A Comprehensive Way to Access Hospital Death Prediction Model for Acute Mesenteric Ischemia: A Combination of Traditional Statistics and Machine Learning

PURPOSE: This study aimed to use traditional statistics and machine learning to develop and validate prediction models for predicting hospital death in patients with AMI and compare these models’ performance. PATIENTS AND METHODS: Data were retrieved from the Medical Information Mart for Intensive Care (MIMIC III) electronic clinical database. A total of 338 eligible AMI patients were divided into a training cohort (n = 238) and a validation cohort (n = 100), and all patients were divided into survival groups and nonsurvival groups according to patients’ hospital outcomes. The performance of the traditional statistics prediction model and the optimal machine learning prediction model was evaluated and compared with respect to discrimination, calibration, and clinical utility in the validation cohort. RESULTS: Univariate and multivariate logistic regression analyses identified the following independent risk factors associated with hospital death for AMI in the training cohort, including diastolic blood pressure, blood lactate, blood creatinine, age, blood pH, and red blood cell distribution width. Both the nomogram (AUC = 77.0%, 67.9–86.1%) and optimal machine learning model (AUC = 82.9%, 74.9–91.0%) achieved good discrimination and calibration in the validation cohort. Decision curves analysis showed that the optimal machine learning model has a greater net benefit than that of nomogram in this study. CONCLUSION: The nomogram achieved a concise and relatively accurate prediction of hospital death in patients with AMI, the machine learning model also has good discrimination and seems to have better clinical utility. Traditional statistics may help infer the relationship between risk factors and hospital death, while machine learning may contribute to a more accurate prediction. Traditional statistics and machine learning are complementary in developing the prediction model for hospital death of AMI. Therefore, a combination of nomogram–machine learning (Nomo-ML) predictive model may improve care and help clinicians make AMI management-related decisions.