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Abstract Purpose Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders. Methods Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted. Results A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance. Conclusions We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

Abstract Background This dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception. Methods An analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results Lupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787–0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation. Conclusion The predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception.

Yaping Wang,1 Yiyi Zhao,1 Chaolu Shi,2 Juanqing Li,1,3,4 Xiufeng Huang1,3,4 1Zhejiang University, Womens Hospital, Sch Med, Department Obstet & Gynecol, Hangzhou, Zhejiang, People’s Republic of China; 2Cixi maternity&health Care Hospital, Department Obstet & Gynecol Ningbo, Ningbo, Zhejiang, People’s Republic of China; 3Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 4Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Juanqing Li; Xiufeng Huang, Email ljq0313@zju.edu.cn; huangxiufeng@zju.edu.cnPurpose: The aim of this study was to investigate the risk factors of postmenopausal special uterine leiomyoma pathological types or leiomyosarcoma and to develop a nomogram for clinical risk assessment, ultimately to reduce unnecessary surgical interventions and corresponding economic expenses.Methods: A total of 707 patients with complete information were enrolled from 1 August 2012 to 1 August 2022. Univariate and multivariate logistic regression models were used to analyse the association between variables and special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. A nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients was developed and validated by bootstrap resampling. The calibration curve was used to assess the accuracy of the model and receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were compared with the clinical experience model.Results: The increasing trend after menopause, the diameter of the largest uterine fibroid, serum carcinoembryonic antigen 125 concentration, Serum neutrophil to lymphocyte ratio, and Serum phosphorus ion concentration were independent risk factors for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. We developed a user-friendly nomogram which showed good diagnostic performance (AUC=0.724). The model was consistent and the calibration curve of our cohort was close to the ideal diagonal line. DCA indicated that the model has potential value for clinical application. Furthermore, our model was superior to the previous clinical experience model in terms of ROC and DCA.Conclusion: We have developed a prediction nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. This nomogram could serve as an important warning signal and evaluation method for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients.Keywords: special uterine leiomyoma pathological types, leiomyosarcoma, postmenopausal women, prediction nomogram, bootstrap

Abstract The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer–Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Background: Carbapenem-resistant Gram-negative organism (CRO) infection is a critical clinical disease with high mortality rates. The 30-day mortality rate following antibiotic treatment serves as a benchmark for assessing the quality of care. Colistin sulfate is currently considered the last resort therapy against infections caused by CRO. Nevertheless, there is a scarcity of reliable tools for personalized prognosis of CRO infections. This study aimed to develop and validate a nomogram to predict the 30-day all-cause mortality in patients with CRO infection who underwent colistin sulfate treatment. Methods: A prediction model was developed and preliminarily validated using CRO-infected patients treated with colistin sulfate at Tongji Hospital in Wuhan, China, who were hospitalized between May 2018 and May 2023, forming the study cohort. Patients admitted to Xianning Central Hospital in Xianning, China, between May 2018 and May 2023 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of 30-day allcause mortality. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and the calibration curve were used to evaluate model performance. The decision curve analysis (DCA) was used to assess the model clinical utility. Results: A total of 170 patients in the study cohort and 65 patients in the external validation cohort were included. Factors such as age, duration of combination therapy, nasogastric tube placement, history of previous surgery, presence of polymicrobial infections, and occurrence of septic shock were independently associated with 30-day all-cause mortality and were used to construct the nomogram. The AUC of the nomogram constructed from the above six factors was 0.888 in the training set. The Hosmer-Lemeshow test showed that the model was a good fit (p = 0.944). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. Conclusion: A nomogram was developed and validated to predict the occurrence of 30-day all-cause mortality in patients with CRO infection treated with colistin sulfate. This nomogram offers healthcare providers a precise and efficient means for early prediction, treatment management, and patient notification in cases of CRO infection treated with colistin sulfate. [ABSTRACT FROM AUTHOR]

Purpose: Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders. Methods: Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted. Results: A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance. Conclusions: We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice. [ABSTRACT FROM AUTHOR]

Background: This dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception. Methods: An analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: Lupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787–0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation. Conclusion: The predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception. [ABSTRACT FROM AUTHOR]

BackgroundPostpartum urinary incontinence (UI) is a serious condition that significantly affects the quality of life. Several studies have demonstrated that it is associated with pelvic floor dysfunction. This study aimed to develop and validate a UI risk prediction model to identify primiparas with singleton pregnancies at high risk.MethodsA multistage stratified random sampling process was used. UI was measured using the International Standard Consultation on Incontinence Questionnaire Form (a modified Bristol questionnaire, ICIQ-FLUTS). Records of 1,340 primiparas with singleton pregnancies were reviewed, and data were collected from January 2014 to December 2014 in multiple centers. A univariate logistic regression analysis was performed, followed by a multivariable logistic regression analysis of the data. Using bootstrap resampling, we constructed a nomogram to assess postpartum UI risk.ResultsA total of 1,340 patients were enrolled, including 345 with postpartum UI and 995 with non-postpartum UI. The occurrence of postpartum UI was significantly related to the mode of delivery, family history of UI, coffee or tea consumption, antenatal UI, and frequent cough. The nomogram exhibited good discriminatory ability with a C-index of 0.718 (95% confidence interval: 0.684–0.752) and a bootstrap-corrected C-index of 0.716. Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the decision curve analysis indicated that the nomogram exhibited favorable clinical applicability.ConclusionThe decision curve analysis suggests that the nomogram could provide clinical value. The clinician will then feel more confident about making clinical recommendations regarding postpartum UI screening for primiparous women with singleton pregnancies.

