Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation.

BACKGROUND: This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation. METHODS: Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C0 . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml-1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model. RESULTS: A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml-1 ). The highest ratio of the APE ≤3 ng ml-1 was the LAR model (71.77%). And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor. CONCLUSIONS: Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emergent) admission ('A'), Charlson comorbidity index ('C') and visits to the emergency department during the previous 6 months ('E')] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia. METHODS: Elderly patients with arrhythmia who were hospitalized at Sichuan Provincial People's Hospital between 1 June 2018 and 31 May 2020 were enrolled. The LACE index was calculated for each patient, and the area under the receiver operating characteristic curve (AUROC) was calculated. Six machine learning algorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop machine learning models. AUROC and area under the precision-recall curve (AUPRC) were used to assess discrimination. Shapley additive explanations (SHAP) analysis was used to explain the contributions of the features. RESULTS: A total of 523 patients were enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning model had the best predictive performance, with an AUROC of 0.7571 and an AUPRC of 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotransferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level. CONCLUSIONS: Compared with the LACE index, the machine learning model can accurately identify the risk of 1 year readmission for heart failure in elderly patients with arrhythmia.

Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.

Background: Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms. Methods: The detailed clinical data of elderly CHD patients were collected retrospectively. Five ML algorithms, including extreme gradient boosting (XGB), random forest, multilayer perceptron, categorical boosting, and logistic regression, were used to establish predictive models. We used the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, the F1 value, the Brier score, the area under the precision-recall curve (AUPRC), and the calibration curve to evaluate the performance of ML models. The SHapley Additive exPlanations (SHAP) value was used to interpret the best model. Results: The final study included 834 elderly CHD patients, whose average age was 73.5 ± 8.4 years, among whom 426 (51.08%) were men and 139 had 7-day unplanned readmissions. The XGB model had the best performance, exhibiting the highest AUC (0.9729), accuracy (0.9173), F1 value (0.9134), and AUPRC (0.9766). The Brier score of the XGB model was 0.08. The calibration curve of the XGB model showed good performance. The SHAP method showed that fracture, hypertension, length of stay, aspirin, and D-dimer were the most important indicators for the risk of 7-day unplanned readmissions. The top 10 variables were used to build a compact XGB, which also showed good predictive performance. Conclusions: In this study, five ML algorithms were used to predict 7-day unplanned readmissions in elderly patients with CHD. The XGB model had the best predictive performance and potential clinical application perspective.

Research trends and hotspots in exercise rehabilitation for coronary heart disease: A bibliometric analysis.

Exercise rehabilitation can improve the prognosis of patients with coronary heart disease. However, a bibliometric analysis of the global exercise rehabilitation for coronary heart disease (CHD) research topic is lacking. This study investigated the development trends and research hotspots in the field of coronary heart disease and exercise rehabilitation. CiteSpace software was used to analyze the literature on exercise therapy for CHD in the Web of Science Core Collection database. We analyzed the data of countries/institutions, journals, authors, keywords, and cited references. A total of 3485 peer-reviewed papers were found, and the number of publications on the topic has steadily increased. The most productive country is the USA (1125), followed by China (477) and England (399). The top 3 active academic institutions are Research Libraries UK (RLUK) (236), Harvard University (152), and the University of California System (118). The most commonly cited journals are Circulation (2596), The most commonly cited references are "Exercise-based cardiac rehabilitation for coronary heart disease" (75), Lavie CJ had published the most papers (48). World Health Organization was the most influential author (334 citations). The research frontier trends in this field are body composition, participation, and function. Research on the effects of physical activity or exercise on patients with CHD is a focus of continuous exploration in this field. This study provides a new scientific perspective for exercise rehabilitation and CHD research and gives researchers valuable information for detecting the current research status, hotspots, and emerging trends for further research.

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

OBJECTIVE: This study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice. DESIGN: A nested case-control study. SETTING: National Center for ADR Monitoring and the Electronic Medical Record (EMR) system. PARTICIPANTS: All patients were from five medical institutions in Sichuan Province from January 2010 to December 2018. MAIN OUTCOMES/MEASURES: Data of patients with ADR who used Chinese herbal injections containing Panax notoginseng saponin were collected from the National Center for ADR Monitoring. A nested case-control study was used to randomly match patients without ADR from the EMR system by the ratio of 1:4. Eighteen machine learning algorithms were applied for the development of ADR prediction models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. An ADR prediction system was established by the best model selected from the 1080 models. RESULTS: A total of 530 patients from five medical institutions were included, and 1080 ADR prediction models were developed. Among these models, the AUC of the best capable one was 0.9141 and the accuracy was 0.8947. According to the best model, a prediction system, which can provide early identification of patients at risk for the ADR of Panax notoginseng saponin, has been established. CONCLUSION: The prediction system developed based on the machine learning model in this study had good predictive performance and potential clinical application.