Carl van Walraven1, Alan J Forster2. 1. Departments of Medicine and Epidemiology & Community Medicine, University of Ottawa, Ontario, Canada; Ottawa Hospital Research Institute, Ontario, Canada; Institute for Clinical Evaluative Sciences (ICES@uOttawa), Ontario, Canada. Electronic address: carlv@ohri.ca. 2. Departments of Medicine and Epidemiology & Community Medicine, University of Ottawa, Ontario, Canada; Ottawa Hospital Research Institute, Ontario, Canada; The Ottawa Hospital, Ontario, Canada.
Abstract
BACKGROUND: The Hospital-patient One-year Mortality Risk (HOMR) score is an externally validated index using health administrative data to accurately predict the risk of death within 1 year of admission to the hospital. This study derived and internally validated a HOMR modification using data that are available when the patient is admitted to the hospital. METHODS: From all adult hospitalizations at our tertiary-care teaching hospital between 2004 and 2015, we randomly selected one per patient. We added to all HOMR variables that could be determined from our hospital's data systems on admission other factors that might prognosticate. Vital statistics registries determined vital status at 1 year from admission. RESULTS: Of 2,06,396 patients, 32,112 (15.6%) died within 1 year of admission to the hospital. The HOMR-now! model included patient (sex, comorbidities, living and cancer clinic status, and 1-year death risk from population-based life tables) and hospitalization factors (admission year, urgency, service and laboratory-based acuity score). The model explained that more than half of the total variability (Regenkirke's R2 value of 0.53) was very discriminative (C-statistic 0.92), and accurately predicted death risk (calibration slope 0.98). CONCLUSION: One-year risk of death can be accurately predicted using routinely collected data available when patients are admitted to the hospital.
BACKGROUND: The Hospital-patient One-year Mortality Risk (HOMR) score is an externally validated index using health administrative data to accurately predict the risk of death within 1 year of admission to the hospital. This study derived and internally validated a HOMR modification using data that are available when the patient is admitted to the hospital. METHODS: From all adult hospitalizations at our tertiary-care teaching hospital between 2004 and 2015, we randomly selected one per patient. We added to all HOMR variables that could be determined from our hospital's data systems on admission other factors that might prognosticate. Vital statistics registries determined vital status at 1 year from admission. RESULTS: Of 2,06,396 patients, 32,112 (15.6%) died within 1 year of admission to the hospital. The HOMR-now! model included patient (sex, comorbidities, living and cancer clinic status, and 1-year death risk from population-based life tables) and hospitalization factors (admission year, urgency, service and laboratory-based acuity score). The model explained that more than half of the total variability (Regenkirke's R2 value of 0.53) was very discriminative (C-statistic 0.92), and accurately predicted death risk (calibration slope 0.98). CONCLUSION: One-year risk of death can be accurately predicted using routinely collected data available when patients are admitted to the hospital.
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