OBJECTIVE: This study was performed to construct and validate a risk prediction model for non-invasive ventilation (NIV) failure after birth in premature infants with gestational age < 32 weeks.
METHODS: The data were derived from the multicenter retrospective study program - Jiangsu Provincial Neonatal Respiratory Failure Collaboration Network from Jan 2019 to Dec 2021. The subjects finally included were preterm infants using NIV after birth with gestational age less than 32 weeks and admission age within 72 h. After screening by inclusion and exclusion criteria, 1436 babies were subsequently recruited in the study, including 1235 infants in the successful NIV group and 201 infants in the failed NIV group.
RESULTS: (1) Gestational age, 5 min Apgar, Max FiO2 during NIV, and FiO2 fluctuation value during NIV were selected by univariate and multivariate analysis. (2) The area under the curve of the prediction model was 0.807 (95% CI: 0.767-0.847) in the training set and 0.825 (95% CI: 0.766-0.883) in the test set. The calibration curve showed good agreement between the predicted probability and the actual observed probability (Mean absolute error = 0.008 for the training set; Mean absolute error = 0.012 for the test set). Decision curve analysis showed good clinical validity of the risk model in the training and test cohorts.
CONCLUSIONS: This model performed well on dimensions of discrimination, calibration, and clinical validity. This model can serve as a useful tool for neonatologists to predict whether premature infants will experience NIV failure after birth.

OBJECTIVE: Non-obstructive azoospermia (NOA) is a severe and common cause of male infertility. Currently, the most reliable predictor of sperm retrieval success in NOA is histopathology, but preoperative testicular biopsy often increases the difficulty of sperm retrieval surgery. This study aims to explore the characteristics of N6-methyladenosine (m6A) modification in NOA patients and investigate the potential biomarkers and molecular mechanisms for pathological diagnosis and treatment of NOA using m6A-related genes.
METHODS: NOA-related datasets were downloaded from the GEO database. Based on the results of LASSO regression analysis, a prediction model was established from differentially expressed m6A-related genes, and the predictive performance of the model was evaluated using ROC curves. Cluster analysis was performed based on differentially expressed m6A-related genes to evaluate the differences in different m6A modification patterns in terms of differentially expressed genes (DEGs), biological features, and immune features.
RESULTS: There were significant differences in eight m6A-related genes between NOA samples and healthy controls. The ROC curves showed excellent predictive performance for the diagnostic models constructed with ALKBH5 and FTO. DEGs of two m6A modification subtypes indicated the influence of m6A-related genes in the biological processes of mitosis and meiosis in NOA patients, and there were significant immune differences between the two subtypes.
CONCLUSIONS: The NOA pathological diagnostic models constructed with FTO and ALKBH5 have good predictive ability. We have identified two different m6A modification subtypes, which may help predict sperm retrieval success rate and treatment selection in NOA patients.

BACKGROUND: For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.
METHODS: A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.
RESULTS: In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.
CONCLUSIONS: Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.
BACKGROUND: This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

This study aimed to develop and internally validate a nomogram model for assessing the risk of intraoperative hypothermia in patients undergoing video-assisted thoracoscopic (VATS) lobectomy. This study is a retrospective study. A total of 530 patients who undergoing VATS lobectomy from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into hypothermia group (n = 346) and non-hypothermia group (n = 184) according to whether hypothermia occurred during the operation. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of hypothermia during operation, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Calibration curve and Hosmer Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Intraoperative hypothermia occurred in 346 of 530 patients undergoing VATS lobectomy (65.28%). Logistic regression analysis showed that age, serum total bilirubin, inhaled desflurane, anesthesia duration, intraoperative infusion volume, intraoperative blood loss and body mass index were risk factors for intraoperative hypothermia in patients undergoing VATS lobectomy (P < 0.05). The area under ROC curve was 0.757, 95% CI (0.714-0.799). The optimal cutoff value was 0.635, the sensitivity was 0.717, and the specificity was 0.658. These results suggested that the model was well discriminated. Calibration curve has shown that the actual values are generally in agreement with the predicted values. Hosmer-Lemeshow test showed that χ2 = 5.588, P = 0.693, indicating that the model has a good accuracy. The DCA results confirmed that the model had high clinical utility. The nomogram model constructed in this study showed good discrimination, accuracy and clinical utility in predicting patients with intraoperative hypothermia, which can provide reference for medical staff to screen high-risk of intraoperative hypothermia in patients undergoing VATS lobectomy.

