Yang Cui1, Colleen Metge2, Xibiao Ye2, Michael Moffatt2, Luis Oppenheimer3, Evelyn L Forget4. 1. The George and Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada umcui@myumanitoba.ca. 2. The George and Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada. 3. Departments of Surgery & Family Medicine, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada Manitoba Health, Winnipeg, Manitoba, Canada. 4. Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
Abstract
OBJECTIVE: A number of predictive models have been developed to identify patients at risk of hospital readmission. Most of these have focused on readmission within 30 days of discharge. We used population-based health administrative data to develop a predictive model for hospital readmission within 12 months of discharge in Winnipeg, Canada. METHODS: This was a retrospective cohort study with derivation and validation data sets. Multivariable logistic regression analyses were performed and factors significantly associated with readmission were selected to construct a risk scoring tool. RESULTS: Several variables were identified that predicted readmission (i.e. older age, male, at least one hospital admission in the previous two years, an emergent (index) hospital admission, Charlson comorbidity score >0 and length of stay). Discrimination power was acceptable (C statistic =0.701). At a median risk score threshold, the sensitivity, specificity, positive and negative predictive values were 45.5%, 79%, 68.8% and 58.6%. CONCLUSIONS: This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.
OBJECTIVE: A number of predictive models have been developed to identify patients at risk of hospital readmission. Most of these have focused on readmission within 30 days of discharge. We used population-based health administrative data to develop a predictive model for hospital readmission within 12 months of discharge in Winnipeg, Canada. METHODS: This was a retrospective cohort study with derivation and validation data sets. Multivariable logistic regression analyses were performed and factors significantly associated with readmission were selected to construct a risk scoring tool. RESULTS: Several variables were identified that predicted readmission (i.e. older age, male, at least one hospital admission in the previous two years, an emergent (index) hospital admission, Charlson comorbidity score >0 and length of stay). Discrimination power was acceptable (C statistic =0.701). At a median risk score threshold, the sensitivity, specificity, positive and negative predictive values were 45.5%, 79%, 68.8% and 58.6%. CONCLUSIONS: This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.
Authors: Christine Xia Wu; Ernest Suresh; Francis Wei Loong Phng; Kai Pik Tai; Janthorn Pakdeethai; Jared Louis Andre D'Souza; Woan Shin Tan; Phillip Phan; Kelvin Sin Min Lew; Gamaliel Yu-Heng Tan; Gerald Seng Wee Chua; Chi Hong Hwang Journal: Appl Clin Inform Date: 2021-05-19 Impact factor: 2.342