Background: Neonatal respiratory failure (NRF) is a critical condition with high morbidity and mortality rates. This study aimed to develop a nomogram prediction model to early predict the risk of death in Chinese neonates with NRF. Methods: A retrospective analysis was conducted on NRF neonates from 21 tertiary neonatal intensive care units (NICUs) across 13 prefecture-level cities in Jiangsu Province, China, from March 2019 to March 2022. NRF neonates from one random NICU were selected as the external validation set, while those from the remaining 20 NICUs were divided into the training set and the internal validation set at a 7:3 ratio. Death was the primary outcome. LASSO regression and multivariate logistic regression were used to identify the predictive factors from the training set and then the nomogram was constructed. Results: A total of 5387 neonates with NRF were included in the analysis. Among them, 3444 were in the training set, 1470 were in the internal validation set, and 473 were in the external validation set. The nomogram was constructed based on the eight predictors of the 1-min Apgar score, birth weight, gestational age, the relationship between birth weight and gestational age, mode of first respiratory support, inhaled nitric oxide, antenatal corticosteroids, and vasoactive drugs. The area under the curve of the nomogram in the training set, internal validation set, and external validation set was 0.763, 0.733, and 0.891, respectively. The P-values of the Hosmer-Lemeshow goodness of fit test were 0.638, 0.273, and 0.253, respectively. Brier scores were 0.066, 0.072, and 0.037, respectively. The decision curve analysis demonstrated a significant net benefit in all cases. These data indicate the good performance of the nomogram. Conclusions: This nomogram can serve as a reference for clinicians to identify high-risk neonates early and reduce the incidence of neonatal mortality.

Abstract Background The rising number of women giving birth at advanced maternal age has posed significant challenges in obstetric care in recent years, resulting in increased incidence of neonatal transfer to the Neonatal Intensive Care Unit (NICU). Therefore, identifying fetuses requiring NICU transfer before delivery is essential for guiding targeted preventive measures. Objective This study aims to construct and validate a nomogram for predicting the prenatal risk of NICU admission in neonates born to mothers over 35 years of age. Study design Clinical data of 4218 mothers aged ≥ 35 years who gave birth at the Department of Obstetrics of the Second Hospital of Shandong University between January 1, 2017 and December 31, 2021 were reviewed. Independent predictors were identified by multivariable logistic regression, and a predictive nomogram was subsequently constructed for the risk of neonatal NICU admission. Results Multivariate logistic regression demonstrated that the method of prenatal screening, number of implanted embryos, preterm premature rupture of the membranes, preeclampsia, HELLP syndrome, fetal distress, premature birth, and cause of preterm birth are independent predictors of neonatal NICU admission. Analysis of the nomogram decision curve based on these 8 independent predictors showed that the prediction model has good net benefit and clinical utility. Conclusion The nomogram demonstrates favorable performance in predicting the risk of neonatal NICU transfer after delivery by mothers older than 35 years. The model serves as an accurate and effective tool for clinicians to predict NICU admission in a timely manner.

Abstract Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (

Background: The rising number of women giving birth at advanced maternal age has posed significant challenges in obstetric care in recent years, resulting in increased incidence of neonatal transfer to the Neonatal Intensive Care Unit (NICU). Therefore, identifying fetuses requiring NICU transfer before delivery is essential for guiding targeted preventive measures. Objective: This study aims to construct and validate a nomogram for predicting the prenatal risk of NICU admission in neonates born to mothers over 35 years of age. Study design: Clinical data of 4218 mothers aged ≥ 35 years who gave birth at the Department of Obstetrics of the Second Hospital of Shandong University between January 1, 2017 and December 31, 2021 were reviewed. Independent predictors were identified by multivariable logistic regression, and a predictive nomogram was subsequently constructed for the risk of neonatal NICU admission. Results: Multivariate logistic regression demonstrated that the method of prenatal screening, number of implanted embryos, preterm premature rupture of the membranes, preeclampsia, HELLP syndrome, fetal distress, premature birth, and cause of preterm birth are independent predictors of neonatal NICU admission. Analysis of the nomogram decision curve based on these 8 independent predictors showed that the prediction model has good net benefit and clinical utility. Conclusion: The nomogram demonstrates favorable performance in predicting the risk of neonatal NICU transfer after delivery by mothers older than 35 years. The model serves as an accurate and effective tool for clinicians to predict NICU admission in a timely manner. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the factors associated with pathologic node-negativity (ypN0) in patients who received neoadjuvant chemotherapy (NAC) to develop and validate an accurate prediction nomogram. Methods: The CSBrS-012 study (2010-2020) included female patients with primary breast cancer treated with NAC followed by breast and axillary surgery in 20 hospitals across China. In the present study, 7,711 eligible patients were included, comprising 6,428 patients in the primary cohort from 15 hospitals and 1,283 patients in the external validation cohort from five hospitals. The hospitals were randomly assigned. The primary cohort was randomized at a 3:1 ratio and divided into a training set and an internal validation set. Univariate and multivariate logistic regression analyses were performed on the training set, after which a nomogram was constructed and validated both internally and externally. Results: In total, 3,560 patients (46.2%) achieved ypN0, and 1,558 patients (20.3%) achieved pathologic complete response in the breast (bpCR). A nomogram was constructed based on the clinical nodal stage before NAC (cN), ER, PR, HER2, Ki67, NAC treatment cycle, and bpCR, which were independently associated with ypN0. The area under the receiver operating characteristic curve (AUC) for the training set was 0.80. The internal and external validation demonstrated good discrimination, with AUCs of 0.79 and 0.76, respectively. Conclusion: We present a real-world study based on nationwide large-sample data that can be used to effectively screen for ypN0 to provide better advice for the management of residual axillary disease in breast cancer patients undergoing NAC. [ABSTRACT FROM AUTHOR]

Background: The coronary no-reflow (NR) phenomenon is an independent predictor of major adverse cardiac events (MACEs). This study aimed to establish a clinical and comprehensive nomogram for predicting NR in acute myocardial infarction (AMI) patients after primary percutaneous coronary intervention (pPCI). Methods: The multivariable logistic regression analysis was performed to determine the NR-related factors. A nomogram was established via several clinical and biochemical factors, and the performance was evaluated via discrimination, calibration, and clinical factors. Results: The study consisted of 3041 AMI patients after pPCI, including 2129 patients in the training set (70%) and 912 patients in the validation set (30%). The NR event was 238 in the training set and 87 in the validation set. The level of N-terminal prohormone B-type natriuretic peptide (NT-proBNP), basophil count (BASO), neutrophil count (NEUBC), D-dimer, hemoglobin (Hb), and red blood cell distribution width (RDW.CV) in NR patients showed statistically significant differences. In the training set, the C-index was 0.712, 95% CI 0.677 to 0.748. In the validation set, the C-index was 0.663, 95% CI 0.604 to 0.722. Conclusions: A nomogram that may predict NR in AMI patients undergoing pPCI was established and validated. We hope this nomogram can be used for NR risk assessment and clinical decision-making and significantly prevent potentially impaired reperfusion associated with NR. [ABSTRACT FROM AUTHOR]

