Please use this identifier to cite or link to this item: https://doi.org/10.1111/ijcp.14306
Title: Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study
Authors: Sharmin, Sifat
Meij, Johannes J
Zajac, Jeffrey D
Moodie, Alan Rob
Maier, Andrea B 
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
Pharmacology & Pharmacy
General & Internal Medicine
HOSPITAL READMISSION
RISK-FACTORS
DEATH
VALIDATION
DISEASE
MODELS
Issue Date: 17-May-2021
Publisher: WILEY
Citation: Sharmin, Sifat, Meij, Johannes J, Zajac, Jeffrey D, Moodie, Alan Rob, Maier, Andrea B (2021-05-17). Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study. INTERNATIONAL JOURNAL OF CLINICAL PRACTICE 75 (8). ScholarBank@NUS Repository. https://doi.org/10.1111/ijcp.14306
Abstract: Objective: To develop a predictive model for identifying patients at high risk of all-cause unplanned readmission within 30 days after discharge, using administrative data available before discharge. Materials and methods: Hospital administrative data of all adult admissions in three tertiary metropolitan hospitals in Australia between July 01, 2015, and July 31, 2016, were extracted. Predictive performance of four mixed-effect multivariable logistic regression models was compared and validated using a split-sample design. Diagnostic details (Charlson Comorbidity Index CCI, components of CCI, and primary diagnosis categorised into International Classification of Diseases chapters) were added gradually in the clinically simplified model with socio-demographic, index admission, and prior hospital utilisation variables. Results: Of the total 99 470 patients admitted, 5796 (5.8%) were re-admitted through the emergency department of three hospitals within 30 days after discharge. The clinically simplified model was as discriminative (C-statistic 0.694, 95% CI [0.681-0.706]) as other models and showed excellent calibration. Models with diagnostic details did not exhibit any substantial improvement in predicting 30-days unplanned readmission. Conclusion: We propose a 10-item predictive model to flag high-risk patients in a diverse population before discharge using readily available hospital administrative data which can easily be integrated into the hospital information system.
Source Title: INTERNATIONAL JOURNAL OF CLINICAL PRACTICE
URI: https://scholarbank.nus.edu.sg/handle/10635/234879
ISSN: 1368-5031
1742-1241
DOI: 10.1111/ijcp.14306
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