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Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Care | Research article

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Authors: Sameh N. Saleh, Anil N. Makam, Ethan A. Halm, Oanh Kieu Nguyen

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8–30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors.

Methods

We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50–50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients.

Results

Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive.

Conclusion

A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
Literature
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go back to reference Auerbach AD, Kripalani S, Vasilevskis EE, Sehgal N, Lindenauer PK, Metlay JP, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176:484–93.CrossRef Auerbach AD, Kripalani S, Vasilevskis EE, Sehgal N, Lindenauer PK, Metlay JP, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med. 2016;176:484–93.CrossRef
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go back to reference Nguyen OK, Makam AN, Clark C, Zhang S, Xie B, Velasco F, et al. Predicting all-cause readmissions using electronic health record data from the entire hospitalization: model development and comparison. J Hosp Med. 2016;11:473–80.CrossRef Nguyen OK, Makam AN, Clark C, Zhang S, Xie B, Velasco F, et al. Predicting all-cause readmissions using electronic health record data from the entire hospitalization: model development and comparison. J Hosp Med. 2016;11:473–80.CrossRef
Metadata
Title
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
Authors
Sameh N. Saleh
Anil N. Makam
Ethan A. Halm
Oanh Kieu Nguyen
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
Care
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-01248-1

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