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model's calibration and predictability. The model's clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions. [ABSTRACT FROM AUTHOR]

Yue Liu, Shi Qi Xie, Xia Yang, Jing Lan Chen, Jian Rong Zhou School of Nursing, Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Jian Rong Zhou; Shi Qi Xie, School of Nursing, Chongqing Medical University, 1 Medical College Road, Yu Zhong District, Chongqing, 400016, People’s Republic of China, Tel +86-135 0830 0955 ; +86-156 0833 2043, Fax +86-23-63555767, Email 202028@cqmu.edu.cn; xie47@cqmu.edu.cnPurpose: The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.Patients and Methods: From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.Results: A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.Conclusion: A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.Keywords: obstructive sleep apnea, children, cephalometric, prediction nomogram, risk prediction model

Background: Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan‐cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk. Methods: Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence‐based cancer classification system (BM‐CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM‐CCS and the nomogram, respectively. Results: A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%–17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%–5.6%), and category C (1.2%; 95% CI: 1.1%–1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM‐CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%–88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%–87.1%). The decision curve analysis also demonstrated the clinical usefulness. Conclusion: The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM. [ABSTRACT FROM AUTHOR]

Autoimmunity is present in patients with stable chronic obstructive pulmonary disease (COPD), playing a role in indirect and direct ways. We aimed to explore whether autoimmunity could play a role in COPD exacerbations and construct autoimmunity-related prediction models. This prospective, longitudinal, observational cohort study enrolled 155 patients with acute COPD exacerbations (AECOPD) followed for at least two years. The laboratory parameters, including complete blood count, serum immunoglobulins G/A/M and complement C3/C4 levels, were collected at enrollment. We studied the demographic characteristics, clinical characteristics and laboratory parameters to identify independent risk factors and build predictive models. The results showed that lower lymphocyte count was associated with noninvasive ventilation (NIV) in patients with AECOPD (the odds ratio [OR] 0.25, the 95% confidence interval [CI]: 0.08–0.81, P = 0.02). Lymphocyte count performed well with an area under the curves (AUC) of 0.75 (P < 0.0001, sensitivity: 78.1%, specificity: 62.3%, cutoff value [Cov] ≤ 1.1). The C index, calibration plot, decision curve analysis (DCA) and bootstrap repetitions indicated that this clinical prediction model based on lymphocyte count for NIV in patients with AECOPD performed well. Having prior home oxygen therapy (OR: 2.82, 95% CI: 1.25–6.36, P = 0.013) and higher COPD Assessment Test (CAT) scores (OR: 1.14, 95% CI: 1.03–1.25, P = 0.011) were associated with the increased risk for respiratory failure. For predicting respiratory failure, CAT scores and home oxygen therapy combined had an AUC-ROC of 0.73 (P < 0.0001). This clinical prediction model based on lymphocyte count may help to assist in treatment decisions for NIV in patients with AECOPD. Lower complement C3 seems to be associated with worse outcomes in patients with AECOPD. [ABSTRACT FROM AUTHOR]

Developing and validating a clinical prediction nomogram of enteral feeding intolerance (NOFI) in critically ill patients. So as to help clinicians implement pre-intervention for patients with high risk of enteral feeding intolerance (FI), formulate individualized feeding strategies, and reduce the probability of FI occurrence. From March 2018 to April 2023, patients who met the inclusion criteria but did not meet the exclusion criteria constituted the development cohort for retrospective analysis, and NOFI was developed. Patients recruited consecutively between May 2023 and July 2023 constituted the validation cohort for the prospective analysis for independent external validation of NOFI. Initially, a backward stepwise method was employed to conduct a multivariate logistic regression analysis in the development cohort, aiming to identify the optimal-fit model. Subsequently, a nomogram was derived from this model. The validation of the nomogram was carried out in an independent external validation cohort, where discrimination and calibration were evaluated. Additionally, a decision curve analysis was conducted to assess the net benefit of utilizing the nomogram for decision-making. A total of 628 and 143 patients, 49.0 % and 51.7 % of patients occurred FI, were included in the development and validation cohort, respectively. We developed a NOFI in severely ill patients and the primary diagnosis, Acute gastrointestinal injury (AGI) grade, and APACHE II score were independent predictors of FI, with the OR of the primary diagnosis of circulatory disease being 2.281 (95 % CI, 1.364–3.816; P = 0.002); The OR of respiratory diseases was 0.424 (95 % CI, 0.259–0.594; P = 0.001); The OR of AGI grade was 4.920 (95 % CI, 3.773–6.416; P < 0.001), OR of APACHE II score was 1.100 (95 % CI, 1.059–1.143; P < 0.001). Independent external validation of the prediction model was performed. This model has good discrimination and calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (full early enteral nutrition or delayed enteral nutrition). The prediction of enteral feeding intolerance can be conveniently facilitated by the NOFI that integrates primary diagnosis, AGI grade, and APACHE II score in critically ill patients. [ABSTRACT FROM AUTHOR]

PurposeThis study aimed to investigate the factors associated with pathologic node-negativity (ypN0) in patients who received neoadjuvant chemotherapy (NAC) to develop and validate an accurate prediction nomogram.MethodsThe CSBrS-012 study (2010–2020) included female patients with primary breast cancer treated with NAC followed by breast and axillary surgery in 20 hospitals across China. In the present study, 7,711 eligible patients were included, comprising 6,428 patients in the primary cohort from 15 hospitals and 1,283 patients in the external validation cohort from five hospitals. The hospitals were randomly assigned. The primary cohort was randomized at a 3:1 ratio and divided into a training set and an internal validation set. Univariate and multivariate logistic regression analyses were performed on the training set, after which a nomogram was constructed and validated both internally and externally.ResultsIn total, 3,560 patients (46.2%) achieved ypN0, and 1,558 patients (20.3%) achieved pathologic complete response in the breast (bpCR). A nomogram was constructed based on the clinical nodal stage before NAC (cN), ER, PR, HER2, Ki67, NAC treatment cycle, and bpCR, which were independently associated with ypN0. The area under the receiver operating characteristic curve (AUC) for the training set was 0.80. The internal and external validation demonstrated good discrimination, with AUCs of 0.79 and 0.76, respectively.ConclusionWe present a real-world study based on nationwide large-sample data that can be used to effectively screen for ypN0 to provide better advice for the management of residual axillary disease in breast cancer patients undergoing NAC.

Background Our goal was to create a simple risk‐prediction model for renal function decline after cardiac surgery to help focus renal follow‐up efforts on patients most likely to benefit. Methods and Results This single‐center retrospective cohort study enrolled 24 904 patients who underwent cardiac surgery from 2012 to 2019 at Fuwai Hospital, Beijing, China. An estimated glomerular filtration rate (eGFR) reduction of ≥30% 3 months after surgery was considered evidence of renal function decline. Relative to patients with eGFR 60 to 89 mL/min per 1.73 m2 (4.5% [531/11733]), those with eGFR ≥90 mL/min per 1.73 m2 (10.9% [1200/11042]) had a higher risk of renal function decline, whereas those with eGFR ≤59 mL/min per 1.73 m2 (5.8% [124/2129]) did not. Each eGFR stratum had a different strongest contributor to renal function decline: increased baseline eGFR levels for patients with eGFR ≥90 mL/min per 1.73 m2, transfusion of any blood type for patients with eGFR 60 to 89 mL/min per 1.73 m2, and no recovery of renal function at discharge for patients with eGFR ≤59 mL/min per 1.73 m2. Different nomograms were established for the different eGFR strata, which yielded a corrected C‐index value of 0.752 for eGFR ≥90 mL/min per 1.73 m2, 0.725 for eGFR 60–89 mL/min per 1.73 m2 and 0.791 for eGFR ≤59 mL/min per 1.73 m2. Conclusions Predictors of renal function decline over the follow‐up showed marked differences across the eGFR strata. The nomograms incorporated a small number of variables that are readily available in the routine cardiac surgical setting and can be used to predict renal function decline in patients stratified by baseline eGFR.

Abstract Background Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. Methods In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. Results The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70–0.86, P

Abstract Aims Left ventricular (LV) remodelling after acute myocardial infarction (AMI) is associated with heart failure and increased mortality. There was no consensus on the definition of LV remodelling, and the prognostic value of LV remodelling with different definitions has not been compared. We aimed to find the optimal definition and develop a prediction nomogram as well as online calculator that can identify patients at risk of LV remodelling. Methods and results This prospective, observational study included 829 AMI patients undergoing percutaneous coronary intervention from January 2015 to January 2020. Echocardiography was performed within the 48 h of admission and at 6 months after infarction to evaluate LV remodelling, defined as a 20% increase in LV end‐diastolic volume (LVEDV), a 15% increase in LV end‐systolic volume (LVESV), or LV ejection fraction (LVEF)

Abstract Background Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan‐cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk. Methods Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence‐based cancer classification system (BM‐CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM‐CCS and the nomogram, respectively. Results A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%–17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%–5.6%), and category C (1.2%; 95% CI: 1.1%–1.4%) (p

Summary: Background: Essential thrombocythemia (ET), a myeloproliferative neoplasm (MPN), has a substantial risk of evolving into post-essential thrombocythemia myelofibrosis (post-ET MF). This study aims to establish a prediction nomogram for early prediction of post-ET MF in ET patients. Methods: The training cohort comprised 558 patients from 8 haematology centres between January 1, 2010, and May 1, 2023, while the external validation cohort consisted of 165 patients from 6 additional haematology centres between January 1, 2010, and May 1, 2023. Univariable and multivariable Cox regression analysis was performed to identified independent risk factors and establish a nomogram to predict the post-ET MF free survival. Both bias-corrected area under the curve (AUC), calibration curves and concordance index (C-index) were employed to assess the predictive accuracy of the nomogram. Findings: Multivariate Cox regression demonstrated that elevated red blood cell distribution width (RDW), elevated levels of lactate dehydrogenase (LDH) and the level of haemoglobin (Hb), a history of smoking and the presence of splenomegaly were independent risk factors for post-ET MF. The C-index displayed of the training and validation cohorts were 0.877 and 0.853. The 5 years, 10 years AUC values in training and external validation cohorts were 0.948, 0.769 and 0.978, 0.804 respectively. Bias-corrected curve is close to the ideal curve and revealed a strong consistency between actual observation and prediction. Interpretation: We developed a nomogram capable of predicting the post-ET MF free survival probability at 5 years and 10 years in ET patients. This tool helps doctors identify patients who need close monitoring and appropriate counselling. Funding: This research was funded by the Key R&D Program of Zhejiang (No. 2022C03137); the Public Technology Application Research Program of Zhejiang, China (No. LGF21H080003); and the Zhejiang Medical Association Clinical Medical Research special fund project (No. 2022ZYC-D09).

