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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]

Complicated urinary tract infection (cUTI) has higher incidences of antibiotic resistance, recurrence, chronicity, and progression. There exist differences in the immune functions, anatomical structures and physiological functions of urinary tract system between children and adults. In addition, the immature stage of hygiene habit development and the inability to express discomfort accurately all contribute to cUTI recurrence in pediatric patients. cUTI recurrence in children can lead to serious consequences such as growth and development delay, renal scars, and progression to end-stage kidney diseases. Despite the high incidence of cUTI recurrence, no predictive model currently exists to guide targeted intervention in pediatric patients. This study aimed to establish a nomogram to predict the risk of cUTI recurrence in children, thereby facilitating targeted prevention and treatment strategies in pediatric patients. The nomogram was developed based on a retrospective cohort that included 421 pediatric patients with cUTI at West China Second University Hospital from January 2020 to August 2023. The patients were randomly divided into a training set and a validation set in a 3:1 ratio. Logistic regression analysis was used to identify risk factors and construct the nomogram for predicting the risk of cUTI recurrence, followed by validation and performance analysis. Of the 421 children with cUTI, the recurrence rate of cUTI was 68.4% (288 cases) during an average follow-up duration of 22.9 months. The nomogram comprised female gender, history of urinary tract surgery, Escherichia coli in urine culture, renal dysfunction, and vesicoureteral reflux as predictors of cUTI recurrence in pediatric patients. The model showed favorable performance with C-index values of 0.735 and 0.750 in the training dataset and the validation dataset, respectively. The decision curve analysis revealed that the nomogram might be clinically useful. The reliable nomogram would be beneficial for clinicians to identify children with high risks of cUTI recurrence for targeted intervention. [ABSTRACT FROM AUTHOR]

Background Gastroesophageal reflux disease (GERD) is among the most common complications of bariatric surgery. This study aimed to analyse the risk factors affecting the worsening of GERD symptoms after laparoscopic sleeve gastrectomy (LSG), and to establish and validate a related nomogram model. Methods The study recruited 236 participants and randomly divided them into training and validation sets in a ratio of 7:3. LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to construct the column line graphs. The performance of the nomogram was evaluated and validated by analyzing the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results In this study, Lasso-logistic regression was applied to select 5 predictors from the relevant variables, which were body mass index (BMI), diabetes, hiatal hernia, GERD, and triglyceride levels. These 5 predictor variables constructed a model with moderate predictive power, with an area under the ROC curve of 0.779 for the training set and 0.796 for the validation set. Decision curve analysis showed that in external validation, if the risk thresholds were between 4 and 98% and 14–95%, then the nomogram can be applied to the clinic. Conclusions We have developed and validated a nomogram that effectively predicts the risk of worsening gastroesophageal reflux symptoms following LSG. [ABSTRACT FROM AUTHOR]

Objective: To investigate contralateral central lymph node metastasis (CCLNM) in patients with unilateral papillary thyroid carcinoma (UPTC). To provide a reference for clinical decision-making, a prediction model for the probability of CCLNM was established. Method: The clinicopathological data of 221 UPTC patients who underwent surgical treatment were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CCLNM according to clinicopathological characteristics, construct a prediction model to construct a visual nomogram, and evaluate the model. Results: According to univariate and multivariate logistic regression analyses, sex (P = 0.01, OR: 3.790, 95% CI: 1.373–10.465), extrathyroidal tumor extension (ETE) (P = 0.040, OR: 6.364, 95% CI: 1.083–37.381), tumor diameter (P = 0.010, OR: 3.674, 95% CI: 1.372–9.839) and ipsilateral central lymph node metastasis (ICLNM) (P < 0.001, OR: 38.552, 95% CI: 2.675–27.342) were found to be independent risk factors for CCLNM and were used to construct a nomogram for internal verification. The ROC curve had an AUC of 0.852 in the training group and an AUC of 0.848 in the verification group, and the calibration curve indicated that the prediction probability of the model was consistent with the actual probability. Finally, the analysis of the decision curve showed that the model has good application value in clinical decision-making. Conclusion: Sex, ETE, tumor size, and ICLNM emerged as independent risk factors for CCLNM in UPTC patients. A predictive model was therefore developed, harnessing these variables to enable an objective, personalized estimation of CCLNM risk. This tool offers valuable insights to inform surgical planning and optimize treatment strategies for UPTC management. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) is a common complication of mid–to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers. Methods: Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves. Results: Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband– wife relationship, family history, and parental relationships as predictors. The Hosmer–Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ2 = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89–0.97), 0.72 (95% CI: 0.62–0.81), and 0.68 (95% CI: 0.53–0.83), respectively. Conclusion: In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information. [ABSTRACT FROM AUTHOR]

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging global infectious disease with a high mortality rate. Clinicians lack a convenient tool for early identification of critically ill SFTS patients. The aim of this study was to construct a simple and accurate nomogarm to predict the prognosis of SFTS patients. Methods: We retrospectively analyzed the clinical data of 372 SFTS patients collected between May 2015 and June 2023, which were divided 7:3 into a training set and an internal validation set. We used LASSO regression to select predictor variables and multivariable logistic regression to identify independent predictor variables. Prognostic nomograms for SFTS were constructed based on these factors and analysed for concordance index, calibration curves and area under the curve (AUC) to determine the predictive accuracy and consistency of the model. Results: In the training set, LASSO and multivariate logistic regression analyses showed that age, SFTSV RNA, maximum body temperature, pancreatitis, gastrointestinal bleeding, pulmonary fungal infection (PFI), BUN, and PT were independent risk factors for death in SFTS patients. There was a strong correlation between neurological symptoms and mortality (P < 0.001, OR = 108.92). Excluding neurological symptoms, nomograms constructed based on the other eight variables had AUCs of 0.937 and 0.943 for the training and validation sets, respectively. Furthermore, we found that age, gastrointestinal bleeding, PFI, bacteraemia, SFTSV RNA, platelets, and PT were the independent risk factors for neurological symptoms, with SFTSV RNA having the highest diagnostic value (AUC = 0.785). Conclusions: The nomogram constructed on the basis of eight common clinical variables can easily and accurately predict the prognosis of SFTS patients. Moreover, the diagnostic value of neurological symptoms far exceeded that of other predictors, and SFTSV RNA was the strongest independent risk factor for neurological symptoms, but these need to be further verified by external data. [ABSTRACT FROM AUTHOR]

