Development and External Validation of a Predictive Multivariable Model for Palliative Cancer Patients’ Survival (PACS)

Abstract

Abstract Purpose Various prognostic indexes have been proposed to improve physicians’ ability to predict survival time in advanced cancer patients admitted to palliative care (PC), but no optimal score has still been identified. The study therefore aims to develop and externally validate a new multivariable predictive model in this setting.Methods We developed the model on 1020 cancer patients prospectively enrolled to home care palliative care at VIDAS Milan, Italy, between May 2018 and February 2020 and followed-up to June 2020. The model was then validated among two separate samples of 544 home care and 247 hospice patients. Overall survival was considered as the primary outcome to develop and validate the model; Cox and flexible parametric Royston-Parmar regression models were used.Results Through a four-step modelling process, among 68 clinical factors considered, five predictors were included in the predictive model, i.e., rattle, heart rate, anorexia, liver failure, and the Karnofsky performance status. Patient’s survival probability at various time points was estimated. The predictive model showed a good calibration and moderate discrimination (area under the receiver operating characteristic curve between 0.72 and 0.79) in the home care validation set, but model calibration was suboptimal in hospice patients.Conclusions The new multivariable predictive model for palliative cancer patients’ survival (PACS model) includes clinical parameters routinely at patient’s admission to PC and can be easily used to facilitate immediate and appropriate clinical decisions for PC cancer patients in the home setting.