Background: Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. Methods: In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. Results: The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70–0.86, P < 0.0001, sensitivity: 60.4%, specificity: 86.8%). The nomogram model, the C index, calibration plot, decision curve analysis, and clinical impact curve all indicate the model's good predictive performance. The observed decrease in the proportions of CD4+CD8+ T cells appears to be correlated with more unfavorable outcomes. Conclusions: The nomogram model, developed to forecast respiratory failure in patients with AECOPD, utilizing variables such as home oxygen therapy, CAT score, and CD4+CD8+ T cells%, demonstrated a high level of practicality in clinical settings. CD4+CD8+ T cells serve as a reliable and readily accessible predictor of AECOPD, exhibiting greater stability compared to other indices. It is less susceptible to subjective influences from patients or physicians. This model facilitated personalized estimations, enabling healthcare professionals to make informed decisions regarding preventive interventions. [ABSTRACT FROM AUTHOR]

Aims: Left ventricular (LV) remodelling after acute myocardial infarction (AMI) is associated with heart failure and increased mortality. There was no consensus on the definition of LV remodelling, and the prognostic value of LV remodelling with different definitions has not been compared. We aimed to find the optimal definition and develop a prediction nomogram as well as online calculator that can identify patients at risk of LV remodelling. Methods and results: This prospective, observational study included 829 AMI patients undergoing percutaneous coronary intervention from January 2015 to January 2020. Echocardiography was performed within the 48 h of admission and at 6 months after infarction to evaluate LV remodelling, defined as a 20% increase in LV end‐diastolic volume (LVEDV), a 15% increase in LV end‐systolic volume (LVESV), or LV ejection fraction (LVEF) < 50% at 6 months. The impact of LV remodelling on long‐term outcomes was analysed. Lasso regression was performed to screen potential predictors, and multivariable logistic regression analysis was conducted to establish the prediction nomogram. The area under the curve, calibration curve and decision curve analyses were used to determine the discrimination, calibration and clinical usefulness of the remodelling nomogram. The incidences of LV remodelling defined by LVEDV, LVESV and LVEF were 24.85% (n = 206), 28.71% (n = 238) and 14.60% (n = 121), respectively. Multivariable Cox regression models demonstrated that different definitions of LV remodelling were independently associated with the composite endpoint. However, only remodelling defined by LVEF was significantly connected with long‐term mortality (hazard ratio = 2.78, 95% confidence interval 1.41–5.48, P = 0.003). Seven variables were selected to construct the remodelling nomogram, including diastolic blood pressure, heart rate, AMI type, stent length, N‐terminal pro brain natriuretic peptide, troponin I, and glucose. The prediction model had an area under the receiver operating characteristics curve of 0.766. The calibration curve and decision curve analysis indicated consistency and better net benefit in the prediction model. Conclusions: LV remodelling defined by LVEDV, LVESV and LVEF were independent predictors for long‐term mortality or heart failure hospitalization in AMI patients after percutaneous coronary intervention. However, only remodelling defined by LVEF was suitable for predicting all‐cause death. In addition, the nomogram can provide an accurate and effective tool for the prediction of postinfarct remodelling. [ABSTRACT FROM AUTHOR]

Background and Aim: Postoperative complications are important clinical outcomes for colon cancer patients. This study aimed to investigate the predictive value of inflammatory–nutritional indicators combined with computed tomography body composition on postoperative complications in patients with stage II–III colon cancer. Methods: We retrospectively collected data from patients with stage II–III colon cancer admitted to our hospital from 2017 to 2021, including 198 patients in the training cohort and 50 patients in the validation cohort. Inflammatory–nutritional indicators and body composition were included in the univariate and multivariate analyses. Binary regression was used to develop a nomogram and evaluate its predictive value. Results: In the multivariate analysis, the monocyte–lymphocyte ratio (MLR), systemic immune‐inflammation index (SII), nutritional risk score (NRS), skeletal muscle index (SMI), and visceral fat index (VFI) were independent risk factors for postoperative complications of stage II–III colon cancer. In the training cohort, the area under the receiver operating characteristic curve of the predictive model was 0.825 (95% confidence interval [CI] 0.764–0.886). In the validation cohort, it was 0.901 (95% CI 0.816–0.986). The calibration curve showed that the prediction results were in good agreement with the observational results. Decision curve analysis showed that colon cancer patients could benefit from the predictive model. Conclusions: A nomogram combining MLR, SII, NRS, SMI, and VFI with good accuracy and reliability in predicting postoperative complications in patients with stage II–III colon cancer was established, which can help guide treatment decisions. [ABSTRACT FROM AUTHOR]

Purpose: The prediction of axillary lymph node status after neoadjuvant chemotherapy (NAC) becoming critical because of the advocation of the de-escalation of axillary management. We investigate associated factors of axillary upstaging in clinical node-negative (cN0) breast cancer patients receiving NAC to develop and validate an accurate prediction nomogram. Methods: We retrospectively analyzed 1892 breast cancer patients with stage of cT1-3N0 treated by NAC and subsequent surgery between 2010 and 2020 in twenty hospitals across China. Patients randomly divided into a training set and validation set (3:1). Univariate and multivariate logistic regression analysis were performed, after which a nomogram was constructed and validated. Results: In total, pathologic node negativity (ypN0) achieved in 1406 (74.3%) patients and another 486 (25.7%) patients upstaged to pathologic node positive (ypN+). Breast pathologic complete response (bpCR) was achieved in 445 (23.5%) patients and non-bpCR in 1447 (76.5%) patients. A nomogram was established by ER, tumor histology, HER2 status, cycle of NAC treatment, and the bpCR, which were confirmed by multivariate logistic analysis as independent predictors of nodal upstaging in the training cohort (n = 1419). The area under the receiver operating characteristic curve (AUC) of the training cohort and validation cohort (n = 473) were 0.73 (95% CI 0.693–0.751) and 0.77 (95% CI 0.723–0.812) respectively. Conclusion: We present a nomogram with a nationwide large sample data which can effectively predict axillary upstaging after neoadjuvant chemotherapy to give better advice for individualized axillary lymph node management of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract Background To develop and validate a predictive nomogram for tumor residue 3–6 months after treatment based on postradiotherapy plasma Epstein–Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II–IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). Methods In this retrospective study, 1050 eligible patients with stage II–IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3–6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). Results Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P

Abstract Background The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI) . Methods This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell’s C-index and decision curve analysis (DCA). Results In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell’s C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721–0.823) in the development cohort and 0.736 (95%CI: 0.656–0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. Conclusions Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI.

Edy Suyanto,1 Ibnu Fajar,2 Tanto Hariyanto3 1Nursing Department, Poltekkes Kemenkes Malang, Malang, Indonesia; 2Nutrition Department, Poltekkes Kemenkes Malang, Malang, Indonesia; 3Blood Bank Technology Department, Poltekkes Kemenkes Malang, Malang, IndonesiaCorrespondence: Edy Suyanto, Poltekkes Kemenkes Malang, Jl. Ijen 77C, Malang, Indonesia, Email edy_suyanto@poltekkes-malang.ac.id

Key points The MRI-based nomogram could predict the SDS occurrence in patients with SOC. The relationship between the sigmoid colon/rectum and ovarian mass accounts is critical. A preoperative nomogram provides gynecologists with an accurate and effective tool.

