Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer

Autor(es)

dos Santos, Hellen Geremias ^[1] ; Zampieri, Fernando Godinho ^[2] ; Normilio-Silva, Karina ^{[2, 3]} ; da Silva, Gisela Tunes ^[4] ; Pedroso de Lima, Antonio Carlos ^[4] ; Cavalcanti, Alexandre Biasi ^{[2, 3]} ; Porto Chiavegatto Filho, Alexandre Dias ^[5]

Afiliação do(s) autor(es)

^[1] Fundacao Oswaldo Cruz, Carlos Chagas Inst, Rua Prof Algacyr Munhoz Mader 3775, BR-81350010 Curitiba, Parana - Brazil

^[2] Heart Hosp, Res Inst, Hcor, Sao Paulo, SP - Brazil

^[3] Canc Inst State Sao Paulo, Sao Paulo, SP - Brazil

^[4] Univ Sao Paulo, Inst Math & Stat, Dept Stat, Sao Paulo, SP - Brazil

^[5] Univ Sao Paulo, Sch Publ Hlth, Dept Epidemiol, Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF CRITICAL CARE; v. 55, p. 73-78, FEB 2020.
• Citações Web of Science: 0

Resumo

Purpose: To develop and compare the predictive performance ofmachine-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. (C) 2019 Published by Elsevier Inc. (AU)

• Processo FAPESP: 17/09369-8 - Predição de óbitos segundo causa básica com machine learning em uma amostra longitudinal de 502.632 indivíduos
• Beneficiário: Alexandre Dias Porto Chiavegatto Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 09/17245-0 - QALY: modelo para predizer sobrevida ajustada para a qualidade em pacientes com câncer admitidos em unidades de terapia intensiva
• Beneficiário: Alexandre Biasi Cavalcanti
• Modalidade de apoio: Auxílio à Pesquisa - Regular