Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.
Objective To systematically evaluate risk prediction models for acute exacerbation of chronic obstructive pulmonary disease (COPD), and provide a reference for early clinical identification. Methods The literature on the risk prediction models of acute exacerbation of COPD published by CNKI, VIP, Cochrane, Embase and Web of Science database was searched in Chinese and English from inception to April 2022, and relevant studies were collected on the development of risk prediction models for acute exacerbations of COPD. After independent screening of the literature and extraction of information by two independent researchers, the quality of the included literature was evaluated using the PROBASTA tool. Results Five prospective studies, one retrospective case-control study and seven retrospective cohort studies were included, totally 13 papers containing 24 models. Twelve studies (92.3%) reported the area under the receiver operator characteristic curve ranging 0.66 to 0.969. Only five studies reported calibrated statistics, and three studies were internally and externally validated. The overall applicability of 13 studies was good, but there was a high risk of bias, mainly in the area of analysis. Conclusions The existing predictive risk models for acute exacerbations of COPD are unsatisfactory, with wide variation in model performance, inappropriate and incomplete inclusion of predictors, and a need for better ways to develop and validate high-quality predictive models. Future research should refine the study design and study report, and continue to update and validate existing models. Secondly medical staff should develop and implement risk stratification strategies for acute exacerbations of COPD based on predicted risk classification results in order to reduce the frequency of acute exacerbations and to facilitate the rational allocation of medical resources.
Objective To construct a risk prediction score model for serious adverse event (SAE) after cardiac catheterization in patients with adult congenital heart disease (ACHD) and pulmonary hypertension (PH) and verify its predictive effect. Methods The patients with PH who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected. The patients were randomly divided into a model group and a validation group according to the order of admission. The model group was divided into a SAE group and a non-SAE group according to whether SAE occurred after the catheterization. The data of the two groups were compared, and the risk prediction score model was established according to the results of multivariate logistic regression analysis. The discrimination and calibration of the model were evaluated using the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test, respectively. Results A total of 758 patients were enrolled, including 240 (31.7%) males and 518 (68.3%) females, with a mean age of 43.1 (18.0-81.0) years. There were 530 patients in the model group (47 patients in the SAE group and 483 patients in the non-SAE group) and 228 patients in the validation group. Univariate analysis showed statistical differences in age, smoking history, valvular disease history, heart failure history, N-terminal pro-B-type natriuretic peptide, and other factors between the SAE and non-SAE groups (P<0.05). Multivariate analysis showed that age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, surgical general anesthesia, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients (P<0.05). The risk prediction score model had a total score of 0-139 points and patients who had a score>50 points were high-risk patients. Model validation results showed an area under the ROC curve of 0.937 (95%CI 0.897-0.976). Hosmer-Lemeshow goodness-of-fit test: χ2=3.847, P=0.797. Conclusion Age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, general anesthesia for surgery, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients. The risk prediction model based on these factors has a high predictive value and can be applied to the risk assessment of SAE after interventional therapy in ACHD-PH patients to help clinicians perform early intervention.
ObjectiveTo summarize the current status and update of the use of medical imaging in risk prediction of pancreatic fistula following pancreaticoduodenectomy (PD).MethodA systematic review was performed based on recent literatures regarding the radiological risk factors and risk prediction of pancreatic fistula following PD.ResultsThe risk prediction of pancreatic fistula following PD included preoperative, intraoperative, and postoperative aspects. Visceral obesity was the independent risk factor for clinically relevant postoperative pancreatic fistula (CR-POPF). Radiographically determined sarcopenia had no significant predictive value on CR-POPF. Smaller pancreatic duct diameter and softer pancreatic texture were associated with higher incidence of pancreatic fistula. Besides the surgeons’ subjective intraoperative perception, quantitative assessment of the pancreatic texture based on medical imaging had been reported as well. In addition, the postoperative laboratory results such as drain amylase and serum lipase level on postoperative day 1 could also be used for the evaluation of the risk of pancreatic fistula.ConclusionsRisk prediction of pancreatic fistula following PD has considerable clinical significance, it leads to early identification and early intervention of the risk factors for pancreatic fistula. Medical imaging plays an important role in this field. Results from relevant studies could be used to optimize individualized perioperative management of patients undergoing PD.
