Prediction of depression among patients with cardiovascular disease scheduled cardiac surgery using an AI-empowered pipeline. (3rd October 2022)

• Record Type: Journal Article
• Authors: Pezoulas, V; Nowakowska, K; Kazmierski, J; Fotiadis, D; Sakellarios, A
• Is Part Of: European heart journal. Volume 43(2022)Supplement 2
• Publication Date: 2022-10-03
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac544.2428
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Introduction: Several studies have demonstrated, that one in three patients with cardiovascular disease (CVD) suffers from depression, and depression increases the likelihood for cardiac morbidity and mortality in the CVD population by 2–3-fold, independently of traditional risk factors or gender. Purpose: The aim of this work is to predict depression in patients with CVD. Methods: The clinical study was conducted in the cardiac surgical intensive care unit of The Central Clinical Hospital, the Medical University of Lodz in Poland, among patients scheduled for elective coronary artery bypass graft surgery (CABG). 224 patients signed an informed consent form, met the inclusion criteria and were enrolled in the study. All the patients had chronic coronary syndromes. The inclusion criteria were: consecutive adult patients scheduled for CABG surgery or CABG surgery with cardiac valve repair or replacement (CVR). The study population was examined by a psychiatrist the day prior to the scheduled operation, a diagnosis of major depressive disorder (MDD) was established on the basis of DSM-5 criteria. A data curation pipeline was applied to automatically remove outliers and duplicated fields in the input dataset. An AI-empowered pipeline was developed to classify patients at higher risk for depression. Random downsampling with replacement was applied to deal with the increased class imbalance by taking into consideration two confound factors, namely the Mini Mental State Examination and Hemoglobin concentration <10mg/dl. The downsampling process was repeated K times. In each iteration, three bagging and boosting ML schemas were utilized for the classification task including the AdaBoost (adaptive boosting), Random Forests and Extreme Gradient boosting trees (XGBoost). To this end, a nested cross-validation process was applied for hyperparameter optimization and model validation, where: (i) a 3-fold cross-validation process was first applied to seek for the optimal set of hyperparameters based on the grid search approach by tuning core parameters, including the learning rate, number of estimators, and max depth, among others, and (ii) a stratified 5-fold cross-validation process was subsequently used to evaluate the performance of the best model from the previous stage by computing the classification accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The performance evaluation results were averaged across the 5 folds and across the downsampling iterations. Shappley additive explanation analysis was finally applied to provide explainable risk factors for depression. Results: sRAGE was significantly correlated with depression (r=0.32, p=0.038). The Random forests classifier presented the highest performance to predict depression (Accuracy: 0.71, Sensitivity: 0.71, Specificity: 0.75 and AUC: 0.74). Conclusions: Depression can be predicted with 71% accuracy at patients with CVD and elevated sRAGE values. Funding Acknowledgement: Type of funding sources: Public grant(s) – EU funding. Main funding source(s): HORIZON2020