UNASSIGNED: To analyze the risk factors of delirium in elderly patients after hip arthroplasty and to construct a prediction model.
UNASSIGNED: Clinical data of 248 elderly patients who underwent hip arthroplasty in the Department of Traumatology and Orthopedics at Wuhan Fourth Hospital were retrospectively collected from November 2021 to February 2023. Logistic regression analysis was used to identify the risk factors of delirium after hip arthroplasty, and a nomogram prediction model was constructed using the RMS package of R4.1.2 software. The accuracy and stability of the model was evaluated based on the Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic (ROC) curve.
UNASSIGNED: Age, nighttime sleep, anesthesia method, intraoperative blood loss, hypoxemia, and C-reactive protein (CRP) level were all risk factors of delirium after the hip arthroplasty (P<0.05). These factors were used to construct a nomogram prediction model that was internally validated using the Bootstrap method. The prediction model had the area under ROC curve (AUC) of 0.980 (95% CI: 0.964-0.996), indicating that it has certain predictive value for postoperative delirium. When the optimal cut off value was selected, the sensitivity and specificity were 92.7% and 92.3%, respectively, indicating that the prediction model is effective.
UNASSIGNED: Age, short nighttime sleep, general anesthesia, high intraoperative blood loss, hypoxemia, and high CRP levels are independent risk factors for delirium after hip arthroplasty. The nomogram prediction model constructed based on these risk factors can effectively predict delirium in elderly patients after hip arthroplasty.

BACKGROUND: This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.
METHODS: We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).
RESULTS: The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).
CONCLUSIONS: The prediction model based on radiomics and genomics features has a good prediction performance for STAS.

OBJECTIVE: Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynamic prediction model to predict post-operative blood transfusion requirements. This will provide more accurate guidance for perioperative humoral management and rational allocation of medical resources.
METHODS: We retrospectively analyzed data from 829 patients who underwent total hip arthroplasty for femoral neck fractures at three third-class hospitals between January 2017 and August 2023. Patient data from one hospital were used for model development, whereas data from the other two hospitals were used for external validation. Logistic regression analysis was used to screen the characteristic subsets related to blood transfusion. Various machine learning algorithms, including logistic regression, SVA (support vector machine), K-NN (k-nearest neighbors), MLP (multilayer perceptron), naive Bayes, decision tree, random forest, and gradient boosting, were used to process the data and construct prediction models. A 10-fold cross-validation algorithm facilitated the comparison of the predictive performance of the models, resulting in the selection of the best-performing model for the development of an open-source computing program.
RESULTS: BMI (body mass index), surgical duration, IBL (intraoperative blood loss), anticoagulant history, utilization rate of tranexamic acid, Pre-Hb, and Pre-ALB were included in the model as well as independent risk factors. The average area under curve (AUC) values for each model were as follows: logistic regression (0.98); SVA (0.91); k-NN (0.87) MLP, (0.96); naive Bayes (0.97); decision tree (0.87); random forest (0.96); and gradient boosting (0.97). A web calculator based on the best model is available at: (https://nomo99.shinyapps.io/dynnomapp/).
CONCLUSIONS: Utilizing a computer algorithm, a prediction model with a high discrimination accuracy (AUC > 0.5) was developed. The logistic regression model demonstrated superior differentiation and reliability, thereby successfully passing external validation. The model\'s strong generalizability and applicability have significant implications for clinicians, aiding in the identification of patients at high risk for postoperative blood transfusion.

