Marlies Verhoeff, Janke de Groot, Jako S. Burgers, Barbara C. van Munster
Abstract
To develop and internally validate prediction models for future hospital care utilization in patients with multiple chronic conditions.
Introduction
The prevalence of multimorbidity (defined as having two or more chronic conditions) is increasing [1]. Kingston et al. (2018) predicted that by 2035 67.8% of the adults in the UK aged over 65 years will be living with multimorbidity [2]. An increasing prevalence of multimorbidity puts pressure on current healthcare systems, as hospital organizations are mostly providing disease-specific care that is generally delivered by separate disciplines or medical specialties [3,4]. Compared to patients with single chronic conditions, patients with multimorbidity have a higher risk of experiencing fragmented care, possibly resulting in suboptimal outcomes [4–8].
Methods
Our study is a retrospective cohort study of a large hospital population of patients with multimorbidity. We used data on the population’s demographics and healthcare utilization in 2017 to develop and internally validate three prediction models for healthcare utilization outcomes in 2018.
Results
Overall, 18180 patients were included (S1 Fig). Table 1 shows the general, disease and care characteristics in 2017. Median age of the population was 68.0 years (IQR 48.1–87.8 years). 61.6% of the included patients had two diagnoses, 24.4% had three diagnoses and the remaining patients had four or more chronic and/or oncologic diagnoses for which they had used hospital care. With regard to the outcomes in 2018, 2257 patients (12.4%) had at least one hospitalization in 2018, 1258 (6.9%) had two or more ED visits in 2018 and 1293 patients (7.1%) had at least 12 outpatient visits in 2018.
Discussion
The aim of this study was to develop and validate prediction models for future (1) ≥2 emergency department visits, (2) ≥1 acute hospitalization and (3) ≥12 outpatient visits in patients with multimorbidity, using existing administrative EHR data. Our results suggest that local administrative data from the EHR can be used to locally develop and validate reasonable performing prediction models for these outcomes. All prediction models also performed reasonably well in the validation sets (see S2–S4 Tables). The predicted and actual probabilities show good agreement in each model, but show a tendency to overestimate the actual probability in the higher risk groups for ≥1 hospitalization and ≥2 ED visits.
Citation: Verhoeff M, de Groot J, Burgers JS, van Munster BC (2022) Development and internal validation of prediction models for future hospital care utilization by patients with multimorbidity using electronic health record data. PLoS ONE 17(3): e0260829. https://doi.org/10.1371/journal.pone.0260829
Editor: Ram Chandra Bajpai, Keele University, UNITED KINGDOM
Received: March 24, 2021; Accepted: November 17, 2021; Published: March 17, 2022
Copyright: © 2022 Verhoeff et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because of privacy reasons: the data contains patient sensitive information and is bound to national privacy regulation. Data are available from the Gelre Institutional Data Access / Ethics Committee (contact via wetenschap@gelre.nl) for researchers who meet the criteria for access to confidential data.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