Background: Carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are the predominant serum tumour markers (STMs) for predicting the prognosis of colorectal cancer (CRC). The objective of this research is to develop clinical prediction models based on preoperative and postoperative CEA and CA19-9 levels. Methods: 1,452 consecutive participants with stage I-III colorectal cancer were included. Kaplan-Meier method, log-rank test, and multivariate COX regression were used to evaluate the significance of preoperative and postoperative STMs. Patients were grouped into a discovery cohort (70%) and a validation cohort (30%). Variables for the nomograms were selected according to the Akaike information criterion (AIC). Subsequently, two clinical predictive models were constructed, evaluated, validated, and then compared with the AJCC 8th TNM stage. Results: The overall survival (OS) rate and disease-free survival(DFS) rate declined progressively as the number of positive tumour markers(NPTMs) before and after surgery increased. For both OS and DFS, age, sex, pN stage, and NPTMs before and after surgery were independent prognostic factors, and then clinical prediction models were developed. The Concordance index (C-index), Receiver operating characteristic (ROC) curve, calibration curve, Decision curve analysis (DCA), and risk score stratification all indicated that the models possessed robust predictive efficacy and clinical applicability. The Net reclassification index (NRI) and Integrated discrimination improvement (IDI) indicated that the performance of models was significantly superior to the TNM stage. Conclusion: Nomograms based on pre-and postoperative CEA and CA19-9 can accurately predict survival and recurrence for stage I-III CRC patients after radical surgery, and were significantly better than the AJCC 8th TNM stage. [ABSTRACT FROM AUTHOR]

Background: In recent years, the incidence of pelvic fractures has been on the rise, predominantly affecting the elderly population. Deep vein thrombosis may lead to poor prognosis in patients. monocyte-to-lymphocyte ratio is novel biomarkers of inflammation, and this study aims to verify their predictive effect and construct the nomogram model. Method: This study used binary logistic regression analysis to predict the predictive effect of MLR on the occurrence of DVT in pelvic fractures patients. And use R studio to construct nomogram model. Result: The results showed that Age (1.04 [1.01, 1.07], p = 0.006), WBC (1.44 [1.28, 1.61], p < 0.001), and MLR (2.11 [1.08, 4.13], p = 0.029) were independent predictive factors. The nomogram demonstrated good predictive performance with small errors in both the training and validation groups, and most clinical patients could benefit from them. Conclusion: The nomogram constructed based on MLR can assist clinicians in early assessment of the probability of DVT occurrence. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images. Methods: This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility. Results: The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram. Conclusion: A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions. [ABSTRACT FROM AUTHOR]

Background: This study aimed to compare the diagnostic accuracy of four indicators, including waist-to-height ratio (WHTR), vascular adiposity index (VAI), TG/HDL-C, and BMI/HDL-C for metabolic syndrome (MS) in Chinese adults aged 40 years and above. Additionally, the study aimed to develop an efficient diagnostic model displayed by a nomogram based on individual's BMI and circulating HDL-C level. Methods: A cross-sectional study was conducted on 699 participants aged 40 years and above. Quartiles of BMI/HDL-C, TG/HDL-C, VAI, and WHTR were used as independent variables, and metabolic syndrome was used as the dependent variable. Logistic regression was conducted to explore the impact of each parameter on the risk of MS. The areas under the receiver operating characteristics were compared to determine the accuracy of the indicators in diagnosing MS in the participants. Logistic regression was run to construct the nomograms, and the performance of the nomogram was assessed by a calibration curve. Results: MS subjects had higher levels of BMI, BFM, PBF, VFA, AMC, WC, SCR, TG, and insulin, but lower LDH and HDL-C levels than the subjects without MS. The BMI/HDL-C ratio was positively correlated with the prevalence of MS and its components. The final diagnostic model included five variables: gender, BFM, WC, TG, and BMI/HDL-C. The model showed good calibration and discrimination power with an AUC of 0.780. The cut-off value for the nomogram was 0.623 for diagnosing MS. Conclusions: BMI/HDL-C ratio was an independent risk factor for MS in Chinese adults. BMI/HDL-C was significantly correlated with MS and its components. BMI/HDL-C was the most powerful diagnostic indicator compared to other indicators, including TG/HDL-C, VAI and WHTR for diagnosing MS. The nomogram drawn based on the diagnostic model provided a practical tool for diagnosing MS in Chinese adults. [ABSTRACT FROM AUTHOR]

Background: Predicting long-term survival in intensive care unit patients with intracerebral hemorrhage (ICH) is crucial. This study aimed to develop a platelet-to-white blood cell ratio (PWR) incorporated nomogram for long-term survival prediction. Methods: A retrospective analysis was conducted on 1,728 ICH patients in the MIMIC-IV 2.2 database. The independent prognostic value of PWR for 1- year mortality was assessed. A nomogram was developed using LASSO and Cox regression to predict 1-year survival, incorporating PWR and other factors. The performance of the nomogram was evaluated through calibration curves, area under the curve, Delong test, net reclassification index, integrated discrimination improvement, and decision curve analysis. Results: The nomogram, which included age, weight, Glasgow Coma Scale (GCS) score, mechanical ventilation, glucose, red blood cell (RBC) count, blood urea nitrogen (BUN), and PWR, showed good predictive performance for 1-year survival. The C-index was 0.736 (95% CI = 0.716--0.756) for the training set and 0.766 (95% CI = 0.735--0.797) for the testing set. Higher age and ventilation increased mortality risk, while higher weight, GCS score, RBC count, and PWR decreased risk. The nomogram outperformed conventional scores. Conclusions: A nomogramincorporating PWR as a prognostic factor accurately predicts long-term survival in ICH patients. However, validation in large-scale multicenter studies and further exploration of biomarkers are needed. [ABSTRACT FROM AUTHOR]

Background: Acute postoperative urinary retention (POUR) is a common complication in patients with ischemic stroke following femoral artery puncture (FAP), leading to discomfort, delayed hospital discharge, and increased patient morbidity. The relevant risk factors are unclear; thus, a predictive tool is required to guide treatment decisions. Objective: To develop and validate a nomogram to predict acute POUR in patients with ischemic stroke following FAP. Methods: We retrospectively collected cases from 1729 patients with ischemic stroke from the electronic record system of Jiangmen Central Hospital from January 2021 to December 2023. A total of 731 patients were randomly divided into development (n = 511, 70%) and validation (n = 220, 30%) groups. Univariate and multivariate logistic regression analyses with backward stepwise regression were used to select the predictive variables, and a nomogram was developed. The discrimination was evaluated based on the area under the curve (AUC). Calibration was assessed using calibration plots and the Hosmer-Lemeshow test. Clinical applications were evaluated using decision curve analysis (DCA). Results: The incidence of acute POUR was 12.72%. Preoperative statin use within 24 h, operation type, intraoperative infusion, postoperative water intake within 3 h, postoperative pain, and postoperative anxiety were included in the nomogram. The AUC values were 0.764 [95% confidence interval (CI): 0.705-0.825] in the development group and 0.741 (95% CI: 0.615-0.856) in the validation group. The calibration plots showed good calibration. The p values in the Hosmer-Lemeshow tests were 0.962 and 0.315 for the development and validation groups, respectively. The DCA showed that patients could benefit from this nomogram. Conclusion: A nomogram was developed to successfully predict acute POUR in patients with ischemic stroke following FAP. This nomogram is a convenient and effective tool for clinicians to aid in the prevention and early intervention of acute POUR. [ABSTRACT FROM AUTHOR]

