Objectives To evaluate the impact of comorbidities, acute illness burden and social determinants of health on predicting the risk of frequent hospital admissions.
Design Multivariable logistic regression was used to associate the predictive variables extracted from electronic health records and frequent hospital admission risk. The model's performance of our predictive model was evaluated using a 10-fold cross-validation.
Setting A single tertiary hospital in Singapore.
Participants All adult patients admitted to the hospital between 1 January 2013 and 31 May 2014 (n=25 244).
Main outcome measure Frequent hospital admissions, defined as 3 or more inpatient admissions within 12 months of discharge. Area under the receiver operating characteristic curve (AUC) of the predictive model, and the sensitivity, specificity and positive predictive values for various cut-offs.
Results 4322 patients (17.1%) met the primary outcome. 11 variables were observed as significant predictors and included in the final regression model. The strongest independent predictor was treatment with antidepressants in the past 1 year (adjusted OR 2.51, 95% CI 2.26 to 2.78). Other notable predictors include requiring dialysis and treatment with intravenous furosemide during the index admission. The predictive model achieved an AUC of 0.84 (95% CI 0.83 to 0.85) for predicting frequent hospital admission risk, with a sensitivity of 73.9% (95% CI 72.6% to 75.2%), specificity of 79.1% (78.5% to 79.6%) and positive predictive value of 42.2% (95% CI 41.1% to 43.3%) at the cut-off of 0.235.
Conclusions We have identified several predictors for assessing the risk of frequent hospital admissions that achieved high discriminative model performance. Further research is necessary using an external validation cohort.
- Frequent hospital admission
- illness burden
- social determinants of health
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Contributors LLL and NL conceived and designed the study. LLL, NL and SW performed the study and analysed the data, including modelling and cross validation. LLL, NL, SW and KHL interpreted the results. LLL and NL wrote the first draft of the paper and all authors critically revised the paper and gave final approval for publication.
Funding This research received funding from Singapore Ministry of Health (MOH) Health Services Research New Investigator Grant HSRNIG14nov002 and SingHealth Foundation Health Services Research (Aging) Startup Grant SHF/HSRAg004/2015.
Competing interests None declared.
Ethics approval This study was approved by Singapore Health Services (SingHealth) Centralized Institutional Review Board CIRB 2015/2696 with a waiver of informed consent.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Details of the variables and derived predictive model are available from the corresponding authors at firstname.lastname@example.org or email@example.com.
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