was developed based on the 9 selected predictors. The model was assessed by area under receiver operating characteristic (ROC) curves (AUC) for discrimination, calibration curves for calibration, and decision curve analysis (DCA) for clinical efficacy. In addition, machine learning models were constructed to enhance the robustness of our findings and evaluate the importance of the predictors. Results 9 factors were selected as predictors of sarcopenia for patients with diabetes. The nomogram model exhibited good discrimination in training, internal validation and external validation sets, with AUC of 0.808, 0.811 and 0.794. machine learning models revealed that age and hemoglobin were the most significant predictors. Calibration curves and DCA illustrated excellent calibration and clinical applicability of this model. Conclusion This comprehensive nomogram presented high clinical predictability, which was a promising tool to evaluate the risk of sarcopenia in patients with diabetes.

Abstract Background Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation. Methods Eight hundred forty-six patients with indwelling urinary catheters who were resuscitated in a post-anesthesia care unit (PACU). Trained researchers used the CRBD 4-level assessment method to evaluate the severity of a patient’s CRBD. They then inputted 24 predictors into six different machine learning algorithms. The performance of the models was evaluated using metrics like the area under the curve (AUC). Results The AUCs of the six models ranged from 0.82 to 0.89. Among them, the RF model displayed the highest predictive ability, with an AUC of 0.89 (95%CI: 0.87, 0.91). Additionally, it achieved an accuracy of 0.93 (95%CI: 0.91, 0.95), 0.80 sensitivity, 0.98 specificity, 0.94 positive predictive value (PPV), 0.92 negative predictive value (NPV), 0.87 F1 score, and 0.07 Brier score. The logistic regression (LR) model has achieved good results (AUC:0.87) and converted into a nomogram. Conclusions The study has successfully developed a machine learning prediction model that exhibits excellent predictive capabilities in identifying patients who may develop moderate-to-severe CRBD after undergoing general anesthesia. Furthermore, the study also presents a nomogram, which serves as a valuable tool for clinical healthcare professionals, enabling them to intervene at an early stage for better patient outcomes.

Abstract Background Cervical lymph node metastasis (CLNM) significantly impacts the prognosis of papillary thyroid carcinoma (PTC) patients. Accurate CLNM prediction is crucial for surgical planning and patient outcomes. This study aimed to develop and validate a nomogram-based risk stratification system to predict CLNM in PTC patients. Methods This retrospective study included 1069 patients from Zhongshan Hospital and 253 from the Qingpu Branch of Zhongshan Hospital. Preoperative ultrasound (US) data and various nodule characteristics were documented. Patients underwent lobectomy with central lymph node dissection and lateral dissection if suspicious. Multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest algorithm were used to identify CLNM risk factors. A nomogram was constructed and validated internally and externally. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration plots, DeLong’s test, decision curve analysis (DCA), and the clinical impact curve (CIC). Results Six independent CLNM risk factors were identified: age, sex, tumor size, calcification, internal vascularity, and US-reported CLNM status. The model's area under the curve (AUC) was 0.77 for both the training and the external validation sets. Calibration plots and Hosmer‒Lemeshow (HL) tests showed good calibration. The optimal cutoff value was 0.57, with a sensitivity of 58.02% and a specificity of 83.43%. Risk stratification on the basis of the nomogram categorized patients into low-, intermediate-, and high-risk groups, effectively differentiating the likelihood of CLNM, and an online calculator was created for clinical use. Conclusion The nomogram accurately predicts CLNM risk in PTC patients, aiding personalized surgical decisions and improving patient management.

