Abstract 14629: Predictive Model Testing for Life-Sustaining Treatment Decisions at the End-of-Life: Applying Machine Learning Methods

Abstract

Introduction: Predictive models of factors affecting decisions about life-sustaining treatments (LSTs) have been frequently examined, but the accuracy and precision of those models remain elusive. Machine learning algorithms (MLAs) may help identify the best model in terms of its performance measures. Objectives: To determine the best MLA to predict LST preferences (cardiopulmonary resuscitation [CPR], ventilation support, and hemodialysis, and hospice care). Methods: In this secondary analysis, three MLA-based prediction models of LST preferences, including seven known predictors, were developed based on random forest (RF), logistics regression (LR), and linear support vector machine (SVM), and were evaluated by a five-fold validation set comprising accuracy, precision, area under the receiver operating characteristic curve, recall, and F1 score. Results: Among 375 adults with malignancy or non-malignancy diseases or healthy adults, 23.2% preferred CPR, 17.9% ventilation support, 19.2% hemodialysis, and 60.8% hospice care. Compared to RF and LR, SVM was the most accurate and precise model to predict each preference of CPR (0.78 vs. 0.85; 0.15-0.44 vs. 0.75, respectively), ventilation support (0.84 vs. 0.88; 0.00-0.10 vs. 0.89, respectively), hemodialysis (0.82 vs. 0.84; 0.33-0.54 vs. 0.69, respectively), and hospice care (0.64-0.68 vs. 0.85; 0.65-0.70 vs. 0.84, respectively). In the models, age, attitudes toward advance directives, and susceptibility to end-of-life care were reported as common predictors with relatively high importance on LST preferences (Table 1). Conclusions: SVM was the best model in identifying predictors of LST preferences in people with or without chronic diseases. In addition, the findings suggest important targets of interventions, including attitudes toward advance directives, and susceptibility to end-of-life care, to assist people to make the best decisions for their future medical care.