The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model. Objective: Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage IIIII gastric cancer. Methods: A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies--Least absolute shrinkage and selection operator (LASSO) and random forest (RF)--we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogrammodel. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output. Results: Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy. Conclusion: In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making. [ABSTRACT FROM AUTHOR]

This study aimed to identify predictive factors for the prognosis of acute-on-chronic liver disease (AoCLD) due to both hepatitis B virus (HBV) and alcohol and to develop prognostic models to improve treatment management. AoCLD patients with HBV and alcohol as etiological factors were selected from two multicenter prospective cohorts (NCT02457637,NCT03641872) and included in separate training and validation cohorts (n = 180 and n = 148). In the training cohort, the CATCH-LIFE A nomogram (based on age, bilirubin, international normalized ratio, serum sodium, and hepatic encephalopathy score) and CATCH-LIFE B nomogram (based on age, bilirubin, international normalized ratio, serum albumin, white blood cell, platelet count, and hepatic encephalopathy score) had discriminatory ability for predicting 28-day (c-indexes of 0.910 and 0.899) and 90-day mortality (c-indexes of 0.878 and 0.887, respectively). The area under the curve values for 28-day and 90-day mortality prediction by the CATCH-LIFE A nomogram were 0.922 (95% CI : 0.874, 0.971) and 0.905 (0.856, 0.956), respectively, while those for the CATCH-LIFE B nomogram were 0.916(0.861,0.972) and 0.915 (0.866,0.964), respectively. Similar performance results were observed in the validation cohort. Optimal cut-off scores for each nomogram could be used for patient stratification in high- and low-risk groups, and the high-risk groups showed shorter survival times than the low-risk groups in both the training and validation cohorts. Two nomograms constructed from the first short-term follow-up data from patients with AoCLD due to combined HBV infection and alcohol exposure showed good predictive performance for 28-day and 90-day mortality and might be used to guide clinical management. [ABSTRACT FROM AUTHOR]

Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model's discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted P < 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73–0.82) and 0.65 (95% CI: 0.56–0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population. [ABSTRACT FROM AUTHOR]

The potential association between red blood cell distribution width (RDW) at admission and prognosis in patients with sepsis-induced cardiomyopathy(SIC) remains uncertain. The purpose of this study was to explore the prognostic value of RDW on mortality in patients with SIC. Data for this retrospective study were obtained from the MIMIC IV2.2 database. We used propensity score matching (PSM) and Cox proportional hazards regression analysis to evaluate the main risk factors associated with mortality in SIC patients. This analysis was utilized to develop a predictive nomogram. To assess the predictive accuracy and clinical usefulness of the model, we employed the concordance index (C-index) and decision curve analysis. To define the high- and low-RDW groups among patients with SIC, we determined the optimal cut-off value by maximizing the Youden index. According to the screening criteria, we identified a cohort of 1051 patients diagnosed with SIC. When comparing the high-RDW group to the low-RDW group, it was found that the high-RDW group exhibited longer Los_ICU(4.5 days vs.3.8 days, respectively, P = 0.009) and higher mortality rates at 28 days (33.8% vs. 7.8%, respectively, P < 0.001). A nomogram model was created using matched patients which included various factors such as Age, RDW, LDH, CKMB, creatinine and the administration of β-blocker. The C-index predicting 28-day survival probability was 0.846. Decision curves analysis demonstrated that the inclusion of RDW in the model provided a greater net benefit compared to excluding RDW. The prognosis of patients with SIC can be predicted by the RDW value. The nomogram model provides a useful tool in identifying and managing SIC patients. [ABSTRACT FROM AUTHOR]

Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model's discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to assess the risk factors associated with thrombotic events in obstetric antiphospholipid syndrome (OAPS) patients and to develop a predictive model specifically tailored to predict the risk of postpartum thrombosis in OAPS patients without prior thrombotic events. This research seeks to enhance clinician's awareness regarding the postpartum care and monitoring of OAPS patients. Methods: A retrospective study was conducted at the First Affiliated Hospital of the Fourth Military Medical University including 269 consecutive inpatients diagnosed with antiphospholipid syndrome (APS) from July 1, 2008 to July 31, 2022. All participants met the 2006 Sydney APS classification criteria or the "non-criteria OAPS classification". Out of 98 candidate clinical and laboratory parameters considered, 40 potential variables were selected for analysis based on expert opinion. The logistic regression mode with the Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify optimal predictive characteristics. All samples were included in the model building and a nomogram was generated based on these characteristics. The differentiation, calibration, and clinical utility of the predictive model were evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis. The model was also validated by a 1000 bootstrap tests. Results: 126 patients with OAPS were enrolled, and a total of 89 OAPS patients who had never experienced thrombosis were retrospectively analyzed. After 3 years follow-up, 32.58% of the patients (29/89) developed thrombosis. In order to create, LASSO logistic regression identified three optimal variables: the platelet count less than 125X109/L, more than one positive aPLs (antiphospholipid antibody), and the use of low molecular weight heparin (LMWH) or low dose aspirin (LDA) after delivery. A predictive model was conducted using these three predictive indicators for patients with OAPS who experience thrombosis for the first-time. This prediction model has good distinction, good calibration, and fair clinical practicality. Conclusion: Our model has good predictive ability in assessing the risk of thrombosis in patients with OAPS without prior thrombotic events. This model is easy to predict, has good discriminability and calibration, and can be utilized as a routine tool for thrombus screening in OAPS patients. [ABSTRACT FROM AUTHOR]

Objective: To construct and verify a risk prediction model for sleep disturbance in elderly patients with hypertension, aiming to offer guidance for sleep management in this demographic. Methods: A cohort of 6,708 elderly hypertensive patients from the NHANES database, spanning 2005 to 2018, met the inclusion criteria and were selected for this study. Participants were randomly assigned to a development group (n = 4,696) and a verification group (n = 2,012) in a 7:3 ratio. The occurrence of sleep disturbance was assessed across the subjects. Independent risk factors for sleep disturbance were analyzed using weighted multivariate logistic regression within the development group. A predictive model for sleep disturbance risk in elderly hypertensive patients was developed and verified using Stata 17.0. The model's predictive accuracy and stability were evaluated using the verification group's data. Results: Of the 6,708 subjects, 2,014 (30.02%) were identified with sleep disturbance, and the weighted prevalence of sleep disturbance among elderly hypertensive patients was 33.283%. Weighted multivariate logistic regression analysis in the development group revealed that six factors were independently associated with sleep disturbance: higher total depression scores, higher education level, asthma, overweight, arthritis, and work restriction (OR > 1 and P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) for the nomogram prediction model was 0.709 in the development group and 0.707 in the verification group, indicating good discrimination ability. Brier scores for the nomogram model were 0.185 in the development group and 0.189 in the verification group, both below 0.25, suggesting good calibration. Decision Curve Analysis (DCA) determined that the nomogram's clinical net benefit was maximized when the threshold probability for sleep disturbance in elderly hypertensive patients was 0.13–0.67 in the development group and 0.14–0.61 in the verification group, highlighting the model's clinical utility. Limitations: This study is not without its limitations, including issues with data collection, the absence of external validation, and the non-extrapolation of results. Conclusion: The prevalence of sleep disturbance among elderly hypertensive patients stands at 33.283%. The nomogram model, based on identified risk factors for sleep disturbance in this population, has demonstrated good predictive efficiency and clinical relevance. It serves as a valuable tool to assist healthcare providers in identifying elderly hypertensive patients at high risk for sleep disturbance. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM). Methods: Prospectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram. Result: Univariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility. Conclusion: The nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions. [ABSTRACT FROM AUTHOR]