Objective To investigate the independent risk factors for postoperative deep vein thrombosis in lung cancer patients and to construct a risk prediction model. Methods Clinical data of 354 inpatients who underwent thoracoscopic surgery for lung cancer in Department of Thoracic Surgery of First Affiliated Hospital of Army Medical University between May 2019 and May 2023 were retrospectively collected and analyzed. LASSO regression was used to screen potential factors, followed by multivariate logistic regression to identify risk factors, and then a nomogram prediction model was constructed. Calibration curves, receiver operating characteristic (ROC) curves, and decision curves were drawn to evaluate the model's calibration, discrimination, sensitivity, specificity, and clinical utility. The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were employed to compare the predictive performance of the constructed model with the Caprini score for outcome events. Results LASSO regression identified 17 potential influencing factors. Multivariate regression analysis showed that D-dimer, central venous catheter (CVC) placement, and lower extremity varicose veins were independent risk factors for postoperative DVT in lung cancer patients (P < 0.05). Calibration curve analysis showed the model had good agreement between the predicted and observed values. ROC curve analysis indicated that the sensitivity and specificity of the model was 0.812 and 0.963, respectively, with an area under the curve (AUC) value of 0.912 (95%CI: 0.840~0.983). In comparison, the Caprini model had a sensitivity and specificity of 0.625 and 0.860, respectively, with an AUC value of 0.752 (95%CI: 0.657~0.846). The NRI and IDI for the model group compared to the Caprini model were 0.709 and 0.431, respectively. Decision curve analysis showed that the net benefit of applying the model from this study was higher than that of the Caprini model. Conclusion D-dimer, CVC, and varicose veins of lower extremities are independent risk factors for DVT after thoracoscopic surgery in patients with lung cancer. Our constructed nomogram model can effectively predict the risk of DVT after thoracoscopic surgery in patients with lung cancer.

Abstract Background Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis. Objective This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model. Methods This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model’s calibration. Results Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381–0.734, P

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases. Methods From January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance. Results The multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p 8.0 μmol/L) (p

Xue Yang,1,&ast; Man Wu,2,&ast; Tangzhiming Li,3,&ast; Jie Yu,4 Tian Fu,5 Guoping Li,6 Huanwen Xiong,7 Gang Liao,8 Sensen Zhang,9 Shaofeng Li,10 Zhonghua Zeng,11 Chun Chen,12 Benhui Liang,13,14 Zhiguo Zhou,15 Ming Lu16 1Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, People’s Republic of China; 3Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People’s Hospital (The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Jinan University), Shenzhen, Guangdong, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, People’s Republic of China; 5Department of Respiratory and Critical Care Medicine, Jining No 1. People’s Hospital, Jining, Shandong, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Tongde Hospital of Zhejiang Hangzhou, Zhejiang, People’s Republic of China; 7Department of Respiratory and Critical Care Medicine, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 8Department of Respiratory and Critical Care Medicine, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong, People’s Republic of China; 9Department of Respiratory Medicine, The Third Central Hospital of Tianjin, Tianjin, People’s Republic of China; 10Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China; 11Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Fuzhou, Jiangxi, People’s Republic of China; 12Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People’s Republic of China; 13Department of Cardiology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 14Department of Structural Heart Disease, National Center for Cardiovascular Disease, China & State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, National Clinical Research Center for Cardiovascular Diseases, Beijing, People’s Republic of China; 15Department of Pulmonary and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China; 16Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhiguo Zhou, Department of Pulmonary and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China, Email zhouzhiguo1217@163.com Ming Lu, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China, Email 602032934@qq.comIntroduction: C. psittaci pneumonia has atypical clinical manifestations and is often ignored by clinicians. This study analyzed the clinical characteristics, explored the risk factors for composite outcome and established a prediction model for early prediction of composite outcome among C. psittaci pneumonia patients.Methods: A multicenter, retrospective, observational cohort study was conducted in ten Chinese tertiary hospitals. Patients diagnosed with C. psittaci pneumonia were included, and their clinical data were collected and analyzed. The composite outcome of C. psittaci pneumonia included death during hospitalization, ICU admission, and mechanical ventilation. Univariate and multivariable logistic regression analyses were conducted to determine the significant variables. A ten-fold cross-validation was performed to internally validate the model. The model performance was evaluated using various methods, including receiver operating characteristics (ROC), C-index, sensitivity, specificity, positive/negative predictive value (PPV/NPV), decision curve analysis (DCA), and clinical impact curve analysis (CICA).Results: In total, 83 patients comprised training cohorts and 36 patients comprised validation cohorts. CURB-65 was used to establish predictive Model 1. Multivariate logistic regression analysis identified three independent prognostic factors, including serum albumin, CURB-65, and white blood cells. These factors were employed to construct model 2. Model 2 had acceptable discrimination (AUC of 0.898 and 0.825 for the training and validation sets, respectively) and robust internal validity. The specificity, sensitivity, NPV, and PPV for predicting composite outcome in the nomogram model were 91.7%, 84.5%, 50.0%, and 98.4% in the training sets, and 100.0%, 64.7%, 14.2%, and 100.0% in the validation sets. DCA and CICA showed that the nomogram model was clinically practical.Conclusion: This study constructs a refined nomogram model for predicting the composite outcome in C. psittaci pneumonia patients. This nomogram model enables early and accurate C. psittaci pneumonia patients’ evaluation, which may improve clinical outcomes.Keywords: Chlamydia psittaci pneumonia, nomogram, prediction model, composite outcome

