A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning

Abstract

BackgroundPatients with heart failure (HF) with diabetes may face a poorer prognosis and higher mortality than patients with either disease alone, especially for those in intensive care unit. So far, there is no precise mortality risk prediction indicator for this kind of patient.MethodTwo high-quality critically ill databases, the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) Collaborative Research Database, were used for study participants’ screening as well as internal and external validation. Nine machine learning models were compared, and the best one was selected to define indicators associated with hospital mortality for patients with HF with diabetes. Existing attributes most related to hospital mortality were identified using a visualization method developed for machine learning, namely, Shapley Additive Explanations (SHAP) method. A new composite indicator ASL was established using logistics regression for patients with HF with diabetes based on major existing indicators. Then, the new index was compared with existing indicators to confirm its discrimination ability and clinical value using the receiver operating characteristic (ROC) curve, decision curve, and calibration curve.ResultsThe random forest model outperformed among nine models with the area under the ROC curve (AUC) = 0.92 after hyper-parameter optimization. By using this model, the top 20 attributes associated with hospital mortality in these patients were identified among all the attributes based on SHAP method. Acute Physiology Score (APS) III, Sepsis-related Organ Failure Assessment (SOFA), and Max lactate were selected as major attributes related to mortality risk, and a new composite indicator was developed by combining these three indicators, which was named as ASL. Both in the initial and external cohort, the new indicator, ASL, had greater risk discrimination ability with AUC higher than 0.80 in both low- and high-risk groups compared with existing attributes. The decision curve and calibration curve indicated that this indicator also had a respectable clinical value compared with APS III and SOFA. In addition, this indicator had a good risk stratification ability when the patients were divided into three risk levels.ConclusionA new composite indicator for predicting mortality risk in patients with HF with diabetes admitted to intensive care unit was developed on the basis of attributes identified by the random forest model. Compared with existing attributes such as APS III and SOFA, the new indicator had better discrimination ability and clinical value, which had potential value in reducing the mortality risk of these patients.

Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning

“…Although our identified models have a ‘fair’ predictive capacity (close to ‘good’), their estimated AUC is generally lower than the previous studies 9 , 12 , 14 , 15 . This state of affairs can be attributed in part to the availability of clinical information regarding the laboratory tests and vital signs that were used in the previous investigations, whereas in our study none of these information were used.…”

“…In another study 14 , the mortality of heart failure patients with diabetes was predicted using nine classifiers, including LR, RF, SVM, KNN, DT, GBM, XGBoost, LightGBM, and Bagging. The RF algorithm outperformed other algorithms, achieving an AUC of 0.92.…”

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“…Thereby, a new model is created to reduce the errors of the previous model [27]. LR was chosen for its simplicity and interpretability in terms of a broad application, XGBoost for its accuracy, performance, and handling of complex, high-dimensional, and imbalanced data [28][29][30].…”