Statistical Models in Clinical Studies

Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review

Background: Disease prediction models often use statistical methods or machine learning, both with their own corresponding application scenarios, raising the risk of errors when used alone. Integrating machine learning into statistical methods may yield robust prediction models. This systematic review aims to comprehensively assess current development of global disease prediction integration models. Methods: PubMed, EMbase, Web of Science, CNKI, VIP, WanFang, and SinoMed databases were searched to collect studies on prediction models integrating machine learning into statistical methods from database inception to 2023 May 1. Information including basic characteristics of studies, integrating approaches, application scenarios, modeling details, and model performance was extracted. Results: A total of 20 eligible studies in English and 1 in Chinese were included. Five studies concentrated on diagnostic models, while 16 studies concentrated on predicting disease occurrence or prognosis. Integrating strategies of classification models included majority voting, weighted voting, stacking, and model selection (when statistical methods and machine learning disagreed). Regression models adopted strategies including simple statistics, weighted statistics, and stacking. AUROC of integration models surpassed 0.75 and performed better than statistical methods and machine learning in most studies. Stacking was used for situations with >100 predictors and needed relatively larger amount of training data. Conclusion: Research on integrating machine learning into statistical methods in prediction models remains limited, but some studies have exhibited great potential that integration models outperform single models. This study provides insights for the selection of integration methods for different scenarios. Future research could emphasize on the improvement and validation of integrating strategies.

Predicting postoperative hypoxemia risk factors in the patients after triple-branched stent graft implantation surgery with acute type A aortic dissection: A retrospective study

OBJECTIVE

To evaluate the risk factors of postoperative hypoxemia in patients after triple-branched stent graft implantation surgery with acute type A aortic dissection by conducting a nomogram.

METHODS

We evaluated 97 patients with acute type A aortic dissection (2020-2021), who underwent triple-branched stent graft implantation surgery. The independent risk factors were screened using univariate and multivariate logistic regression. We integrated significant factors as well as potential interference factors to build the nomogram model. The accuracy of the nomogram model was determined by using receiver operating characteristic curves (ROC), decision curve analyses (DCA), and calibration plots. Internal verification was evaluated using bootstrap validation.

RESULTS

Logistic regression analysis illustrated that the postoperative lactic acid, postoperative creatinine, and intraoperative aortic occlusion time were all independent risk factors for hypoxemia. Age, sex, and body mass index (BMI) were clinically relevant for predicting postoperative hypoxemia. We established a nomogram based on these six risk factors. The ROC (area under the curve [AUC] = 0.765), DCA, and calibration confirmed good discriminatory applicability and accuracy of the nomogram. Bootstrap validation (AUC = 0.76) verified the applicability of the nomogram.

CONCLUSIONS

The nomogram model could serve as a tool for the prediction of postoperative hypoxemia in patients after modified triple-branched stent graft implantation surgery with acute type A aortic dissection.