Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis

Maggie Yu; Mark Harrison; Nick Bansback

doi:10.1007/s11136-024-03638-8

Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis

Qual Life Res. 2024 Apr 30. doi: 10.1007/s11136-024-03638-8. Online ahead of print.

Authors

Maggie Yu^{1

2}, Mark Harrison^{3

2}, Nick Bansback^{4

5}

Affiliations

¹ School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
² Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada.
³ Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada.
⁴ School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. nick.bansback@ubc.ca.
⁵ Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada. nick.bansback@ubc.ca.

PMID: 38689165
DOI: 10.1007/s11136-024-03638-8

Abstract

Purpose: To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions.

Methods: We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines.

Results: Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data.

Conclusion: Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

Keywords: Hospital readmission; Patient-reported outcome measures; Prediction model; Quality of life; Systematic review.

Publication types

Review