Currently submitted to: JMIR Medical Informatics
Date Submitted: Aug 4, 2026
Open Peer Review Period: Aug 5, 2026 - Sep 30, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Developing a Heart Failure Readmission Model From Inpatient Electronic Medical Record Data
ABSTRACT
Background:
Heart failure readmissions remain common following hospitalization, but accurately identifying which patients will be readmitted after discharge remains challenging. Improved prediction could support targeted transitional care interventions and more efficient allocation of clinical resources.
Objective:
In this study we attempted to improve readmission prediction after heart failure hospitalization by using variables chosen through a modified Delphi process, and using inpatient Electronic Medical Record (EMR) data, focusing on clinical notes.
Methods:
This prognostic study developed competing risk survival models to predict readmission after heart failure hospitalization. Variables were chosen using a modified Delphi process, and extracted from EMR notes using various natural language processing techniques or from other EMR elements where appropriate. Patients were admitted between 2011 through 2019, and at least one year of follow-up was available for all patients. Models were evaluated using C-statistics, as well as sensitivity, specificity, positive and negative predictive values. During the study period, all acute-care facilities in Calgary, Alberta used the same EMR system, from which patients were selected. Patients were 18 years or older, resided in Alberta, and were admitted to a Calgary hospital. All corresponding admissions with a most responsible diagnosis of heart failure were included (n=15,160). The main outcome of interest was readmission within 30 days, though 90- and 365-day time frames were also analyzed. Death was treated as a competing risk and analysed at those time frames as well.
Results:
Many of the identified variables believed to be important for predicting readmission were not collected reliably enough to be used in readmission models. The final model had a C-statistic of 64.4 when predicting readmission within 30 days, in line with previous studies.
Conclusions:
Efforts to improve heart failure readmission prediction should work with clinical teams to ensure variables believed to be important are collected during hospitalization.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.