Background: To develop and validate a predictive nomogram for tumor residue 3–6 months after treatment based on postradiotherapy plasma Epstein–Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II–IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). Methods: In this retrospective study, 1050 eligible patients with stage II–IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3–6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). Results: Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P<0.001). A prediction nomogram based on postradiotherapy plasma EBV DNA level (0 vs. 1–499 vs. ≥500 copies/ml), clinical stage (II vs. III vs. IVA), and RT dose (68.00–69.96 vs. 70.00–74.00 Gy) estimated the probability of residue development. The nomogram showed better discrimination (area under the curve (AUC): 0.752) than either the clinical stage (0.659) or postradiotherapy EBV DNA level (0.627) alone in the development and validation cohorts (AUC: 0.728). Conclusions: We developed and validated a nomogram model integrating clinical characteristics at the end of IMRT for predicting whether tumor will residue or not after 3–6 months. Thus, high-risk NPC patients who might benefit from immediate additional intervention could be identified by the model, and the probability of residue can be reduced in the future. [ABSTRACT FROM AUTHOR]

Background: The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI). Methods: This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell's C-index and decision curve analysis (DCA). Results: In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell's C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721–0.823) in the development cohort and 0.736 (95%CI: 0.656–0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. Conclusions: Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI. [ABSTRACT FROM AUTHOR]

Background: A different treatment was used when peritoneal metastases (PM) occurred in patients with gastric cancer (GC). Certain cancers' peritoneal metastasis could be predicted by the cardiophrenic angle lymph node (CALN). This study aimed to establish a predictive model for PM of gastric cancer based on the CALN. Methods: Our center retrospectively analyzed all GC patients between January 2017 and October 2019. Pre-surgery computed tomography (CT) scans were performed on all patients. The clinicopathological and CALN features were recorded. PM risk factors were identified via univariate and multivariate logistic regression analyses. The receiver operator characteristic (ROC) curves were generated using these CALN values. Using the calibration plot, the model fit was assessed. A decision curve analysis (DCA) was conducted to assess the clinical utility. Results: 126 of 483 (26.1%) patients were confirmed as having peritoneal metastasis. These relevant factors were associated with PM: age, sex, T stage, N stage, enlarged retroperitoneal lymph nodes (ERLN), CALN, the long diameter of the largest CALN (LD of LCALN), the short diameter of the largest CALN (SD of LCALN), and the number of CALNs (N of CALNs). The multivariate analysis illustrated that the LD of LCALN (OR = 2.752, p < 0.001) was PM's independent risk factor in GC patients. The area under the curve (AUC) of the model was 0.907 (95% CI 0.872–0.941), demonstrating good performance in the predictive value of PM. There is excellent calibration evident from the calibration plot, which is close to the diagonal. The DCA was presented for the nomogram. Conclusion: CALN could predict gastric cancer peritoneal metastasis. The model in this study provided a powerful predictive tool for determining PM in GC patients and helping clinicians allocate treatment. [ABSTRACT FROM AUTHOR]

Background: Serous ovarian carcinoma (SOC) has the highest morbidity and mortality among ovarian carcinoma. Accurate identification of the probability of suboptimal debulking surgery (SDS) is critical. This study aimed to develop a preoperative prediction nomogram of SDS for patients with SOC. Methods: A prediction model was established including 205 patients of SOC from institution A, and 45 patients from institution B were enrolled for external validation. Multivariate logistic regression was used to screen independent predictors and establish a nomogram to predict the occurrence of SDS. Results: Multivariate logistic regression demonstrated that the CA-125 level (odds ratio [OR] 8.260, 95% confidence interval [CI] 2.003–43.372), relationship between the sigmoid colon/rectum and ovarian mass (OR 28.701, 95% CI 4.561–286.070), diaphragmatic metastasis (OR 12.369, 95% CI 1.675–274.063), and FIGO stage (OR 32.990, 95% CI 6.623–274.509) were independent predictors for SDS. The area under the curve, concordance index, and 95% CI of the nomogram constructed from the above four factors were 0.951, 0.934, and 0.919–0.982, respectively. The model showed a good fit by the Hosmer–Lemeshow test (training set, p = 0.2475; internal validation set, p = 0.2355; external validation set, p = 0.2707). The external validation proved the reliability of the prediction nomogram. The calibration curve was close to the ideal diagonal line. The decision curve analysis demonstrated a significantly better net benefit. The clinical impact curve indicated good effectiveness in clinical application. Conclusion: A prediction nomogram for SDS in patients with SOC provides gynecologists with an accurate and effective tool for appropriate management. Key points: The MRI-based nomogram could predict the SDS occurrence in patients with SOC. The relationship between the sigmoid colon/rectum and ovarian mass accounts is critical. A preoperative nomogram provides gynecologists with an accurate and effective tool. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to investigate the role of human chorionic gonadotropin (hCG) daily variations and levels prior to methotrexate treatment as predictors for treatment outcome. Methods: This retrospective study included patients who had a sonographically confirmed ectopic pregnancy at the International Peace Maternity and Child Health Hospital between November 2015 and June 2020. The associations of hCG levels and daily variations with the treatment success were evaluated by multivariable logistic regression and receiver operator characteristic (ROC) curve. Establish a nomogram that predicts how methotrexate (MTX) therapy will turn out. The performance of the model was assessed utilizing concordance index, receiver operating characteristic curves, and calibration plots. Results: The median serum hCG levels before treatment and hCG daily variation in the failure group were higher than those in the success group (487.8 vs. 270.7 IU/L, −1.86% vs. 7.29%, both p < 0.01). According to the ROC curve analysis, the cutoff values of serum hCG level before treatment and daily variations were 617.35 IU/L and 1.76%/day. By multivariable logistic regression analysis, serum hCG levels before treatment (odds ratio [OR]: 1.001, 95% confidence interval [CI]: 1.000 ~ 1.001) and hCG daily variations were independently associated with the treatment success (OR: 1.033, 95% CI: 1.015 ~ 1.052). The nomogram was effective at predicting the outcome of MTX treatment with a receiver operating characteristic area under the curve of 0.717 (p < 0.001). The nomogram's calibration curve was almost parallel to the ideal diagonal line. Conclusion: We successfully created a nomogram based on serum hCG levels before treatment and hCG daily changes to anticipate the result of MTX therapy, which could assist medical professionals in selecting therapeutic schedule for patients with tubal pregnancies. [ABSTRACT FROM AUTHOR]

Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3). Based on the preoperative clinical, US and contrast-enhanced CT features, univariate and multivariate logistic regression analyses were used to determine the independent predictive factors of CLNM, and a personalized nomogram was constructed. Calibration curve, receiver operating characteristic (ROC) curve and decision curve analyses were used to assess discrimination, calibration and clinical application of the prediction model. As a result, 38.9% (306/786) of patients with PTC and CLNM(−) status before surgery had confirmed CLNM using postoperative pathology. In multivariate analysis, a young age (≤45 years), the male sex, no presence of Hashimoto thyroiditis, isthmic location, microcalcification, inhomogeneous enhancement and capsule invasion were independent predictors of CLNM in patients with PTC. The nomogram integrating these 7 factors exhibited strong discrimination in both the training group [Area under the curve (AUC)=0.826] and the validation group (AUC=0.818). Furthermore, the area under the ROC curve for predicting CLNM based on clinical, US and contrast-enhanced CT features was higher than that without contrast-enhanced CT features (AUC=0.818 and AUC=0.712, respectively). In addition, the calibration curve was appropriately fitted and decision curve analysis confirmed the clinical utility of the nomogram. In conclusion, the present study developed a novel nomogram for preoperative prediction of CLNM, which could provide a basis for prophylactic central lymph node dissection in patients with PTC. [ABSTRACT FROM AUTHOR]

Background/aim: To develop an accurate method to predict nodal pathological complete response (ypN0) in patients after neoadjuvant chemotherapy (NAC) for clinically node-positive breast cancer., Patients and Methods: We included 128 patients with clinically node-positive primary breast cancer who underwent axillary lymph node dissection after NAC., Results: Breast primary tumor clinical complete response (ycT0) was observed in 29.7% and nodal clinical complete response (ycN0) in 44.5% of cases. When ycN0 was predicted as ypN0, the negative predictive value was 77.2%, and the false-negative rate was 19.7%. Estrogen receptor status, ycT0, and ycN0 were independent predictive factors for ypN0 after NAC in patients with clinically node-positive breast cancer. These factors were used to develop a nomogram for ypN0 prediction. The following points were added: 82 in case of estrogen receptor-negative, 56 in case of ycT0, and 100 in case of ycN0. Score summation was used to prognosticate the manifestation of ypN0. Our nomogram predicted ypN0 with a negative predictive value of 92.9% and false-negative rate of 4.5%, demonstrating an approximate 15% improvement over ypN0 prediction using ycN0 alone., Conclusion: Estrogen receptor-negativity, ycT0, and ycN0 are independent predictive factors for ypN0 after NAC in clinically node-positive breast cancer. The nomogram may improve individualized axillary treatment., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Neonatal respiratory failure (NRF) is a critical condition with high morbidity and mortality rates. This study aimed to develop a nomogram prediction model to early predict the risk of death in Chinese neonates with NRF., Methods: A retrospective analysis was conducted on NRF neonates from 21 tertiary neonatal intensive care units (NICUs) across 13 prefecture-level cities in Jiangsu Province, China, from March 2019 to March 2022. NRF neonates from one random NICU were selected as the external validation set, while those from the remaining 20 NICUs were divided into the training set and the internal validation set at a 7:3 ratio. Death was the primary outcome. LASSO regression and multivariate logistic regression were used to identify the predictive factors from the training set and then the nomogram was constructed., Results: A total of 5387 neonates with NRF were included in the analysis. Among them, 3444 were in the training set, 1470 were in the internal validation set, and 473 were in the external validation set. The nomogram was constructed based on the eight predictors of the 1-min Apgar score, birth weight, gestational age, the relationship between birth weight and gestational age, mode of first respiratory support, inhaled nitric oxide, antenatal corticosteroids, and vasoactive drugs. The area under the curve of the nomogram in the training set, internal validation set, and external validation set was 0.763, 0.733, and 0.891, respectively. The P-values of the Hosmer-Lemeshow goodness of fit test were 0.638, 0.273, and 0.253, respectively. Brier scores were 0.066, 0.072, and 0.037, respectively. The decision curve analysis demonstrated a significant net benefit in all cases. These data indicate the good performance of the nomogram., Conclusions: This nomogram can serve as a reference for clinicians to identify high-risk neonates early and reduce the incidence of neonatal mortality., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Rui Cheng reports financial support was provided by 10.13039/501100007289Nanjing Medical University Special Disease Cohort Foundation. Rui Cheng reports financial support was provided by 10.13039/501100001809National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

BackgroundDisruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS.MethodsACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis.ResultsThere were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702–0.877) in the development cohort and 0.894 (95% CI: 0.820–0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739–0.866) in the development cohort and 0.798 (95% CI: 0.693–0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs.ConclusionOur validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.