UNASSIGNED: Gallstone disease (GSD) is one of the common digestive tract diseases with a high worldwide prevalence. The effects of GSD on patients include but are not limited to the symptoms of nausea, vomiting, and biliary colic directly caused by GSD. In addition, there is mounting evidence from cohort studies connecting GSD to other conditions, such as cardiovascular diseases, biliary tract cancer, and colorectal cancer. Early identification of patients at a high risk of GSD may help improve the prevention and control of the disease. A series of studies have attempted to establish prediction models for GSD, but these models could not be fully applied in the general population due to incomplete prediction factors, small sample sizes, and limitations in external validation. It is crucial to design a universally applicable GSD risk prediction model for the general population and to take individualized intervention measures to prevent the occurrence of GSD. This study aims to conduct a multicenter investigation involving more than 90000 people to construct and validate a complete and simplified GSD risk prediction model.
UNASSIGNED: A total of 123634 participants were included in the study between January 2015 and December 2020, of whom 43929 were from the First Affiliated Hospital of Chongqing Medical University (Chongqing, China), 11907 were from the First People\'s Hospital of Jining City (Shandong, China), 1538 were from the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China), and 66260 were from the People\'s Hospital of Kaizhou District (Chongqing, China). After excluding patients with incomplete clinical medical data, 35976 patients from the First Affiliated Hospital of Chongqing Medical University were divided into a training data set (n=28781, 80%) and a validation data set (n=7195, 20%). Logistic regression analyses were performed to investigate the relevant risk factors of GSD, and a complete risk prediction model was constructed. Factors with high scores, mainly according to the nomograms of the complete model, were retained to simplify the model. In the validation data set, the diagnostic accuracy and clinical performance of these models were validated using the calibration curve, area under the curve (AUC) of the receiver operating characteristic curve, and decision curve analysis (DCA). Moreover, the diagnostic accuracy of these two models was validated in three other hospitals. Finally, we established an online website for using the prediction model (The complete model is accessible at https://wenqianyu.shinyapps.io/Completemodel/, while the simplified model is accessible at https://wenqianyu.shinyapps.io/Simplified/).
UNASSIGNED: After excluding patients with incomplete clinical medical data, a total of 96426 participants were finally included in this study (35876 from the First Affiliated Hospital of the Chongqing Medical University, 9289 from the First People\'s Hospital of Jining City, 1522 from the Tianjin Medical University Cancer Institute, and 49639 from the People\'s Hospital of Kaizhou District). Female sex, advanced age, higher body mass index, fasting plasma glucose, uric acid, total bilirubin, gamma-glutamyl transpeptidase, and fatty liver disease were positively associated with risks for GSD. Furthermore, gallbladder polyps, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and aspartate aminotransferase were negatively correlated to risks for GSD. According to the nomograms of the complete model, a simplified model including sex, age, body mass index, gallbladder polyps, and fatty liver disease was constructed. All the calibration curves exhibited good consistency between the predicted and observed probabilities. In addition, DCA indicated that both the complete model and the simplified model showed better net benefits than treat-all and treat-none. Based on the calibration plots, DCA, and AUCs of the complete model (AUC in the internal validation data set=74.1% [95% CI: 72.9%-75.3%], AUC in Shandong=71.7% [95% CI: 70.6%-72.8%], AUC in Tianjin=75.3% [95% CI: 72.7%-77.9%], and AUC in Kaizhou=72.9% [95% CI: 72.5%-73.3%]) and the simplified model (AUC in the internal validation data set=73.7% [95% CI: 72.5%-75.0%], AUC in Shandong=71.5% [95% CI: 70.4%-72.5%], AUC in Tianjin=75.4% [95% CI: 72.9%-78.0%], and AUC in Kaizhou=72.4% [95% CI: 72.0%-72.8%]), we concluded that the complete and simplified risk prediction models for GSD exhibited excellent performance. Moreover, we detected no significant differences between the performance of the two models (P>0.05). We also established two online websites based on the results of this study for GSD risk prediction.
UNASSIGNED: This study innovatively used the data from 96426 patients from four hospitals to establish a GSD risk prediction model and to perform risk prediction analyses of internal and external validation data sets in four cohorts. A simplified model of GSD risk prediction, which included the variables of sex, age, body mass index, gallbladder polyps, and fatty liver disease, also exhibited good discrimination and clinical performance. Nonetheless, further studies are needed to explore the role of low-density lipoprotein cholesterol and aspartate aminotransferase in gallstone formation. Although the validation results of the complete model were better than those of the simplified model to a certain extent, the difference was not significant even in large samples. Compared with the complete model, the simplified model uses fewer variables and yields similar prediction and clinical impact. Hence, we recommend the application of the simplified model to improve the efficiency of screening high-risk groups in practice. The use of the simplified model is conducive to enhancing the self-awareness of prevention and control in the general population and early intervention for GSD.

BACKGROUND: Delayed union, malunion, and nonunion are serious complications in the healing of fractures. Predicting the risk of nonunion before or after surgery is challenging.
OBJECTIVE: To compare the most prevalent predictive scores of nonunion used in clinical practice to determine the most accurate score for predicting nonunion.
METHODS: We collected data from patients with tibial shaft fractures undergoing surgery from January 2016 to December 2020 in three different trauma hospitals. In this retrospective multicenter study, we considered only fractures treated with intramedullary nailing. We calculated the tibia FRACTure prediction healING days (FRACTING) score, Nonunion Risk Determination score, and Leeds-Genoa Nonunion Index (LEG-NUI) score at the time of definitive fixation.
RESULTS: Of the 130 patients enrolled, 89 (68.4%) healed within 9 months and were classified as union. The remaining patients (n = 41, 31.5%) healed after more than 9 months or underwent other surgical procedures and were classified as nonunion. After calculation of the three scores, LEG-NUI and FRACTING were the most accurate at predicting healing.
CONCLUSIONS: LEG-NUI and FRACTING showed the best performances by accurately predicting union and nonunion.

BACKGROUND: PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2.
METHODS: Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant.
RESULTS: A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by -2.0% for the entire cohort, -2.9% for ER-positive and -0.0% for ER-negative patients, compared to -7.3%, -4.7% and -13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%.
CONCLUSIONS: PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis.