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. 2023 Dec 16;13(1):22389.
doi: 10.1038/s41598-023-47339-7.

Predictors of frequency of 1-year readmission in adult patients with diabetes

Jade Gek Sang Soh  1   2 , Amartya Mukhopadhyay  3   4   5 , Bhuvaneshwari Mohankumar  6 , Swee Chye Quek  7 , Bee Choo Tai  8   4
Affiliations

Predictors of frequency of 1-year readmission in adult patients with diabetes

Jade Gek Sang Soh et al. Sci Rep. .

Abstract

Diabetes mellitus (DM) is the third most common chronic condition associated with frequent hospital readmissions. Predictors of the number of readmissions within 1 year among patients with DM are less often studied compared with those of 30-day readmission. This study aims to identify predictors of number of readmissions within 1 year amongst adult patients with DM and compare different count regression models with respect to model fit. Data from 2008 to 2015 were extracted from the electronic medical records of the National University Hospital, Singapore. Inpatients aged ≥ 18 years at the time of index admission with a hospital stay > 24 h and survived until discharge were included. The zero-inflated negative binomial (ZINB) model was fitted and compared with three other count models (Poisson, zero-inflated Poisson and negative binomial) in terms of predicted probabilities, misclassification proportions and model fit. Adjusted for other variables in the model, the expected number of readmissions was 1.42 (95% confidence interval [CI] 1.07 to 1.90) for peripheral vascular disease, 1.60 (95% CI 1.34 to 1.92) for renal disease and 2.37 (95% CI 1.67 to 3.35) for Singapore residency. Number of emergency visits, number of drugs and age were other significant predictors, with length of stay fitted as a zero-inflated component. Model comparisons suggested that ZINB provides better prediction than the other three count models. The ZINB model identified five patient characteristics and two comorbidities associated with number of readmissions. It outperformed other count regression models but should be validated before clinical adoption.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Determination of study cohort.
Figure 2
Figure 2
Frequency of number of readmissions in a year.
Figure 3
Figure 3
Comparing predicted probabilities of number of readmissions in a year between observed probabilities versus other count models. ZINB zero-inflated negative binomial regression model, ZIP zero-inflated Poisson regression model, NBRM negative binomial regression model, PRM Poisson regression model.
See this image and copyright information in PMC

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