Objective To develop and compare the predictive performance of five machine learning models for adverse postoperative outcomes in cardiac surgery patients, and to identify key decision factors through SHapley Additive exPlanations (SHAP) interpretability analysis. Methods A retrospective collection of perioperative data (including demographic information, preoperative, intraoperative, and postoperative indicators) with 88 variables was conducted from adult cardiac surgery patients at the First Affiliated Hospital of Xinjiang Medical University in 2023. Adverse postoperative outcomes were defined as the occurrence of acute kidney injury and/or in-hospital mortality during the postoperative hospitalization period following cardiac surgery. Patients were divided into an adverse outcome group and a favorable outcome group based on the presence of adverse postoperative outcomes. After screening feature variables using the least absolute shrinkage and selection operator (LASSO) regression method, five machine learning models were constructed: eXtreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), light gradient boosting machine (LightGBM), and generalized linear model (GLM). The dataset was randomly divided into a training set and a test set at a 7 : 3 ratio using stratified sampling, with postoperative outcome as the stratification factor. Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and F1 Score. The SHAP method was applied to analyze feature contribution. Results A total of 639 patients were included, comprising 395 males and 244 females, with a median age of 62 (55, 69) years. The adverse outcome group consisted of 191 patients, while the favorable outcome group included 448 patients, resulting in an adverse postoperative outcome incidence of 29.9%. Univariate analysis showed no significant differences between the two groups for any variables (P>0.05). Using LASSO regression, 16 feature variables were selected (including cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, systemic inflammatory response index, etc.), and five machine learning models (GLM, RF, GBM, LightGBM, XGBoost) were constructed. Evaluation results demonstrated that the XGBoost model exhibited the best predictive performance on both the training set (n=447) and test set (n=192), with area under the curve values of 0.761 [95%CI (0.719, 0.800) ] and 0.759 [95%CI (0.692, 0.818) ], respectively. It also significantly outperformed other models in positive predictive value, and balanced accuracy in the test set. Decision curve analysis further confirmed its clinical utility across various risk thresholds. SHAP analysis indicated that variables such as cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, and inflammatory markers (SIRI, NLR, CAR) had high contributions to the prediction. Conclusion The XGBoost model effectively predicts adverse postoperative outcomes in cardiac surgery patients. Clinically, attention should be focused on cardiopulmonary bypass support time, postoperative blood glucose control, and monitoring of inflammatory levels to improve patient prognosis.
ObjectiveTo systematically evaluate the risk prediction models for postoperative delirium in adults with cardiac surgery. MethodsThe SinoMed, CNKI, Wanfang, VIP, PubMed, EMbase, Web of Science, and Cochrane Library databases were searched to collect studies on risk prediction models for postoperative delirium in cardiac surgery published up to January 29, 2025. Two researchers screened the literature according to inclusion and exclusion criteria, used the PROBAST bias tool to assess the quality of the literature, and conducted a meta-analysis of common predictors in the model using Stata 17.0 software. ResultsA total of 21 articles were included, establishing 45 models with 28733 patients. Age, cardiopulmonary bypass time, history of diabetes, history of cerebrovascular disease, and gender were the top five common predictors. The area under the curve (AUC) of the 45 models ranged from 0.6 to 0.926. Fourteen out of the 21 studies had good applicability, while the applicability of the remaining seven was unclear; 20 studies had a high risk of bias. Meta-analysis showed that the incidence of postoperative delirium in adults with cardiac surgery was 18.6% [95%CI (15.7%, 21.6%)], and age [OR=1.04 (1.04, 1.05), P<0.001], history of cerebrovascular disease [OR=1.76 (1.46, 2.06), P<0.001], gender [OR=1.73 (1.43, 2.03), P<0.001], minimum mental state examination score [OR=1.00 (0.82, 1.17), P<0.001], and length of ICU stay [OR=5.59 (4.29, 6.88), P<0.001] weer independent influencing factors of postoperative delirium after cardiac surgery. ConclusionThe risk prediction models for postoperative delirium after cardiac surgery have good predictive performance, but there is a high overall risk of bias. In the future, large-sample, multicenter, high-quality prospective clinical studies should be conducted to construct the optimal risk prediction model for postoperative delirium in adults with cardiac surgery, aiming to identify and prevent the occurrence of postoperative delirium as early as possible.
Objective To analyze the risk factors affecting the occurrence of arrhythmia after esophageal cancer surgery, construct a risk prediction model, and explore its clinical value. Methods A retrospective analysis was conducted on the clinical data of patients who underwent radical esophagectomy for esophageal cancer in the Department of Thoracic Surgery at Anhui Provincial Hospital from 2020 to 2023. Univariate and multivariate analyses were used to screen potential factors influencing postoperative arrhythmia. A risk prediction model for postoperative arrhythmia was constructed, and a nomogram was drawn. The predictive performance of the model was then validated. Results A total of 601 esophageal cancer patients were randomly divided into a modeling group (421 patients) and a validation group (180 patients) at a 7 : 3 ratio. In the modeling group, patients were further categorized into an arrhythmia group (188 patients, 44.7%) and a non-arrhythmia group (233 patients, 55.3%) based on whether they developed postoperative arrhythmia. Among those with postoperative arrhythmia, 43 (10.2%) patients had atrial fibrillation (AF), 12 (2.9%) patients had atrial premature beats, 15 (3.6%) patients had sinus bradycardia, and 143 (34%) patients had sinus tachycardia. Some patients exhibited multiple arrhythmias, including 14 patients with AF combined with sinus tachycardia, 7 patients with AF combined with atrial premature beats, and 3 patients with AF combined with sinus bradycardia. Univariate analysis revealed that a history of hypertension, heart disease, pulmonary infection, acute respiratory distress syndrome, postoperative hypoxia, anastomotic leakage, and delirium were risk factors for postoperative arrhythmia in esophageal cancer patients (P<0.05). Multivariate logistic regression analysis showed that a history of heart disease, pulmonary infection, and postoperative hypoxia were independent risk factors for postoperative arrhythmia after esophageal cancer surgery (P<0.05). The area under the receiver operating characteristic curve (AUC) of the constructed risk prediction model for postoperative arrhythmia was 0.710 [95% CI (0.659, 0.760)], with a sensitivity of 0.617 and a specificity of 0.768. Conclusion A history of heart disease, pulmonary infection, and postoperative hypoxia are independent risk factors for postoperative arrhythmia after esophageal cancer surgery. The risk prediction model constructed in this study can effectively identify high-risk patients for postoperative arrhythmia, providing a basis for personalized interventions.