This study aimed to construct an effective Nomogram for the differential diagnosis of cervical tuberculosis lymphadenitis (CTBL) and cervical metastatic lymph node (CMLN) based on ultrasound (US).Retrospectively analyzed 227 patients with CTBL and CMLN who attended Hangzhou Red Cross Hospital from January 2020 to October 2022, and were divided into a training set (n = 163) and a validation set (n = 64) according to the clinical data, US, and CEUS qualitative and quantitative analysis data were recorded to establish the prediction model and perform validation. The area under curve (AUC) of the receiver operating characteristic curve (ROC) was used to assess the discrimination of the model; the calibration curve and brier coefficient were used to assess the calibration of the model; and a Nomogram prediction model was constructed to visualize the results nomogram prediction model was constructed to visualize the results.Gender (OR = 0.200, 95% CI:0.090–0.470, P < 0.001), age (OR = 0.170, 95% CI:0.070–0.410, P < 0.001), liquefaction necrosis (OR = 2.560, 95% CI:1.080–6.040, P = 0.033), perfusion defect (OR = 2.570, 95% CI:1.010–6.580, P = 0.048), and standard deviation (StdDev) (OR = 3.040, 95% CI:1.220–7.570, P = 0.017) were the independent predictors of the constructed model. The AUCs of the constructed predictive model in the training set and validation set were 0.844 and 0.927, respectively; from the calibration curves, it was observed that the predicted values of the model and the actual observed values fell near the 45° diagonal, and the brier scores were 0.145 and 0.109 in the training set and validation set, respectively.StdDev combined with gender, age, and the presence of liquefaction necrosis and perfusion defects are important features to identify CTBL and CMLN, and the constructed visual nomogram is intuitive and convenient to improve the efficiency of clinical work. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC). Patients and methods: A retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes. Results: Multivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661–0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612–0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram. Conclusion: The nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of tibial plateau fractures (TPF) has been on the rise. Deep vein thrombosis (DVT) may lead to poor prognosis in patients. The systemic immune-inflammation index(SII) are novel biomarkers of inflammation, and this study aims to verify their predictive effect and construct the nomogram model. This study used binary logistic regression analysis to predict the predictive effect of SII on the occurrence of DVT in tibial plateau fracture patients. And use R studio to construct nomogram model. The results showed that Age (1.03 [1, 1.06], p = 0.032), SII (3.57 [1.68, 7.61], p = 0.04), and NC (7.22 [3.21, 16.26], p < 0.001) were independent predictive factors for DVT. The nomogram demonstrated good predictive performance with small errors in both the training and validation groups, and most clinical patients could benefit from them. The nomogram constructed based on SII can assist clinicians in early assessment of the probability of DVT occurrence. [ABSTRACT FROM AUTHOR]

Background: Achieving a pathological complete response (pCR) after neoadjuvant therapy (NAT) is considered to be a critical factor for a favourable prognosis in breast cancer. However, discordant pathological complete response (DpCR), characterised by isolated responses in the breast or axillary, represents an intermediate pathological response category between no response and complete response. This study aims to investigate predictive factors and develop models based on peripheral blood inflammatory indexes to more accurately predict NAT outcomes. Method: A total of 789 eligible patients were enrolled in this retrospective study. The patients were randomized into training and validation cohort according to a 7:3 ratio. Lasso and uni/multivariable logistic regression analysis were applied to identify the predictor variables. Two Nomograms combining clinico-pathologic features and peripheral blood inflammatory indexes were developed. Result: Molecular Subtype, HALP, P53, and FAR were used to construct the predictive models for traditional non pCR (T-NpCR) and total-pCR (TpCR). The T-NpCR group was divided into DpCR and non pCR (NpCR) subgroups to construct a new model to more accurately predict NAT outcomes. cN, HALP, FAR, Molecular Subtype, and RMC were used to construct the predictive models for NpCR and DpCR. The receiver operating characteristic (ROC) curves indicate that the model exhibits robust predictive capacity. Clinical Impact Curves (CIC) and Decision Curve Analysis (DCA) indicate that the models present a superior clinical utility. Conclusion: HALP and FAR were identified as peripheral blood inflammatory index predictors for accurately predicting NAT outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Follicular thyroid carcinoma (FTC) is the second most common thyroid malignancy and is characterized by a higher risk of distant metastasis compared to papillary thyroid cancer. Intraoperative frozen section (IOFS) diagnosis of FTC is challenging due to its limited sensitivity and accuracy, leading to uncertainty in intraoperative surgical decision-making. In response, we developed a predictive model to assess the risk of follicular carcinoma in thyroid nodules identified as suspicious for follicular neoplasm by IOFS. Methods: This model was derived from preoperative clinical and ultrasound data of 493 patients who underwent thyroid surgery at Ningbo Medical Center Lihuili Hospital. It identified five significant predictors of follicular carcinoma: nodule size, thyroglobulin (Tg) level, hypoechogenicity, lobulated or irregular margins, and thick halo. Results: The model demonstrated robust discrimination and calibration, with an area under the curve (AUC) of 0.83 (95% CI: 0.77-0.90) in the training set and 0.78 (95% CI: 0.68-0.88) in the validation set. In addition, it achieved a sensitivity of 81.63% (95% CI: 69.39-91.84) and 68.00% (95% CI: 48.00-4.00), a specificity of 77.42% (95% CI: 72.18-82.66) and 72.51% (95% CI: 65.50-78.96), an accuracy of 78.1% (95% CI: 73.4-82.4) and 71.9% (95% CI: 65.3-78.6), a positive predictive value (PPV) of 41. 67% (95% CI: 35.65-48.84) and 26.79% (95% CI: 19.40-34.33), respectively, and a negative predictive value (NPV) of 95.61% (95% CI: 92.86-97.99) and 94.07% (95% CI: 90.44-97.08) in the training and validation sets, respectively. Conclusion: The model can accurately rule out FTC in low-risk nodules, thereby providing surgeons with a practical tool to determine the necessary extent of surgical intervention for nodules flagged as suspicious by IOFS. [ABSTRACT FROM AUTHOR]

Background: Osteonecrosis of the jaw (ONJ) stands as a severe complication linked to the use of bisphosphonates, particularly zoledronic acid, which is widely prescribed for managing conditions like osteoporosis and bone metastasis. This study is geared towards the development and validation of a clinical prediction model for ONJ in patients undergoing zoledronic acid treatment. Methods: We harnessed data from the FDA Adverse Event Reporting System (FAERS) as our training dataset, while the Canada Vigilance Adverse Reaction (CVAR) database served as the testing dataset. The study encompassed patients treated with zoledronic acid and subsequently diagnosed with ONJ. We analysed a range of predictive factors, including breast cancer, bone metastasis, osteoporosis, vitamin D and calcium levels, comorbidities, the number of concomitant medications, dosage, age, weight, and gender. Logistic regression and nomogram analysis were the chosen methodologies for constructing the predictive model. To evaluate the model's performance, we utilized receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The study encompassed a total of 2,126 patients in the training cohort, 911 patients in the internal test cohort from the FAERS database, and 121 patients in the external test cohort from the CVAR database. Notable predictors for ONJ included bone metastasis (OR: 1.65, 95% CI: 1.22-2.24), osteoporosis (OR: 0.33, 95% CI: 0.21-0.52), the number of concomitant medications (OR: 1.07, 95% CI: 1.05-1.09), and the dosage of zoledronic acid (OR: 1.24, 95% CI: 1.10-1.39). The nomogram exhibited robust discriminatory power, evidenced by an area under the curve (AUC) of 0.77 in the training cohort, 0.76 in the internal test cohort, and 0.90 in the external test cohort. Calibration plots demonstrated a strong alignment between observed and predicted probabilities. Furthermore, DCA highlighted the prediction model's significant net benefit across various threshold probabilities. Conclusion: By leveraging data from both the FAERS and Canadian databases, this study has successfully developed and validated a clinical prediction model for ONJ in patients receiving zoledronic acid. This model stands as a valuable tool for clinicians, enabling them to pinpoint high-risk patients and make evidence-based treatment decisions to minimize the risk of ONJ. [ABSTRACT FROM AUTHOR]

Background: Extensively drug-resistant Acinetobacter baumannii (XDRAB) has become a significant pathogen in hospital environments, particularly in intensive care units (ICUs). XDRAB's resistance to conventional antimicrobial treatments and ability to survive on various surfaces pose a substantial threat to patient health, often resulting in severe infections such as ventilator-associated pneumonia (VAP) and bloodstream infections (BSI). Methods: We retrospectively analyzed clinical data from 559 patients with XDRAB infections admitted to Jinhua Central Hospital between January 2021 and December 2023. Patients were randomly divided into a training set (391 cases) and a testing set (168 cases). Variables were selected using Lasso regression and logistic regression analysis, and a predictive model was constructed and validated internally and externally. Model performance and clinical utility were evaluated using the Hosmer-Lemeshow test, C-index, ROC curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: Lasso regression analysis was used to screen 35 variables, selecting features through 10-fold cross-validation. We chose lambda.1se=0.03450 (log (lambda.1se)=-3.367), including 10 non-zero coefficient features. These features were then included in a multivariate logistic regression analysis, identifying 8 independent risk factors for XDRAB infection: ICU stay of 1-7 days (OR=3.970, 95%CI=1.586-9.937), ICU stay >7 days (OR=12.316, 95%CI=5.661-26.793), hypoproteinemia (OR=3.249, 95%CI=1.679-6.291), glucocorticoid use (OR=2.371, 95%CI=1.231-4.564), urinary catheterization (OR=2.148, 95% CI=1.120-4.120), mechanical ventilation (OR=2.737, 95%CI=1.367-5.482), diabetes mellitus (OR=2.435, 95%CI=1.050-5.646), carbapenem use (OR=6.649, 95%CI=2.321-19.048), and β-lactamase inhibitor use (OR=4.146, 95%CI=2.145-8.014). These 8 factors were used to construct a predictive model visualized through a nomogram. The model validation showed a Cindex of 0.932 for the training set and 0.929 for the testing set, with a Hosmer-Lemeshow test p-value of 0.47, indicating good calibration. Furthermore, the DCA curve demonstrated good clinical decision-making performance, and the CIC curve confirmed the model's reliable clinical impact. Conclusion: Regression analysis identified ICU stay duration, hypoproteinemia, glucocorticoid use, urinary catheterization, mechanical ventilation, diabetes mellitus, carbapenem use, and β-lactamase inhibitor use as independent risk factors for XDRAB infection. The corresponding predictive model demonstrated high accuracy and stability. [ABSTRACT FROM AUTHOR]

Background: Febrile seizures constitute a prevalent emergency in early childhood. Previous studies indicated that certain febrile seizures in children may progress to epilepsy, exerting a substantial impact on children's health and developmental trajectory. The objective of this study was to formulate a predictive nomogram to assess the likelihood of transitioning from febrile seizures to epilepsy in pediatric patients, thereby facilitating informed decisions regarding medical interventions for febrile seizures. Methods: A total of 306 patients were enrolled and categorized into training (70%) and test (30%) cohorts. Clinical characteristics were subjected to comparison utilizing chi-squared and t tests. Multivariate logistic regression was employed to identify significant factors for predicting the risk of transitioning from febrile seizures to epilepsy, leading to the development of a nomogram. The nomogram's performance was assessed through receiver operating characteristic curves, calibration, and decision curve analysis. Results: Predictive factors associated with the transition to epilepsy encompassed lower Na, elevated RDW, IL-6, and increased background slow rhythm and epileptiform discharges in EEG. The nomogram, incorporating five factors, exhibited commendable predictive value (AUC train = 0.812, AUC test = 0.791) for assessing the risk of transitioning from febrile seizures to epilepsy. Calibration analyses confirmed reliability, and decision curve analysis underscored its clinical utility. Conclusions: Lower Na, elevated RDW, IL-6, background slow rhythm, and epileptiform discharges are associated with the risk of transitioning from febrile seizures to epilepsy. The nomogram stands as a valuable tool for predicting this risk, aiding in the strategic implementation of medical interventions to enhance outcomes for patients with febrile seizures. [ABSTRACT FROM AUTHOR]

Background: This research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients. Methods: 309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model's predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model. Results: Based on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784–0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability. Conclusion: The online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society. [ABSTRACT FROM AUTHOR]

