Risk Management and Healthcare Policy (Sep 2021)
Predicting Individual Risk of Emergency Hospital Admissions – A Retrospective Validation Study
Abstract
Kirstine Skov Benthien,1 Rikke Kart Jacobsen,1 Louise Hjarnaa,1 Gert Mehl Virenfeldt,1 Knud Rasmussen,2 Ulla Toft1 1Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark; 2Data and Development Support, Region Zealand, Sorø, DenmarkCorrespondence: Kirstine Skov BenthienCenter for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Nordre Fasanvej 57, Frederiksberg, 2000, DenmarkTel +45 31160453Email [email protected]: A high number of hospital admissions may indicate poor general health and less than optimal health care across sectors. To prevent hospital admissions, previous studies have focused on predicting readmissions relating to a defined index admission and specific condition, whereas generic models suited for community-dwelling persons are lacking. The aim of this study was to validate a generic model that predicted risk of emergency hospital admission within the following three months and to investigate regional variation.Materials and Methods: This study is an observational register-based validation study of a prediction model. The prediction model was based on a population of frail elderly, persons with non-communicable diseases, and persons with three emergency hospital admissions using information about diagnoses and hospital contacts. The prediction model consisted of two stages. In the first stage, covariate associations to admissions are estimated from observed data in one year. In the second stage, admissions are predicted in the coming three months based on observed estimations from the first stage. The validity of the model was calculated by comparing predicted and observed admissions from August 1st to October 31st, 2016.Results: The study included 112,026 persons. In nationwide data, area under the curve (AUC) was 0.7742 (95% CI 0.7698– 0.7786), and the positive predictive value was 52% for the 99th percentile (the top 1%). AUC varied between regions from 0.6914 in Southern Denmark (95% CI 0.6779– 0.7049) to 0.8224 (95% CI 0.8064– 0.8384) in North Denmark. AUC was higher with nationwide data compared to regional.Conclusion: The model performed satisfactorily in predicting individual risk of emergency hospital admission.Keywords: hospital admissions, prediction, chronic disease, frailty, prevention