Abstract This study aimed to investigate the current hope levels in patients with primary liver cancer by analyzing the risk indicators of hope levels, constructing and validating a novel hope score-based predictive model. A total of 206 patients with primary liver cancer admitted to the hepato-pancreato-biliary surgery department of a tertiary hospital from October 2020 to June 2021 were included. The Herth Hope Index was utilized to assess hope levels, and based on the questionnaire results, the patients were categorized into low-hope (≤ 30 points) and high-hope (> 30 points) groups. Single-factor analysis and logistic multivariate regression analysis were conducted to explore the factors influencing hope levels in patients with primary liver cancer. A nomogram was plotted, and a risk prediction model for hope levels in these patients was developed. The predictive performance of the nomogram model was evaluated using calibration plots, the Hosmer–Lemeshow test, and other relevant assessments. Total of 206 patients participated in the questionnaire survey, with 82 patients (39.81%) categorized as belonging to the low-hope group. The results of the single-factor analysis showed statistically significant differences (all P 0.05). The observed and expected values generated by the Hosmer–Lemeshow test were plotted as a scatter plot with a fitted linear trend, showing good consistency between the predictive model and actual risk. The constructed predictive model developed in this study exhibited good predictive capability for assessing the hope levels of patients with primary liver cancer. This model can assist clinical staff in rapidly identifying the psychological risk of low hope levels in patients, thereby providing valuable insights for the timely implementation of proactive management measures.

Abstract Background Depression represents a frequent mental health challenge in individuals with fractures, negatively impacting their recuperation and overall well-being. The purpose of this research was to formulate and corroborate a prognostic framework for pinpointing depression risk among fracture sufferers by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020 and a separate hospital-based group. Methods We analyzed records from 1,748 individuals with fractures documented in the NHANES database spanning 2005 to 2020, of which 362 were diagnosed with depression, as indicated by a Patient Health Questionnaire-9 (PHQ-9) score of 10 or higher. An additional validation group comprised 360 fracture patients sourced from a medical center. Considered variables for prediction encompassed demographic details, lifestyle habits, past medical conditions, and laboratory results. The method of least absolute shrinkage and selection operator (LASSO) regression facilitated the narrowing down of variables, while multivariate logistic regression was employed to pinpoint significant predictors. To assist in prediction, a nomogram was designed and subsequently validated. Results Five independent predictors were identified: drinking, insomnia, poverty-to-income ratio, education level, and white blood cell count. The nomogram showed good discrimination in the NHANES cohorts (training area under the curve (AUC) 0.734, validation AUC 0.740) and hospital-based external validation (AUC 0.711). Calibration curves and decision analysis supported its predictive accuracy and clinical value. Conclusion The constructed nomogram offers a precise and clinically relevant instrument for forecasting depression risk in patients with fractures, facilitating the early detection of individuals at high risk and enabling prompt intervention.

Abstract This study aimed to identify factors associated with optimal weight loss response by analyzing pre-weight loss data from a cohort of 2577 patients with obesity who visited weight management clinics between 2013 and 2022. Out of these, 1276 patients had follow-up data available. Following dietary and exercise interventions, 580 participants achieved optimal weight loss outcomes. Participants were subsequently divided into two groups based on their weight loss outcomes: those who achieved optimal weight loss response and those who did not. Statistical analysis, conducted using RStudio, identified thirteen predictor variables through LASSO and logistic regression, with age emerging as the most influential predictor. A nomogram was developed to predict optimal weight loss response, showing good predictive performance (AUC = 0.807) and clinical applicability, validated by internal validation methods. Decision curve analysis (DCA) further illustrated the nomogram's clinical utility. The developed nomogram prediction model for optimal weight loss response is user-friendly, highly accurate, and demonstrates excellent discriminative and calibration capabilities.

