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Current Neurovascular Research

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ISSN (Print): 1567-2026
ISSN (Online): 1875-5739

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Research Article

Prediction Model of Early Return to Hospital after Discharge Following Acute Ischemic Stroke

Author(s): Jiann-Der Lee, Tsong-Hai Lee, Yen-Chu Huang, Meng Lee, Ya-Wen Kuo, Ya-Chi Huang and Ya-Han Hu*

Volume 16, Issue 4, 2019

Page: [348 - 357] Pages: 10

DOI: 10.2174/1567202616666190911125951

Price: $65

Abstract

Background: Reducing hospital readmissions for stroke remains a significant challenge to improve outcomes and decrease healthcare costs.

Methods: We analyzed 10,034 adult patients with ischemic stroke, presented within 24 hours of onset from a hospital-based stroke registry. The risk factors for early return to hospital after discharge were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses.

Results: Among the study population, 277 (2.8%) had 3-day Emergency Department (ED) reattendance, 534 (5.3%) had 14-day readmission, and 932 (9.3%) had 30-day readmission. Multivariate logistic regression revealed that age, nasogastric tube feeding, indwelling urinary catheter, healthcare utilization behaviour, and stroke severity were major and common risk factors for an early return to the hospital after discharge. CART analysis identified nasogastric tube feeding and length of stay for 72-hour ED reattendance, Barthel Index (BI) score, total length of stay in the Year Preceding the index admission (YLOS), indwelling urinary catheter, and age for 14-day readmission, and nasogastric tube feeding, BI score, YLOS, and number of inpatient visits in the year preceding the index admission for 30-day readmission as important factors to classify the patients into subgroups.

Conclusion: Although CART analysis did not improve the prediction of an early return to the hospital after stroke compared with logistic regression models, decision rules generated by CART can easily be interpreted and applied in clinical practice.

Keywords: Acute ischemic stroke, readmission, supervised learning, classification and regression tree, data mining, healthcare.

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