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.
| Original language | English |
|---|---|
| Article number | e14306 |
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | International Journal of Clinical Practice |
| Volume | 75 |
| Issue number | 8 |
| Early online date | 7 May 2021 |
| DOIs | |
| Publication status | Published - Aug 2021 |
Bibliographical note
Funding Information:This work was supported by the Australian Government National Health and Medical Research Council (grant number APP1133163). The funder was not involved in the design of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of this manuscript; or decision to submit the manuscript for publication. We acknowledge Jenni Smith (General Manager, Research, Education and Partnerships Ambulatory Services), Adam King (Director, Decision Support), and Leanne Stojcevski from Northern Health; Liz Singleton (Director, Business Intelligence Unit) and Magda Giagias (Senior Data Analyst, Business Intelligence Unit) from The Royal Melbourne Hospital; and Ray Robbins (Senior Data Analyst) from Austin Health for their help on data extraction from the hospital databases.
Funding Information:
This work was supported by the Australian Government National Health and Medical Research Council (grant number APP1133163). The funder was not involved in the design of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of this manuscript; or decision to submit the manuscript for publication. We acknowledge Jenni Smith (General Manager, Research, Education and Partnerships Ambulatory Services), Adam King (Director, Decision Support), and Leanne Stojcevski from Northern Health; Liz Singleton (Director, Business Intelligence Unit) and Magda Giagias (Senior Data Analyst, Business Intelligence Unit) from The Royal Melbourne Hospital; and Ray Robbins (Senior Data Analyst) from Austin Health for their help on data extraction from the hospital databases.
Publisher Copyright:
© 2021 The Authors. International Journal of Clinical Practice published by John Wiley & Sons Ltd.