Zhanjie Li,1,&ast; Jian Li,2,&ast; Chuanlong Zhu,3 Shengyuan Jiao1,2 1Department of Infection Control, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People’s Republic of China; 2Department of Disease Prevention and Control, Air Force Hospital of Eastern Theater, Nanjing, People’s Republic of China; 3Department of Infections Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shengyuan Jiao, Department of Infection Control, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People’s Republic of China, Tel +8618021136781, Email 17319884906@163.com Chuanlong Zhu, Department of Infections Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210009, People’s Republic of China, Tel +8617714316539, Email zhuchuanlong@jsph.org.cnBackground: To identify risk factors for acquiring HAIs in COVID-19 patients and establish visual prediction model.Methods: Data was extracted from Xinglin Hospital Infection Monitoring System to analyze COVID-19 patients diagnosed between December 1, 2022, and March 1, 2023. Univariate and multivariate analyses were conducted to identify risk factors. Predictive signature was developed by selected variables from lasso, logistic regression, and their intersection and union. Models were compared using DeLong’s t-tests. Likelihood ratio (LR) and Youden’s index was used to evaluate the predictive performance. Nomogram was constructed using optimal variables ensemble, prediction accuracy was evaluated using AUC, DCA and calibration curve.Results: Total of 739 patients met the criteria, of which 53 (7.2%) were HAIs. NSAIDs, surgery, fungi and MDRO detected, hormone drugs and LYMR were independent risk factors. Lasso model screened seven variables, and logistic model identified six risk factors. Union model performed the best with the maximum of the Youden’s index is 0.703, the sensitivity is 95.6%, the specificity is 74.7%, the LR is 3.778. The best AUC of union model is 0.953 (0.928– 0.978), and the accuracy is 87.5%. DCA indicated that the union model provided the best net benefits and calibration curve demonstrated good predictive agreement.Conclusions: HAIs prediction in COVID-19 patients is feasible and beneficial to improve prognosis. Physicians can use this nomogram to identify high-risk COVID-19 populations for HAIs and tailor follow-up strategies.Keywords: risk factors, healthcare-associated infection, COVID-19, nomogram, prediction model

BACKGROUND: Prevention of fractures after stroke is very important, and there are currently no models to predict the occurrence of hip fractures after stroke. OBJECTIVE: To investigate the risk factors leading to hip fracture in stroke patients and to establish a risk prediction model to visualize this risk. METHODS: A total of 439 stroke patients were selected from the Affiliated Hospital of Xuzhou Medical University from June 2014 to June 2017, including 107 males and 332 females, with a mean age of (71.38±9.74) years. They were divided into fracture group (n=35) and non-fracture group (n=404) according to the presence or absence of hip fracture. Univariate and multivariate analyses were used to determine the risk factors for hip fracture after stroke. The data were randomly divided into training set (70%) and test set (30%). Nomogram predicting the risk of hip fracture occurrence was created based on the results of the multifactor analysis, and performance was evaluated using receiver operating characteristic curve, calibration curves, and decision curve analysis. A web calculator was created to facilitate a more convenient interactive experience for clinicians. RESULTS AND CONCLUSION: (1) Univariate analysis showed significant differences between the two groups in the number of falls, smoking, hypertension, glucocorticoids, number of strokes, Mini-Mental State Examination, visual acuity level, National Institute of Health Stroke Scale, Berg Balance Scale, and Stop Walking When Talking scale scores (P < 0.05). (2) Multivariate analysis showed that number of falls [OR=17.104, 95%CI (3.727-78.489), P=0.000], National Institute of Health Stroke Scale [OR=1.565, 95%CI(1.193-2.052), P=0.001], Stop Walking When Talking [OR=12.080, 95%CI(2.398-60.851), P=0.003] were independent risk factors positively associated with new hip fractures. Bone mineral density [OR=0.155, 95%CI(0.044-0.546), P=0.012] and Berg Balance Scale [OR=0.840, 95%CI(0.739-0.954), P=0.007] were negatively associated with new hip fractures after stroke. (3) The AUC values of nomogram were 0.956 and 0.907 in the training and test sets, respectively, and the calibration curves showed a high agreement between predicted and actual status with an area under the decision curve of 0.038 and 0.030, respectively. (4) These findings conclude that the number of falls, low bone mineral density, low Berg Balance Scale score, high National Institute of Health Stroke Scale score, and positive Stop Walking When Talking are risk factors for hip fracture after stroke. Based on this, a nomogram with high accuracy was developed and a web calculator (https://stroke.shinyapps.io/DynNomapp/) was created. [ABSTRACT FROM AUTHOR]