Chlamydia psittaci pneumonia (CPP) exhibits similar characteristics as of COVID-19 with respect to clustering outbreaks and onset symptoms. This study is aimed at exploring the different clinical manifestations of both pneumonias to establish a simple nomogram to distinguish them. This multicenter, retrospective, case–control study compared two independent cohorts of patients with CPP or COVID-19. The risk factors of CPP were analyzed using multivariate logistic regression, which was used to establish the nomogram. Both patients with CPP and COVID-19 exhibited similar clinical symptoms. As compared to patients with COVID-19, a higher proportion of patients with CPP had nervous system symptoms. Patients with CPP had higher inflammatory indicators, creatine kinase, and lower lymphocyte and albumin. They also had lower proportions of ground-glass opacity and bilateral lung involvement than COVID-19 patients. Furthermore, patients with CPP had higher 30 day mortality as well as higher rates of severe pneumonia, septic shock, and ICU admission. Multivariate logistic regression showed that nervous system symptoms, lymphocytes, creatine kinase, bilateral lung lesions, and ground-glass opacity were risk factors for CPP. Incorporating these five factors, the nomogram achieved good concordance index of 0.989 in differentiating CPP from COVID-19, and had well-fitted calibration curves. Despite similar clinical characteristics, nervous system symptoms, lymphocyte, creatine kinase, lesions in bilateral lungs, and ground-glass opacity may help in differentiating the pneumonias. These were combined into a clinically useful nomogram for rapid and early identification of CPP to avoid misdiagnosis and help in the decision-making process. [ABSTRACT FROM AUTHOR]

This study explored the role of radioiodine therapy (RAI) in low-risk thyroid cancer patients and examined the disease-specific survival (DSS) rates in a large cohort of differentiated thyroid cancer patients (DTC). We obtained patient data from SEER database. Patients who underwent total thyroidectomy were included and categorized into three groups based on histology: classical papillary thyroid carcinoma (C-PTC), follicular type variant carcinoma (FV-PTC), and follicular thyroid cancer (FTC). Patients with distant metastasis, tumor size ≥ 200 mm, chemotherapy, or any type of radiation other than RAI were also excluded. A nomogram was developed and tested for discrimination and calibration. In total, 96,532 thyroid cancer cases were examined, including 59,460 C-PTC, 31,583 FV-PTC, and 5,489 FTC cases. Age > 65 years and male sex were correlated with lower survival rates across the subtypes. In addition, extrathyroidal extension had a worse survival effect in patients with FTC. DSS rates were compared between patients who received RAI and those who did not, with a 3% difference in C-PTC (94% vs. 91%, p < 0.001), 2% in FV-PTC (92% vs. 90%, p < 0.001), and 1% in FTC (89% vs. 88%, p = 0.084) at 15 years. The nomograms for long-term DSS showed high discriminatory abilities with C-indices of 0.815, 0.805, and 0.781 for C-PTC, FV-PTC, and FTC, respectively. The developed nomogram can be used in the treatment plan for patients with DTC. Our study emphasizes the prognostic factors for DTC and highlights the need for personalized treatment plans based on individual risk profiles. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The use of nomograms in predicting the prognosis of early‐stage non‐small cell lung cancer (NSCLC), particularly in elderly patients, is not widespread. A validated prognostic model specifically for NSCLC patients over 80 years old holds promising potential for clinical application in forecasting patient outcomes.The prognostic value of various factors for NSCLC patients aged 80 and above was evaluated using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2017). Kaplan–Meier (KM) curves, Cox proportional hazards regression models, and nomogram were utilized to evaluate the impact of each factor on cancer‐specific survival (CSS).A cohort comprising 7045 individuals was selected for inclusion in the analysis. Through rigorous statistical analysis, 10 independent prognostic factors were identified and incorporated into the nomogram. The nomogram's receiver operating characteristic (ROC) curve area under the curve (AUC) was higher than that of the AJCC 7th edition TNM staging system's predicted CSS (0.744 versus 0.602), establishing the superior prognostic value of the nomogram.We have successfully created a highly accurate and discriminative nomogram that enables oncologists to predict the survival outcome of each individual patient with I/II NSCLC who is 80 years or older. [ABSTRACT FROM AUTHOR]

Background: Gangrene and perforation are severe complications of acute appendicitis, associated with a higher mortality rate compared to uncomplicated appendicitis. Accurate preoperative identification of Gangrenous or perforated appendicitis (GPA) is crucial for timely surgical intervention. Methods: This retrospective multicenter study includes 796 patients who underwent appendectomy. Univariate and multivariate logistic regression analyses are used to develop a nomogram model for predicting GPA based on laboratory tests and computed tomography (CT) findings. The model is validated using an external dataset. Results: Seven independent predictors were included in the nomogram: white blood cell count, lymphocyte count, D-dimer, serum glucose, albumin, maximum outer diameter of the appendix, and presence of appendiceal fecalith. The nomogram achieved good discrimination and calibration in both the training and testing sets. In the training set, the AUC was 0.806 (95%CI: 0.763–0.849), and the sensitivity and specificity were 82.1% and 66.9%, respectively. The Hosmer-Lemeshow test showed good calibration (P = 0.7378). In the testing set, the AUC was 0.799 (95%CI: 0.741–0.856), and the sensitivity and specificity were 70.5% and 75.3%, respectively. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram. Conclusion: The laboratory test-CT nomogram model can effectively identify GPA patients, aiding in surgical decision-making and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups. Methods: A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set. Results: Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility. Conclusions: Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility. [ABSTRACT FROM AUTHOR]

Background: The dysregulation of sialylation plays a pivotal role in cancer progression and metastasis, impacting various aspects of tumor behavior. This study aimed to investigate the prognostic significance of long non-coding RNAs (lncRNAs) in relation to sialylation. Additionally, we aimed to develop a signature of sialylation-related lncRNAs in the context of bladder cancer. Methods: This study used transcriptomic data and clinical information from the TCGA (the Cancer Genome Atlas) database to screen for sialylation-related lncRNAs and constructed a prognostic model. The relationships between these lncRNAs and biological pathways, immune cell infiltration, drug sensitivity, etc., were analyzed, and the expression of some lncRNAs was validated at the cellular level. Results: This study identified 6 prognostic lncRNAs related to sialylation and constructed a risk score model with high predictive accuracy and reliability. The survival period of patients in the high-risk group was significantly lower than that of the low-risk group, and it was related to various biological pathways and immune functions. In addition, this study found differences in the sensitivity of patients in different risk groups to chemotherapy drugs, providing a reference for personalized treatment. Conclusion: In this study, we examined the relationship between sialylation-related lncRNA and the prognosis of bladder cancer, providing new molecular markers and potential targets for diagnosis and treatment. Our research revealed correlations between sialylation-related lncRNA characteristics and clinicopathological features, potential mechanisms, somatic mutations, immune microenvironment, chemotherapy response, and predicted drug sensitivity in bladder cancer. Additionally, in vitro cellular studies were conducted to validate these findings and lay the groundwork for future clinical applications. [ABSTRACT FROM AUTHOR]

