• Hellen Geremias Dos Santos, Fernando Godinho Zampieri, Karina Normilio-Silva, Gisela Tunes da Silva, Antonio Carlos Pedroso de Lima, Alexandre Biasi Cavalcanti, and Alexandre Dias Porto Chiavegatto Filho.
• Carlos Chagas Institute, Oswaldo Cruz Foundation, Curitiba, Paraná, Brazil. Electronic address: hellen.santos@fiocruz.br.
• J Crit Care. 2020 Feb 1; 55: 73-78.

PurposeTo 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 MethodsSix 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.ResultsExcept 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.ConclusionsExcept 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.Copyright © 2019. Published by Elsevier Inc.

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