BACKGROUND: Establishing a nomogram prediction model for postoperative pulmonary infection in hip fractures and taking early intervention measures is crucial for improving patients’ quality of life and reducing medical costs. OBJECTIVE: To construct a nomogram risk prediction model of postoperative pulmonary infection in elderly patients with hip fracture, and provide theoretical basis for feasible prevention and early intervention. METHODS: Case data of 305 elderly patients with hip fractures who underwent surgical treatment at Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine between January and October 2020 (training set) were retrospectively analyzed. Using univariate and multivariate logistic regression analysis and Hosmer-Lemeshow goodness of fit test, receiver operating characteristic curve was utilized to analyze the diagnostic predictive efficacy of independent risk factors and joint models for postoperative pulmonary infections. Tools glmnet, pROC, and rms in R Studio software were applied to construct a nomogram model for predicting the risk of postoperative pulmonary infection in elderly patients with hip fractures, and calibration curves were further drawn to verify the predictive ability of the nomogram model. Receiver operating characteristic curves, calibration curves, and decision curves were analyzed for 133 elderly patients with hip fractures (validation set) receiving surgery at the same hospital from November 2022 to March 2023 to further predict the predictive ability of the nomogram model. RESULTS AND CONCLUSION: (1) The postoperative pulmonary infection rate in elderly patients with hip fractures in this group was 9.18% (28/305). (2) Single factor and multivariate analysis, as well as forest plots, showed that preoperative hospitalization days, leukocyte count, hypersensitive C-reactive protein, and serum sodium levels were independent risk factors (P < 0.05). The Hosmer-Leme show goodness of fit test showed good fit (χ² =4.57, P=0.803). Receiver operating characteristic curve analysis was conducted on the independent risk factors and their joint models mentioned above, and the differentiation of each independent risk factor and joint model was good, with statistical significance (P < 0.05). (3) The graphical calibration method, C-index, and decision curve were used to validate the nomogram prediction model. The predicted calibration curve was located between the standard curve and the acceptable line, and the predicted risk of the nomogram model was consistent with the actual risk. (4) The validation set used receiver operating characteristic curve, graphic calibration method, and decision curve to validate the prediction model. The results showed good consistency with clinical practice, indicating that the model had a good fit. The nomogram risk prediction model constructed for postoperative pulmonary infection in elderly patients with hip fractures has good predictive performance. The use of the nomogram risk prediction model can screen high-risk populations and provide a theoretical basis for early intervention. [ABSTRACT FROM AUTHOR]

Objective: To predict the incidence of postoperative ileus in bladder cancer patients after radical cystectomy. Methods: We retrospectively analyzed the perioperative data of 452 bladder cancer patients who underwent radical cystectomy with urinary diversion at the Second Hospital of Tianjin Medical University between 2016 and 2021. Univariate and multivariate logistic regression were used to identify the risk factors for postoperative ileus. Finally, a nomogram model was established and verified based on the independent risk factors. Results: Our study revealed that 96 patients (21.2%) developed postoperative ileus. Using multivariate logistic regression analysis, we found that the independent risk factors for postoperative ileus after radical cystectomy included age > 65.0 years, high or low body mass index, constipation, hypoalbuminemia, and operative time. We established a nomogram prediction model based on these independent risk factors. Validation by calibration curves, concordance index, and decision curve analysis showed a strong correlation between predicted and actual probabilities of occurrence. Conclusion: Our nomogram prediction model provides surgeons with a simple tool to predict the incidence of postoperative ileus in bladder cancer patients undergoing radical cystectomy. [ABSTRACT FROM AUTHOR]