Background: Uterine leiomyosarcoma (uLMS) accounts for roughly 70% of all uterine sarcomas, with recurrence and mortality rates notably higher than those of other uterine tumors. The prognosis of uLMS patients who have distant metastases remains poor. The objective of this study was to determine independent risk variables related to distant metastases in patients with uLMS and prognostic factors for those with distant metastases. Subsequently, two practical nomograms were developed and validated to assess the probability of distant metastases and predict survival outcomes for these with distant metastases, respectively. Methods: A real-world retrospective study was carried out using data from patients diagnosed with primary uLMS in the Surveillance, Epidemiology, and End Results (SEER) database spanning the years 2010 to 2015. Univariate and multivariate logistic regression analyses were utilized to identify clinicopathological characteristics related to the risk of distant metastases, while univariate and multivariate Cox regressions were employed to determine prognostic factors. Then, a risk nomogram incorporating independent risk variables and a prognostic nomogram integrating independent prognostic factors were established in the training cohort and validated for accuracy in the validation cohort, respectively. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration curves were utilized to measure the accuracy of nomograms, while decision curve analysis (DCA) curves were employed to assess their clinical benefit capacity. Based on the median total point derived from the prognostic nomogram, patients were stratified into high- and low-risk groups. The differentiation ability of the prognostic nomogram was evaluated using Kaplan-Meier survival analysis with the log-rank test. Results: The study encompassed 1,362 patients diagnosed with uLMS, among whom 337 cases (24.7%) manifested synchronous distant metastases at the initial diagnosis. Univariate and multivariate logistic regression analyses identified race, histological grade, T stage, N stage, tumor size, surgery, and chemotherapy as independent risk factors for distant metastases in uLMS patients. The outcomes of both univariate and multivariate Cox analyses indicated that surgery and chemotherapy emerged as independent protective factors for prognosis in uLMS patients with distant metastases, whereas higher histological grade and T stage were identified as independent risk factors. The risk nomogram incorporating independent risk variables and the prognostic nomogram integrating independent prognostic factors could respectively predict the risk of metastases and the prognosis very effectively in both training and validation cohorts. Conclusions: In summary, we developed the novel well-validated risk nomogram to precisely assess the probability of metastases in uLMS patients and prognostic nomogram to predict the prognosis of those with distant metastases, providing decision-making guidance for tailoring individualized clinical management of these patients. [ABSTRACT FROM AUTHOR]

Late-onset neonatal sepsis has a high mortality rate in premature infants. To date, no single test in the evaluation of neonatal sepsis has been demonstrated to be both sensitive and specific enough to assist in timely decision making. The aim of our study is to develop a predictive model that can be applied to all premature babies, using clinical and laboratory findings in premature babies, to recognize late-onset neonatal sepsis.65 premature patients diagnosed with culture-proven late-onset neonatal sepsis and hospitalized in Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital neonatal intensive care unit between January 2018 and December 2020, and 65 premature newborns of similar age and gender who did not have sepsis were included in the study retrospectively.In our study, feeding difficulties, worsening in clinical appearance and fever were found to be significant among clinical findings, while thrombocytopenia and high C-reactive protein among laboratory findings are the strongest data supporting late-onset neonatal sepsis. In multiple regression analysis, thrombocytopenia, mean platelet volume, C-reactive protein, lymphocyte count and feeding difficulties had the highest odds ratio (p <  0.05). By converting these data into a scoring system, a nomogram was created that can be easily used by all clinicians.In our study, we developed a scoring system that can be easily applied to all premature patients by evaluating the clinical and laboratory findings in late-onset neonatal sepsis. We think that it will help in recognizing late-onset neonatal sepsis and strengthening the treatment decision. Predicting the individual probability of sepsis in preterm newborns may provide benefits for uninfected newborns to be exposed to less antibiotics, not to be separated from mother and baby, and to reduce healthcare system expenditures. The nomogram can be used to assess the likelihood of sepsis and guide treatment decision. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Materials and Methods Results Conclusions This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence‐free survival (NRFS) in patients with early‐stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes.The management of early‐stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy.A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C‐index of 0.67 (95% CI: 0.58–0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low‐risk and high‐risk groups for occult cervical nodal metastases (p < 0.05).The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node‐negative OSCC patients by incorporating tumor‐specific and patient‐specific risk factors. [ABSTRACT FROM AUTHOR]

Background: Fruquintinib is a third-line and subsequent targeted therapy for patients with metastatic colorectal cancer (mCRC). Identifying survival predictors after fruquintinib is crucial for optimizing the clinical use of this medication. Objectives: We aimed to identify factors influencing the prognosis of patients with mCRC treated with fruquintinib and to leverage these insights to develop a nomogram model for estimating survival rates in this patient population. Design: Multicenter retrospective observational study. Methods: We collected patient data from January 2019 to October 2023, with one healthcare institution's data serving as the training cohort and the other three hospitals' data serving as the multicenter validation cohort. The nomogram for overall survival was calculated from Cox regression models, and variable selection was screened using the univariate Cox regression analysis with additional variables based on clinical experience. Model performance was measured by the concordance index (C-index), calibration curves, decision curve analyses (DCA), and utility (patient stratification into low-risk vs high-risk groups). Results: Data were ultimately collected on 240 patients, with 144 patients included in the training cohort and 96 included in the multicenter validation cohort. Predictors included in the nomogram were CA199, body mass index, T stage, the primary site of the tumor, and other metastatic and pathological differentiation. The C-index of the nomogram in the training set and multicenter validation was 0.714 and 0.729, respectively. The models were fully calibrated and their predictions aligned closely with the observed data. DCA curves indicated the promising clinical benefits of the predictive model. Finally, the reliability of the model was also verified through the risk classification using the nomogram. Conclusions: We constructed a nomogram for mCRC treated with fruquintinib based on six variables that may be used to assist in personalizing the use of the drug. Plain language summary: A nomogram for predicting OS after application of fruquintinib in patients with mCRC The prognostic predictors of fruquintinib as a third-line and subsequent treatment agent for patients with mCRC have not been established. In this study, we explored possible factors influencing its prognosis and developed a nomogram model for estimating survival rates in this patient population. The nomogram, based on six key variables including CA199, BMI, T stage, primary tumor site, other metastatic sites, and pathological differentiation, was validated through a rigorous multicenter validation process. The nomogram has the potential to help clinicians personalize the use of fruquintinib for mCRC patients. [ABSTRACT FROM AUTHOR]

Background: The issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work. Methods: This study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves. Results: After surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P < 0.05). These factors were incorporated into a logistic regression model generated by R software, resulting in a ROC curve with an area under the curve (AUC) of 0.916 (95% CI: 0.884–0.947). The Youden index was 0.731, and the cutoff value was 0.228. Sensitivity and specificity were 0.920 and 0.811, respectively. The H-L test demonstrated good model fit (χ2 = 12.858, P = 0.117, P > 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities. Conclusions: The postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients' return to work. Caregivers should prioritize the early identification of various patient types for proactive intervention to enhance the rate of postoperative return to work. [ABSTRACT FROM AUTHOR]

Angiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients' prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Postoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population. Methods: A total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA). Results: A total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO2) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719–0.762), and it was 0.703 for the validation cohort (95% CI: 0.657–0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort. Conclusion: This study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions. [ABSTRACT FROM AUTHOR]

Background: In this retrospective study, we aimed to identify key risk factors and establish an interpretable model for HCC with a diameter = 5 cm using Lasso regression for effective risk stratification and clinical decision-making. Methods: In this study, 843 patients with advanced hepatocellular carcinoma (HCC) and tumor diameter = 5 cm were included. Using Lasso regression to screen multiple characteristic variables, cox proportional hazard regression and random survival forest models (RSF) were established. By comparing the area under the curve (AUC), the optimal model was selected. The model was visualized, and the order of interpretable importance was determined. Finally, risk stratification was established to identify patients at high risk. Result: Lasso regression identified 8 factors as characteristic risk factors. Subsequent analysis revealed that the lasso-cox model had AUC values of 0.773, 0.758, and 0.799, while the lasso-RSF model had AUC values of 0.734, 0.695, and 0.741, respectively. Based on these results, the lasso-cox model was chosen as the superior model. Interpretability assessments using SHAP values indicated that the most significant characteristic risk factors, in descending order of importance, were tumor number, BCLC stage, alkaline phosphatase (ALP), ascites, albumin (ALB), and aspartate aminotransferase (AST). Additionally, through risk score stratification and subgroup analysis, it was observed that the median OS of the low-risk group was significantly better than that of the middle- and high-risk groups. Conclusion: We have developed an interpretable predictive model for middle and late HCC with tumor diameter = 5 cm using lasso-cox regression analysis. This model demonstrates excellent prediction performance and can be utilized for risk stratification. [ABSTRACT FROM AUTHOR]

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from communityacquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB. [ABSTRACT FROM AUTHOR]

Aim: To develop a nomogram‐based predictive model of tooth survival by comprehensively analysing clinical and radiographic risk factors of tooth loss (TL). Materials and Methods: In this study, 3447 teeth of 131 subjects who underwent non‐surgical periodontal treatment were examined retrospectively within a mean follow‐up period of 11.6 years. The association of risk factors including clinical and radiographic parameters with TL was assessed using univariate and multivariate Cox regression analyses. A nomogram‐based predictive model was developed, and its validation and discriminatory ability were analysed. Results: In all, 313 teeth were lost in 94 patients in this study (overall tooth loss [OTL] 9.08%; 0.21 teeth/patient/year). Male, heavy smoking, molar teeth, probing depth (PD), attachment loss (AL), tooth mobility and radiographic bone loss were significantly associated with TL (p <.05). A gradient effect of tooth mobility on TL increased from degree I to III versus none (p <.0001). The area under the curve (AUC) of the model was 0.865. Calibration curve and decision curve analysis demonstrated good performance and high net benefit, respectively. Conclusions: Adopting a specific nomogram could facilitate the prediction of tooth survival and the development of tailored treatment plans in Chinese patients with advanced periodontitis. [ABSTRACT FROM AUTHOR]

Objective: To identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia and to construct a nomogram predictive model. Methods: This retrospective study included 292 patients who underwent gastrointestinal endoscopy under acupuncture anesthesia at the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from June 2020 to May 2023. Logistic regression analysis was used to identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. A nomogram prediction model was constructed using the RMS package of R4.1.2 software based on the independent risk factors identified. The predictive performance of the model was assessed using consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. Results: Seventy-five patients (25.68%) had complications. Body mass index (BMI), history of cardiovascular diseases, fasting time, history of respiratory diseases, and Sedation-Agitation Scale (SAS) score were identified as risk factors for complications. Based on this risk, a nomogram predictive model was constructed. The C-index of the nomogram model was 0.927. Calibration curve showed a good consistency between actual observations and nomogram predictions. The ROC curve area under curve (AUC) was 0.927 (95% CI: 0.895-0.959), indicating a certain predictive value for the occurrence of complications. When the optimal cut-off value was selected, the sensitivity and specificity of the model were 77.0% and 92.0%, respectively, indicating that the predictive model was effective. Conclusions: BMI, history of cardiovascular disease, fasting time, history of respiratory disease, and SAS score are independent risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. The constructed nomogram predictive model has a good performance in predicting the occurrence of complications in patients undergoing gastrointestinal endoscopy with under acupuncture anesthesia. [ABSTRACT FROM AUTHOR]

To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients who sustain a transient ischemic attack (TIA). A retrospective training dataset (n = 76) and a prospective validation dataset (n = 34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features were selected in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. Model performance was assessed by computing the receiver operating characteristic curve and decision curve analysis (DCA). We found a higher proportion of diabetes and hypertension in the patients with recurrent TIA compared with the stable patients in both the training and validation datasets (P < 0.05). Recurrent patients had significantly higher ABCD2 scores and plaque scores compared to stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (odds ratio [OR], 2.75; 95% confidence interval [CI], 1.47–6.40; and OR, 1.20; 95% CI, 1.07–1.41, respectively). The radiomics model had area under the curve values of 0.737 (95% CI, 0.715–0.878) in the training dataset and 0.899 (95% CI, 0.706–0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P < 0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI, 0.895–0.978) in the training dataset and 0.935 (95% CI, 0.830–0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram. The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in patients with TIA. [ABSTRACT FROM AUTHOR]

Aims: Establishing a nomogram to estimate the probability of oral mucosal membrane pressure injury of endotracheal tube‐intubated hospitalized patients in intensive care unit. Design: Multicentre prospective cohort study. Methods: Using Lasso regression and COX regression, variable selection was performed on demographic, clinical and laboratory data of 1037 ICU endotracheal tube‐intubated hospitalized patients from West China Hospital, to construct a nomogram. External validation was conducted on 484 ICU endotracheal tube‐intubated patients from People's Hospital of Zhongjiang County. Results: Among 38 potential predictors, five variables emerged as independent predictors, integrated into the nomogram: administration of antibiotics, nutritional therapy duration, agitation, hypotension and albumin levels. Conclusions: We established a nomogram based on the hospital characteristics of ICU endotracheal tube‐intubated patients, aiding in the prediction of the occurrence of oral mucosal membrane pressure injury. Reporting Method: The study followed TRIPOD guidelines. Relevance to Clinical Practice: The nomogram we developed can assist clinical worker in better identifying at‐risk patients and risk factors. It enables the implementation of evidence‐based nursing interventions in care to prevent the development of oral mucosal membrane pressure injury. Trial registration: The study has been registered with the Chinese Clinical Trial Registry (http://www.chictr.org.cn) under registration number ChiCTR2200056615. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer. Methods: A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms—k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine—were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis. Results: The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model. Conclusion: The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer. [ABSTRACT FROM AUTHOR]