Annals of translational medicine · 2021
Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bai Zhixun, Hu Shan, Wang Yan, Deng Wenwen, Gu Ning, Zhao Ranzun, Zhang Wei, Ma Yi, Wang Zhenglong, Liu Zhijiang, Shen Changyin, Shi Bei
Аннотация
BACKGROUND: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients.
METHODS: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.
RESULTS: A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.
CONCLUSIONS: In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.
METHODS: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.
RESULTS: A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.
CONCLUSIONS: In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.