Introduction: The dosimetric factors of radiotherapy have an acute impact on the host immune system during chemoradiotherapy (CRT) in locally advanced non-small cell lung cancer (NSCLC). However, even after CRT, a substantial number of patients remain immunosuppressed with delayed lymphopenia. Therefore, we aimed to evaluate clinical and dose-volumetric predictors of delayed lymphopenia after CRT in locally advanced NSCLC. Materials and methods: We retrospectively reviewed 272 patients with locally advanced NSCLC who received definitive CRT from January 2012 to August 2020. Differential blood count data, including serum albumin values, were obtained at baseline, during and at first follow up after CRT. Acute and delayed lymphopenia events were defined as grade III/IV lymphopenia developed during or 4-12 weeks after CRT completion, which accounted for 84% and 10% of cases, respectively. Dose-volume histogram parameters for planned target volume, whole body, heart, lung, great vessels, spleen, esophagus and thoracic vertebral bodies were evaluated. Results: Multivariate analysis revealed that patients with delayed lymphopenia were associated with inferior overall survival (HR 2.53, P = 0.001) and progression-free survival (HR 1.98, P = 0.006). However, there was no significant survival difference between groups stratified by acute lymphopenia. On multivariable logistic regression models, lung V5, baseline ALC, during-CRT ALC, and albumin nadir were significant predictors for delayed lymphopenia. Furthermore, the nomogram for delayed lymphopenia based on these variables had good discrimination (area under the curve, 0.905). Conclusions: In this study, we investigated the prognostic significance of delayed lymphopenia and identified clinico-dosimetric parameters to predict delayed lymphopenia. [ABSTRACT FROM AUTHOR]

Background: Peritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients. Methods: In our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people's hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots. Results: Univariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p<0.0006), depth of invasion (OR=14.55, p<0.0001), lymph node metastases (OR=5.89, p<0.0001), carcinoembryonic antigen (CEA) (OR=2.50, p<0.0001), CA125 (OR=11.46, p<0.0001), CA72-4 (OR=4.09, p<0.0001), CA19-9 (OR=2.74, p<0.0001), CA50 (OR=5.20, p<0.0001) and CA242 (OR=3.83, p<0.0001). Multivariate analysis revealed that clinical invasion depth and serum marker of CA125 and CA72-4 were independent risk factors for PD. The prediction model was established based on the risk factors using the R program. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.931 (95% CI: 0.900–0.960), with the accuracy, sensitivity and specificity values of 90.5%, 86.2% and 82.2%, respectively. Conclusion: The nomogram model constructed using CA125, CA72-4 and depth of invasion increases the accuracy and sensitivity in predicting the incidence of PD in GC patients and can be used as an important tool for preoperative diagnosis. [ABSTRACT FROM AUTHOR]

Summary: Background: Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS. Methods: A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples. Findings: Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324–0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133–0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083–0·909), hospital level (OR 2·479, 95% CI 1·260–4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563–0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer–Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. Interpretation: A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS. Funding: No funding was associated with this study.

BackgroundPeritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients.MethodsIn our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people’s hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots.ResultsUnivariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p

• For the first time, established a predictive model to anticipate the cytokine storms in the acute phase of SFTS patients. • By visualizing the predictive model through a nomogram, it becomes more convenient and user-friendly. • The predictive capacity of the model was evaluated based on the outcomes of 48 cytokines in the validation cohort. Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease known for its high mortality rate and its correlation with Cytokine Storms (CS). Timely detection of CS is crucial for improving the prognosis of the disease. The objective of this investigation was to develop a model for identifying cytokine storms in the acute phase of SFTS. A total of 245 patients diagnosed with SFTS were included in this study between January 2020 and July 2022. Among them, 184 patients were part of the training set, while 61 patients were part of the validation set. Variables identified by LASSO were subsequently included in a multivariate logistic regression analysis to determine independent predictors. Subsequently, a nomogram was then developed to predict the likelihood of CS in SFTS patients. The predictive efficacy and clinical applicability of the nomogram model were further assessed through ROC analysis and the DCA curve. Following LASSO analysis, a total of 11 indicators were included in multivariate logistic regression analysis. The findings indicated that PLT (OR 0.865, P < 0.001), LDH (OR 1.002, P < 0.001), Na+ (OR 1.155, P = 0.005), and ALT (OR 1.019, P < 0.001) serve as independently predictors of CS in the acute phase of SFTS. Furthermore, a nomogram named the PLNA was constructed by integrating these four factors. The PLNA model exhibited favorable predictive accuracy with an AUC of 0.958. Moreover, the PLNA model exhibited excellent clinical applicability in both the training and validation sets, as evidenced by the DCA curve. The PLNA model, constructed using clinical indicators, can predict the probability of cytokine storm in the acute phase of SFTS patients. [ABSTRACT FROM AUTHOR]

Aims: urodynamic diagnosis of dysfunctional voiding/external‐sphincter nonrelaxation (DV/EUSD) needs assistance of specialized testing namely urethral pressure profilometry (UPP), electromyography (EMG), and/or videofluoroscopy (VUDS). We aimed to find a predictive model based on standard pressure‐flow study without need for specialized testing. Material and Methods: In this retrospective study (2017–2021), clinical and urodynamic data of adult men and women presenting with voiding dysfunction was collected. Mandatory inclusion criteria were availability of all—(1) findings of clinical examination and neurological status, (2) a valid filling cystometry and pressure‐flow study (with active detrusor contraction), (3) a final clinic‐urodynamic diagnosis. Voiding cystourethrography (VCUG) was performed to confirm the location of obstruction. Results: Data of 218 participants (178♂, 40♀) was eligible. Plateau detrusor contraction pattern was observed in 89.0% of men and 86% of women with DV/EUSD; whereas only 7.5% men and no women with other obstructions demonstrated this pattern. Forward likelihood Logistic regression analysis revealed presence of plateau pattern, lower bladder outlet obstruction index (BOOI), and smaller difference between Pdetmax and PdetQmax highly predictive of presence of DV/EUSD in men as per the following equation—Y = −9.900 + (0.085 × BOOI) + (0.123 × pdetmax – pdetQmax) + (4.061 × detrusor pattern). A kattan‐type nomogram was constructed based on the above equation. In women, presence of plateau pattern alone was highly predictive of DV/EUSD. Conclusion: Diagnosis of DV/EUSD can be accurately predicted using parameters of three‐channel urodynamics (plateau pattern, BOOI, Pdetmax—pdetQmax) minimizing need for specialized testing. [ABSTRACT FROM AUTHOR]

Background We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT). Methods A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD). Results Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients’ BMI (OR 1.10, 95% CI 1.04–1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22–4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95–0.97, p 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p

We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT). A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD). Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients' BMI (OR 1.10, 95% CI 1.04–1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22–4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95–0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03–1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05). With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT. [ABSTRACT FROM AUTHOR]