Surgical risk prediction is to predict postoperative morbidity and mortality with internationally authoritative mathematical models. For patients undergoing high-risk cardiac surgery, surgical risk prediction is helpful for decision-making on treatment strategies and minimization of postoperative complications, which has gradually arouse interest of cardiac surgeons. There are many risk prediction models for cardiac surgery in the world, including European System for Cardiac Operative Risk Evaluation (EuroSCORE), Ontario Province Risk (OPR)score, Society of Thoracic Surgeons (STS)score, Cleveland Clinic risk score, Quality Measurement and Management Initiative (QMMI), American College of Cardiology/American Heart Association (ACC/AHA)Guidelines for Coronary Artery Bypass Graft Surgery, and Sino System for Coronary Operative Risk Evaluation (SinoSCORE). All these models are established from the database of thousands or ten thousands patients undergoing cardiac surgery in a specific region. As different sources of data and calculation imparities exist, there are probably bias and heterogeneities when the models are applied in other regions. How to decrease deviation and improve predicting effects had become the main research target in the future. This review focuses on the progress of risk prediction models for patients undergoing cardiac surgery.
Objective To construct and compare risk prediction models for skip metastasis in papillary thyroid carcinoma (PTC) patients with lateral lymph node metastasis (N1b) by using multiple machine learning algorithms, and to provide clinical guidance through model interpretation and visualization. MethodsA retrospective analysis of 573 N1b PTC patients who were admitted between November 2011 and August 2024 in Zhongshan Hospital Affiliated to Xiamen University and undergone primary surgery were conducted. Patients were randomly divided into training (n=402) and testing (n=171) sets according to 7∶3 ratio by using R package caret. The training set was used to build the model, and the test set was used for model validation. Five machine learning models including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) by using 10-fold cross-validation on the training set to determine hyperparameters, then refited the models and validate them on the test set. Model performance was evaluated via area under the curve (AUC). Shapley additive explanations (SHAP) was employed for interpretability, and the optimal model was deployed as a web-based calculator using R Shiny. ResultsThe overall skip metastasis rate was 12.7% (73/573) in N1b PTC patients, with 12.9% (52/402) in the training set and 12.3% (21/171) in the testing set (P>0.05 for baseline comparisons). Eleven predictors (age, age≥55, sex, maximum tumor diameter, maximum tumor diameter≤1 cm, upper pole involvement, multifocality, unilateral lobe involvement, extrathyroidal extension, capsular invasion, and Hashimoto thyroiditis) were used to develop the model. Each model’s AUC of the training set: XGBoost, 0.824±0.070 ［95%CI (0.780, 0.868)］; LR, 0.802±0.065 ［95%CI (0.762, 0.842)］; DT, 0.773±0.141 ［95%CI (0.685, 0.861)］; RF, 0.767±0.068 ［95%CI (0.725, 0.809)］; SVM, 0.647±0.103 ［95%CI (0.583, 0.711)］. Each model’s AUC of the testing set: XGBoost, 0.777 ［95%CI (0.667, 0.887); LR, 0.769 ［95%CI (0.655, 0.883)］; DT, 0.737 ［95%CI (0.615, 0.858)］; RF, 0.757 ［95%CI (0.649, 0.865)］; SVM, 0.674 ［95%CI (0.522, 0.826)］. XGBoost was the optimum model which achieved the highest AUC in both training and testing sets. SHAP analysis identified the top six predictors: upper pole involvement (mean absolute SHAP: 0.249), maximum tumor diameter (0.119), extrathyroidal extension (0.078), age (0.065), unilateral lobe involvement (0.018), and capsular invasion (0.013). The XGBoost-based web calculator was accessible. ConclusionsThe XGBoost model demonstrates superior predictive performance among five machine learning algorithms. The developed web-based calculator offers clinical utility for assessing skip metastasis risk in N1b PTC patients.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.