Predicting all-cause unplanned readmission within 30 days of discharge using electronic medical record data: A multi-centre study.

Autor:	Sharmin S; Clinical Outcomes Research Unit, Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia.; Melbourne Academic Centre for Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia., Meij JJ; Melbourne Academic Centre for Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia.; Department of Clinical Genetics and Outpatient Department, Amsterdam University Medical Center, Amsterdam, The Netherlands., Zajac JD; Department of Medicine (Austin Health), University of Melbourne, Melbourne, VIC, Australia., Moodie AR; Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia., Maier AB; Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands.; Department of Medicine and Aged Care, @AgeMelbourne, Royal Melbourne Hospital, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia.; Healthy Longevity Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.; Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore.
Jazyk:	angličtina
Zdroj:	International journal of clinical practice [Int J Clin Pract] 2021 Aug; Vol. 75 (8), pp. e14306. Date of Electronic Publication: 2021 May 17.
DOI:	10.1111/ijcp.14306
Abstrakt:	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. (© 2021 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd.)
Databáze:	MEDLINE
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