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Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health
  1. Lian Leng Low1,2,
  2. Nan Liu3,4,5,
  3. Sijia Wang6,
  4. Julian Thumboo3,7,
  5. Marcus Eng Hock Ong5,8,
  6. Kheng Hock Lee1,2
  1. 1Department of Family Medicine & Continuing Care, Singapore General Hospital, Singapore, Singapore
  2. 2Family Medicine Program, Duke-NUS Medical School, Singapore, Singapore
  3. 3Singapore Health Services, Health Services Research Centre, Singapore, Singapore
  4. 4Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
  5. 5Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
  6. 6Integrated Health Information Systems, Singapore, Singapore
  7. 7Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
  8. 8Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
  1. Correspondence to Dr Lian Leng Low; low.lian.leng{at}singhealth.com.sg

Abstract

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
  • comorbidities
  • illness burden
  • social determinants of health
  • depression
  • readmission

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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Footnotes

  • 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 low.lian.leng@singhealth.com.sg or liu.nan@singhealth.com.sg.

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