Background: Lean individuals with non-alcohol fatty liver disease (NAFLD) often have normal body size but abnormal visceral fat. Therefore, an alternative to body mass index should be considered for prediction of lean-NAFLD. This study aimed to use representative visceral fat links with other laboratory parameters using the least absolute shrinkage and selection operator (LASSO) method to construct a predictive model for lean-NAFLD. Methods: This retrospective cross-sectional analysis enrolled 2325 subjects with BMI < 24 kg/m² from medical records of 51,271 examinees who underwent a routine health check-up. They were randomly divided into training and validation cohorts at a ratio of 1:1. The LASSO-derived prediction model used LASSO regression to select 23 clinical and laboratory factors. The discrimination and calibration abilities were evaluated using the Hosmer-Lemeshow test and calibration curves. The performance of the LASSO model was compared with the fatty liver index (FLI) model. Results: The LASSO-derived model included four variables--visceral fat, triglyceride levels, HDL-C-C levels, and waist hip ratio--and demonstrated superior performance in predicting lean-NAFLD with high discriminatory ability (AUC, 0.8416; 95% CI: 0.811-0.872) that was comparable with the FLI model. Using a cut-off of 0.1484, moderate sensitivity (75.69%) and specificity (79.86%), as well as high negative predictive value (95.9%), were achieved in the LASSO model. In addition, with normal WC subgroup analysis, the LASSO model exhibits a trend of higher accuracy compared to FLI (cut-off 15.45). Conclusions: We developed a LASSO-derived predictive model with the potential for use as an alternative tool for predicting lean-NAFLD in clinical settings. [ABSTRACT FROM AUTHOR]

BACKGROUND: Postoperative delirium is one of the serious complications after total knee arthroplasty, usually occurring 1-5 days after surgery, with confusion and cognitive impairment as the main manifestations, which is not conducive to the recovery of joint function in elderly patients. At present, the risk factors affecting delirium after total knee arthroplasty in the elderly are not clear, and there is a lack of clinical prediction studies to directly present them for promotion and application. OBJECTIVE: To explore the risk factors of delirium after total knee arthroplasty in elderly patients and establish a prediction model of nomogram. METHODS: Medical record data of 116 elderly patients receiving total knee arthroplasty treated in Ganzhou Hospital of Traditional Chinese Medicine, Jiangxi University of Chinese Medicine from January 2019 to December 2021 were retrospectively analyzed, of which 29 elderly patients with delirium after total knee arthroplasty were selected as the observation group, and the remaining 87 elderly patients without delirium after total knee arthroplasty were selected as the control group. Preoperative general clinical data, laboratory examination results, and surgical data were compared between the two groups. Multivariate Logistic regression analysis was used to analyze risk factors for delirium after total knee arthroplasty in elderly patients. The receiver operating characteristic curve was used to analyze the independent risk factors and obtain the best cut-off value. The nomogram model was constructed by R software. RESULTS AND CONCLUSION: (1) There were significant differences in age, cerebrovascular accident history, preoperative hospital stay, preoperative albumin, hemoglobin, American Society of Anesthesiologists classification, operation time, anesthesia time, and intraoperative blood transfusion volume between the two groups (P < 0.05). (2) Multivariate Logistic regression analysis showed that old age, long hospital stay before surgery, high American Society of Anesthesiologists classification grade, and long operation time were risk factors for postoperative delirium in elderly knee arthroplasty patients, while high albumin and high hemoglobin were protective factors for postoperative delirium in elderly knee arthroplasty patients. (3) The areas under the curve of age, preoperative hospital stay, albumin, hemoglobin, American Society of Anesthesiologists classification grade, and operation time were 0.784, 0.706, 0.853, 0.762, 0.617, and 0.542, respectively. The optimal cut-off values were 75 years, 7 days, 40 g/L, 125 g/L, 3 and 200 minutes, respectively. (4) After internal data for verification, the consistency index was 0.974. The actual curve of the model was in good agreement with the ideal curve. (5) These results indicate that this nomogram model based on old age, long hospital stay, high American Society of Anesthesiologists classification grade, low albumin, low hemoglobin, and long operation time has far-reaching clinical significance for early identification, early warning and diagnosis of delirium risk in elderly patients after total knee arthroplasty. [ABSTRACT FROM AUTHOR]

