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.
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.
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 explore the risk factors of chronic postoperative inguinal pain (CPIP) after transabdominal preperitoneal hernia repair (TAPP), establish and verify the risk prediction model, and then evaluate the prediction effectiveness of the model. Methods The clinical data of 362 patients who received TAPP surgery was retrospectively analyzed and divided into model group (n=300) and validation group (n=62). The risk factors of CPIP in the model group were screened by univariate analysis and multivariate logistic regression analysis, and the risk prediction model was established and tested. Results The incidence of CPIP at 6 months after operation was 27.9% (101/362). Univariate analysis showed that gender (χ2= 12.055, P=0.001), age (t=–4.566, P<0.01), preoperative pain (χ2=44.686, P<0.01) and early pain at 1 week after operation (χ2=150.795, P<0.01) were related to CPIP. Multivariate logistic regression analysis showed that gender, age, preoperative pain, early pain at 1 week after operation, and history of lower abdominal surgery were independent risk predictors of CPIP. The area under curve (AUC) of the receiver operating characteristic (ROC) of the risk prediction model was calculated to be 0.933 [95%CI (0.898, 0.967)], and the optimal cut-off value was 0.129, while corresponding specificity and sensitivity were 87.6% and 91.5% respectively. The prediction accuracy, specificity and sensitivity of the model were 91.9% (57/62), 90.7% and 94.7%, respectively when the validation group data were substituted into the prediction model. Conclusion Female, age≤64 years old, preoperative pain, early pain at 1 week after operation and without history of lower abdominal surgery are independent risk factors for the incidence of CPIP after TAPP, and the risk prediction model established on this basis has good predictive efficacy, which can further guide the clinical practice.
Risk prediction models for postoperative pulmonary complications (PPCs) can assist healthcare professionals in assessing the likelihood of PPCs occurring after surgery, thereby supporting rapid decision-making. This study evaluated the merits, limitations, and challenges of these models, focusing on model types, construction methods, performance, and clinical applications. The findings indicate that current risk prediction models for PPCs following lung cancer surgery demonstrate a certain level of predictive effectiveness. However, there are notable deficiencies in study design, clinical implementation, and reporting transparency. Future research should prioritize large-scale, prospective, multi-center studies that utilize multiomics approaches to ensure robust data for accurate predictions, ultimately facilitating clinical translation, adoption, and promotion.
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.
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.
Primary graft dysfunction (PGD) is the most common and significant complication affecting long-term survival rates after lung transplantation. The occurrence of PGD is closely related to donor-recipient risk factors, surgical procedures, and perioperative management. Early identification and standardized intervention are crucial for improving prognosis. This consensus was developed by a multidisciplinary expert group in the field of lung transplantation in China, based on a systematic literature review, evidence-based medical evidence, and clinical practice experience. It systematically outlines the definition and classification of PGD, the main pathological mechanisms, donor-recipient and perioperative risk factors, and establishes a dynamic early warning mechanism and graded treatment standard process. This consensus emphasizes the construction of a complete closed-loop management system through comprehensive preoperative assessment, multiparameter monitoring during surgery, standardized postoperative intervention, and follow-up management after discharge. The aim is to standardize clinical practices, reduce the incidence of PGD, promote graft function recovery, and improve long-term survival rates for patients. The consensus employs the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system to evaluate the strength of recommendations and the level of evidence, providing a scientific, systematic, and actionable clinical guidance framework for lung transplantation centers.
Objective To investigate the key risk factors for low anterior resection syndrome (LARS) within 6 months after rectal cancer surgery and to construct a risk prediction model based on the random forest algorithm, providing a reference for early clinical intervention. Methods A retrospective study was conducted on patients who underwent rectal cancer surgery at the West China Hospital of Sichuan University from January 2020 to August 2021. A prediction model for the occurrence of LARS within 6 months after rectal cancer surgery was constructed using the random forest algorithm. The dataset was divided into a training set and a test set in an 8∶2 ratio. The model performance was evaluated by accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis (DCA). Results A total of 394 patients were enrolled. Among the 394 patients, 106 developed LARS within 6 months after surgery, with an incidence rate of 26.9%. According to the importance ranking in the random forest algorithm, the key predictive factors were: distance from the inferior tumor margin to the dentate line, body mass index (BMI), tumor size, time to first postoperative flatus, operation time, age, neoadjuvant therapy, and TNM stage. The prediction model constructed using these key factors achieved the accuracy of 73.4%, sensitivity of 75.0%, specificity of 72.7%, AUC (95% confidence interval) of 0.801 (0.685, 0.916), and the Brier score of 0.198. DCA showed that the model provided favorable clinical benefit when the threshold probability was between 25% and 64%. Conclusions The results of this study suggest that patients with a shorter distance from the tumor to the dentate line, higher BMI, and larger tumor size are at higher risk of developing LARS. The risk prediction model constructed in this study demonstrates a good predictive performance and may provide a useful reference for early identification of high-risk patients after rectal cancer surgery.