We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
脑卒中病人出院安置预测模型的系统评价.
- Authors
江雅倩; 汪晖; 屈聪蕙; 乐霄; 王怡萱
- Abstract
Objective: To systematically evaluate the prediction models for discharge disposition in stroke patients and provide a basis for clinical staff to choose an appropriate prediction model or develop a new one. Methods: The Cochrane Library, PubMed, EMbase(OVID), CINAHL, SinoMed, CNKI, and Wanfang Data were electronically searched to collect studies about prediction models of discharge disposition in stroke patients from inception to January 16, 2021 and manual search of references as a supplement. Two researchers independently searched and screened the literature, extracted the data, and used PROBAST to evaluate the risk of bias and applicability of the studies. Results: Totally 15 studies describing 14 prediction models were included. The AUC reported in the model ranged from 0. 724 to 0. 900. The predictors commonly reported include activity function, age, cognitive function, stroke type, comorbidities, care support, and stroke severity. 13 studies showed good applicability, but all models had a high risk of bias. Conclusions: The prediction model of discharge disposition in stroke patients is still in development. Most models have good applicability and high discrimination, but still exist the high risk of bias and lack of external validation. In the future, we should focus on the external validation and optimization of existing models, or develop new models with standardized guidance to reduce bias and facilitate clinical application.
- Subjects
MEDICAL databases; ONLINE information services; CINAHL database; MEDICAL information storage &; retrieval systems; SOCIAL support; SYSTEMATIC reviews; PHYSICAL activity; HUMANITY; SEVERITY of illness index; STROKE patients; PREDICTION models; MEDLINE; DISCHARGE planning; COMORBIDITY; EVALUATION
- Publication
Chinese Nursing Research, 2022, Vol 36, Issue 13, p2298
- ISSN
1009-6493
- Publication type
Article
- DOI
10.12102/j.issn.1009-6493.2022.13.008