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CC BY-NC-ND 4.0 · Methods Inf Med 2021; 60(S 02): e65-e75
DOI: 10.1055/s-0041-1735166
Original Article

Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study

Autoren

  • Borim Ryu

    1   Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, South Korea
    2   Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, South Korea

  • Sooyoung Yoo‡   *

    1   Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, South Korea

  • Seok Kim

    1   Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, South Korea

  • Jinwook Choi‡   *

    2   Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, South Korea
    3   Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, South Korea

Funding This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (20004927, Advancing and expanding CDM-based distributed biohealth data platform) funded by Korea's Ministry of Trade, Industry, and Energy.
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Abstract

Background Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans.

Objectives The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management.

Methods Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC).

Results Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation.

Conclusions This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.

Keywords

readmission - machine learning - database - patient-level prediction - common data model

Note

The CDM-based prediction model has the following advantages. It can be easily reintegrated when migrating to a different EHR with analysis code adoption, either as an embedded frame in the EHR or as a standalone application. In addition, it can be easily expanded to another hospital based on OMOP CDM, which could be easily transferred and further developed with regard to our approach.


Authors' Contributions

B.R. analyzed the data and drafted the manuscript as the first author. S.K. helped prepare and evaluate the data. S.Y. helped analyze the data and managed the overall study, and J.C. supervised the overall study.


* These authors equally contributed to this work as co-corresponding authors.


Supplementary Material



Publikationsverlauf

Eingereicht: 17. Februar 2021

Angenommen: 05. Juli 2021

Artikel online veröffentlicht:
28. September 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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