QALY: a model to predict quality-adjusted life years in cancer patients needing intensive care

• Grant number: 09/17245-0
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2010
• End date: May 31, 2012
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Alexandre Biasi Cavalcanti
• Grantee: Alexandre Biasi Cavalcanti
• Host Institution: Instituto do Câncer do Estado de São Paulo Octavio Frias de Oliveira (ICESP). Coordenadoria de Serviços de Saúde (CSS). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil

Abstract

Quality-adjusted life years (QALY) measures length and quality of life simultaneously. QALY might be useful to inform clinical management of critically ill cancer patients. However, a model to predict QALY for critically ill patients is not available. Objectives: To develop a prognostic model having QALY as outcome for critically ill cancer patients being considered for intensive care unit (ICU) admission and another model for cancer patients on the fifth ICU day. Design: Prospective cohort study. Settings: Cancer-specialized intensive care units. Patients: Adult patients with cancer admitted to intensive care units. Baseline variables, endpoints and follow-up: We will collect the following potential prognostic factors: Demographic data; socio-economic data; relevant prior health variables; pre-ICU characteristics; reason for admission to ICU; clinical, physiological and laboratory variables within one hour of ICU admission and between 96 and 120 hours of ICU admission. Follow-up will be carried out by telephone, at 1, 3, 6, 12, 18 and 24 months, to determine vital status and health-related quality of life using EQ-5D. Main outcome is QALY, which is a product of each individual patient life span times the EQ-5D summary index (from 0 to 1). Sample size and statistical analysis: 500 patients will be included for developing the model, the aim of this proposal. After its development, we intend to carry out second study enrolling 250 patients to validate it. Cox proportional hazards regression will be used to model QALY. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DOS SANTOS, HELLEN GEREMIAS; ZAMPIERI, FERNANDO GODINHO; NORMILIO-SILVA, KARINA; DA SILVA, GISELA TUNES; PEDROSO DE LIMA, ANTONIO CARLOS; CAVALCANTI, ALEXANDRE BIASI; PORTO CHIAVEGATTO FILHO, ALEXANDRE DIAS. Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer. JOURNAL OF CRITICAL CARE, v. 55, p. 73-78, FEB 2020. (17/09369-8, 09/17245-0)