Machine learning-based prediction of in-hospital mortality for post cardiovascular surgery patients admitting to intensive care unit: a retrospective observational cohort study based on a large multi-center critical care database. (November 2022)

• Record Type: Journal Article
• Authors: Bi, Siwei; Chen, Shanshan; Li, Jingyi; Gu, Jun
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Machine learning -- Post cardiovascular surgery -- Mortality; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107115
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The machine learning (ML) models outperformed the traditional models predicting mortality in post cardiovascular surgery patients (PCS) admitted to intensive care unit. The predicting ability of models was determined by the particular dataset and more prospective validation is needed. Application of these ML techniques to PCS patients may be of use to estimate in-hospital mortality with the ultimate goal to assist medical care decision Abstract: Background and objectives: The acute physiology and chronic health evaluation-IV model (APACHE-IV), and the sequential organ failure assessment (SOFA) score are two traditional severity assessment systems that can be applied to cardiac surgery patients admitted to intensive care units (ICUs). However, the performance of machine learning approaches in post cardiovascular surgery (PCS) patients admitted to the ICU remains unknown. Methods: The clinical data of adult subjects were collected from the eICU database. Seven models were constructed based on the training set (70% random sample) for predicting hospital mortality, including two traditional models based on APACHE-IV and SOFA scores and five machine learning models. We measured the models' performance in the remaining 30% of the sample by computing AUC-ROC values, prospective prediction results, and decision curves and compared the models with net reclassification improvement. Results: This study included 5860 PCS patients. The AUC-ROC value of the Xgboost model significantly outperformed the APACHE-IV and SOFA scores (0.12 [0.06–0.17] p < 0.01, 0.18 [0.1–0.26] p < 0.01 respectively). The use of ML models would also gain more clinical net benefits than traditional models based on decision curve analysis. There was a significant improvement in integrated discrimination when comparing the backward stepwise linear regression model with the APACHE-IV model (0.11 [0.05, 0.16], p < 0.01) and SOFA model (0.12 [0.06, 0.17], p < 0.01). Conclusions: In conclusion, the predictive ability of ML models was better than that of traditional models. The present study suggested that developing advanced prognosis prediction tools could support clinical decision-making in the ICU for PCS patients.