Hospitalization-Associated Disability After Cardiac Surgery in Elderly Patients　― Exploring the Risk Factors Using Machine Learning Algorithms ―

Figures

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  Figure 1.. Flow diagram of the patient selection process in this study. ARDS, acute respiratory distress syndrome; BI, Barthel Index; HAD, hospitalization-associated disability.

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  Figure 2.. Feature selection using the Boruta algorithm. Box plots depicting the importance of confounding variables on hospitalization-associated disability (HAD). “Confirmed” variables are those that were found to contribute significantly to HAD. *The cardiopulmonary bypass (CPB) time was considered to be 0 for those who did not undergo CPB. BI, Barthel Index; BMI, body mass index; CABG, coronary artery bypass grafting; CHF, chronic heart failure; COPD, chronic obstructive pulmonary disease; CRRT, continuous renal replacement therapy; CVA, cerebral vascular accident; eGFR, estimated glomerular filtration rate; FEV1.0%, forced expiratory volume % in one second; HAD, hospitalization-associated disability; HDS-R, Revised Hasegawa’s Dementia Scale; ICU, intensive care unit; LVEF, left ventricular ejection fraction; NT-proBNP, N-terminal pro B-type natriuretic peptide; NYHA, New York Heart Association; SPPB, Short Physical Performance Battery; VC, vital capacity.

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  Figure 3.. Exploration of the hospitalization-associated disability (HAD) risk profiles using the conditional inference tree. The boxes represent binary splits at the cut-off point (corresponding levels are specified) that maximized the discrepancy in the risk of the 2 subsamples of HAD. SPPB, Short Physical Performance Battery.