PURPOSE: End-of-life care for patients with advanced cancer is aggressive and costly. Oncologists inconsistently estimate life expectancy and address goals of care. Currently available prognostication tools are based on subjective clinical assessment. An objective prognostic tool could help oncologists and patients decide on a realistic plan for end-of-life care. We developed a predictive model (Imminent Mortality Predictor in Advanced Cancer [IMPAC]) for short-term mortality in hospitalized patients with advanced cancer. METHODS: Electronic health record data from 669 patients with advanced cancer who were discharged from Yale Cancer Center/Smilow Cancer Hospital were extracted. Statistical learning techniques were used to develop a tool to estimate survival probabilities. Patients were randomly split into training (70%) and validation (30%) sets 20 times. We tested the predictive properties of IMPAC for mortality at 30, 60, 90, and 180 days past the day of admission. RESULTS: For mortality within 90 days at a 40% sensitivity level, IMPAC has close to 60% positive predictive value. Patients estimated to have a greater than 50% chance of death within 90 days had a median survival time of 47 days. Patients estimated to have a less than 50% chance of death had a median survival of 290 days. Area under the receiver operating characteristic curve for IMPAC averaged greater than .70 for all time horizons tested. Estimated potential cost savings per patient was $15,413 (95% CI, $9,162 to $21,665) in 2014 constant dollars. CONCLUSION: IMPAC, a novel prognostic tool, can generate life expectancy probabilities in real time and support oncologists in counseling patients about end-of-life care. Potentially avoidable costs are significant.
PURPOSE: End-of-life care for patients with advanced cancer is aggressive and costly. Oncologists inconsistently estimate life expectancy and address goals of care. Currently available prognostication tools are based on subjective clinical assessment. An objective prognostic tool could help oncologists and patients decide on a realistic plan for end-of-life care. We developed a predictive model (Imminent Mortality Predictor in Advanced Cancer [IMPAC]) for short-term mortality in hospitalized patients with advanced cancer. METHODS: Electronic health record data from 669 patients with advanced cancer who were discharged from Yale Cancer Center/Smilow Cancer Hospital were extracted. Statistical learning techniques were used to develop a tool to estimate survival probabilities. Patients were randomly split into training (70%) and validation (30%) sets 20 times. We tested the predictive properties of IMPAC for mortality at 30, 60, 90, and 180 days past the day of admission. RESULTS: For mortality within 90 days at a 40% sensitivity level, IMPAC has close to 60% positive predictive value. Patients estimated to have a greater than 50% chance of death within 90 days had a median survival time of 47 days. Patients estimated to have a less than 50% chance of death had a median survival of 290 days. Area under the receiver operating characteristic curve for IMPAC averaged greater than .70 for all time horizons tested. Estimated potential cost savings per patient was $15,413 (95% CI, $9,162 to $21,665) in 2014 constant dollars. CONCLUSION: IMPAC, a novel prognostic tool, can generate life expectancy probabilities in real time and support oncologists in counseling patients about end-of-life care. Potentially avoidable costs are significant.
Authors: Dimitris Bertsimas; Jack Dunn; Colin Pawlowski; John Silberholz; Alexander Weinstein; Ying Daisy Zhuo; Eddy Chen; Aymen A Elfiky Journal: JCO Clin Cancer Inform Date: 2018-12
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Authors: Adrianna E Mojica-Márquez; Joshua L Rodríguez-López; Ankur K Patel; Diane C Ling; Malolan S Rajagopalan; Sushil Beriwal Journal: Cancer Med Date: 2020-06-26 Impact factor: 4.452