In Journal of critical care ; h5-index 48.0
PURPOSE : To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY).
MATERIAL AND METHODS : Six machine-learning algorithms were applied to predict 30-day QALY for 777 patients admitted in a prospective cohort study conducted in Intensive Care Units (ICUs) of two public Brazilian hospitals specialized in cancer care. The predictors were 37 characteristics collected at ICU admission. Discrimination was evaluated using the area under the receiver operating characteristic (AUROC) curve. Sensitivity, 1-specificity, true/false positive and negative cases were measured for different estimated probability cutoff points (30%, 20% and 10%). Calibration was evaluated with GiViTI calibration belt and test.
RESULTS : Except for basic decision trees, the adjusted predictive models were nearly equivalent, presenting good results for discrimination (AUROC curves over 0.80). Artificial neural networks and gradient boosted trees achieved the overall best calibration, implying an accurately predicted probability for 30-day QALY.
CONCLUSIONS : Except for basic decision trees, predictive models derived from different machine-learning algorithms discriminated the QALY risk at 30 days well. Regarding calibration, artificial neural network model presented the best ability to estimate 30-day QALY in critically ill oncologic patients admitted to ICUs.
Santos Hellen Geremias Dos, Zampieri Fernando Godinho, Normilio-Silva Karina, Silva Gisela Tunes da, Lima Antonio Carlos Pedroso de, Cavalcanti Alexandre Biasi, Chiavegatto Filho Alexandre Dias Porto
Clinical decision-making, Critically ill patients, Intensive care unit, Machine learning, Prognosis, Quality of life