This study aimed to develop and validate a predictive nomogram model applicable to depression risk in stroke patients. Participants from the NHANES database (n = 1097) were enrolled from 2005 to 2018; 767 subjects were randomly assigned to the training cohort, and the remaining subjects composed the testing cohort. A nomogram containing the optimal predictors identified by the least absolute shrinkage and selection operator (LASSO) and logistic regression methods was constructed to estimate the probability of depression in stroke patients. To evaluate the performance of the nomogram, the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis (DCA) and internal validation were utilized. Age, family income, trouble sleeping, coronary heart disease, and total cholesterol were included in the nomogram after filtering predictive variables. The AUCs of the nomogram for the training and testing cohorts were 0.782 (95 % CI = 0.742–0.821) and 0.755 (95 % CI = 0.675–0.834), respectively. The calibration plot revealed that the predicted probability was extremely close to the actual probability of depression occurrence in both the training and testing cohorts. DCA revealed that the nomogram model in the training and testing cohorts had a net benefit when the risk thresholds were 0–0.59 and 0–0.375, respectively. This study was limited by the absence of clinical external validation, which hindered the estimation of the nomogram's external applicability. In addition, this study has a cross-sectional design. A novel nomogram was successfully constructed and proven to be beneficial for identifying individuals at high risk for depression among stroke patients. • Age, family income, trouble sleeping, CHD, and TC are key risk factors for developing depression in stroke patients. • The nomogram, based on above five factors, accurately and effectively predicts depression risk in patients with stroke. • The nomogram holds the potential to discern high-risk individuals for depression among stroke patients and enhance prognosis. [ABSTRACT FROM AUTHOR]

Abstract Ventricular septal rupture (VSR) is a mechanical complication of acute myocardial infarction (AMI), and its mortality has not decreased significantly in recent decades. However, no clinical model has been developed to predict short-term mortality in patients with post-infarction VSR (PIVSR). This study aimed to develop a nomogram to predict the 30-day mortality by using the clinical characteristics of hospitalized patients with PIVSR. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis was used to construct a nomogram by R. The model was evaluated by the area under the curve (AUC), calibration curve and decision curve analysis (DCA). The bootstrap method was used to validate the model internally. As a result, a nomogram was constructed by using six variables, including CRRT, mechanical ventilation, PPCI, WBC, PASP and methods of treatment. The AUC of the prediction model was 0.96 (0.93, 0.98). The prediction model was well calibrated. The DCA showed that if the threshold probability was between 15% and 95%, the nomogram model would provide a net benefit. The well-constructed and evaluated nomogram can be beneficial to clinicians to predict the risk of death within 30 days in patients with PIVSR.

Abstract Background The identification and assessment of environmental risks are crucial for the primary prevention of congenital heart disease (CHD). We were aimed to establish a nomogram model for CHD in the offspring of pregnant women and validate it using a large CHD database in Northwest China. Methods A survey was conducted among 29,204 women with infants born between 2010 and 2013 in Shaanxi province, Northwest China. Participants were randomly assigned to the training set and to the validation set at a ratio of 7:3. The importance of predictive variables was assessed using random forest. A multivariate logistic regression model was used to construct the nomogram for the prediction of CHD. Results Multivariate analyses revealed that the gravidity, preterm birth history, family history of birth defects, infection, taking medicine, tobacco exposure, pesticide exposure and singleton/twin pregnancy were significant predictive risk factors for CHD in the offspring of pregnant women. The area under the receiver operating characteristic curve for the prediction model was 0.716 (95% CI: 0.671, 0.760) in the training set and 0.714 (95% CI: 0.630, 0.798) in the validation set, indicating moderate discrimination. The prediction model exhibited good calibration (Hosmer-Lemeshow χ2 = 1.529, P = 0.910). Conclusions We developed and validated a predictive nomogram for CHD in offspring of Chinese pregnant women, facilitating the early prenatal assessment of the risk of CHD and aiding in health education.

Abstract Diabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820–0.849) and 0.851 (95%CI 0.829–0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.

Longyin Zhu,&ast; Juan Cai,&ast; Jia Fang, Lingyu Ran, Huan Chang, Huhai Zhang, Jiamin Zeng, Qin Yang, Chunxiao Fu, Qingping Li, Qianguang Pan, Hongwen Zhao Department of Nephrology, the First Hospital Affiliated to Army Military Medical University (Southwest Hospital), Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hongwen Zhao; Qianguang Pan, Email zhaohw212@126.com; panqianguang@163.comObjective: Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics.Methods: In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI. Subsequently, the CSA-AKI nomogram model was developed using R software. Predictive performance was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation was performed through the Bootstrap method with 1000 repeated samples. Additionally, decision curve analysis (DCA) assessed the clinical applicability of the model.Results: Multivariable logistic regression analysis revealed that being male, age ≥ 50 years, operation time ≥ 290 minutes, postoperative plasma suPAR at 2 hours, and preoperative left ventricular ejection fraction (LVEF) were independent risk factors for CSA-AKI. Employing these variables as predictive factors, a nomogram model was constructed, an ROC curve was generated, and the AUC was computed as 0.817 (95% CI 0.726– 0.907). The calibration curve indicated the accuracy of the model, and the results of DCA demonstrated that the model could benefit the majority of patients.Conclusion: Being male, age ≥ 50 years, operation time ≥ 290 minutes, low preoperative LVEF, and elevated plasma suPAR at 2 hours are independent risk factors for CSA-AKI. The nomogram model established based on these risk factors has high accuracy and clinical value, serving as a predictive tool for assessing the risk of CSA-AKI.Keywords: nomogram, acute kidney injury, prediction model, risk factors, cardiac surgery

Abstract Purpose Few studies have focused on the risk factors leading to postoperative blood transfusion after open reduction and internal fixation (ORIF) of proximal humeral fractures (PHFs) in the elderly. Therefore, we designed this study to explore potential risk factors of blood transfusion after ORIF for PHFs. We have also established a nomogram model to integrate and quantify our research results and give feedback. Methods In this study, we retrospectively analyzed the clinical data of elderly PHF patients undergoing ORIF from January 2020 to December 2021. We have established a multivariate regression model and nomograph. The prediction performance and consistency of the model were evaluated by the consistency coefficient and calibration curve, respectively. Results 162 patients met our inclusion criteria and were included in the final study. The following factors are related to the increased risk of transfusion after ORIF: time to surgery, fibrinogen levels, intraoperative blood loss, and surgical duration. Conclusions Our patient-specific transfusion risk calculator uses a robust multivariable model to predict transfusion risk.The resulting nomogram can be used as a screening tool to identify patients with high transfusion risk and provide necessary interventions for these patients (such as preoperative red blood cell mobilization, intraoperative autologous blood transfusion, etc.).

Abstract Purpose There is a lack of adequate models specifically designed for elderly patients with severe acute pancreatitis (SAP) to predict the risk of death. This study aimed to develop a nomogram for predicting the overall survival of SAP in elderly patients. Methods Elderly patients diagnosed with SAP between January 1, 2017 and December 31, 2022 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed using multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results A total of 326 patients were included in the analysis, with 260 in the survival group and 66 in the deceased group. Multivariate logistic regression indicated that age, respiratory rate, arterial pH, total bilirubin, and calcium were independent prognostic factors for the survival of SAP patients. The nomogram demonstrated a performance comparable to sequential organ failure assessment (P = 0.065). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram. Conclusion We have identified key demographic and laboratory parameters that are associated with the survival of elderly patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.

Abstract This study aimed to develop and internally validate a nomogram model for assessing the risk of intraoperative hypothermia in patients undergoing video-assisted thoracoscopic (VATS) lobectomy. This study is a retrospective study. A total of 530 patients who undergoing VATS lobectomy from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into hypothermia group (n = 346) and non-hypothermia group (n = 184) according to whether hypothermia occurred during the operation. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of hypothermia during operation, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Calibration curve and Hosmer Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Intraoperative hypothermia occurred in 346 of 530 patients undergoing VATS lobectomy (65.28%). Logistic regression analysis showed that age, serum total bilirubin, inhaled desflurane, anesthesia duration, intraoperative infusion volume, intraoperative blood loss and body mass index were risk factors for intraoperative hypothermia in patients undergoing VATS lobectomy (P