期刊
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
卷 29, 期 1, 页码 176-186出版社
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocab197
关键词
care transitions; clinical decision support; discharge planning; prediction models; nursing informatics
类别
资金
- National Institutes of Health/National Institute of Nursing Research [T32NR009356, F31NR019919, R01NR016014-03, R01NR0118196, 5R01NR018831-02, R01NR0118196-Suppl]
- Agency for Healthcare Research and Quality [R01HS26599-01, R01HS02659902S1, 1R01HS027742-01]
- National Institutes of Health/National Institute on Aging [R01AG056607, P30AG064105]
- Betty Irene Moore Fellowship for Nurse Leaders
This article presents a systematic review of studies on the development and validation of models predicting postacute care destination after adult inpatient hospitalization. The results indicate that model performance varies within and across populations, with most models demonstrating risk of bias. Further research should focus on ensuring rigorous variable selection and following TRIPOD guidelines for model development and validation.
Objective: This article reports a systematic review of studies containing development and validation of models predicting postacute care destination after adult inpatient hospitalization, summarizes clinical populations and variables, evaluates model performance, assesses risk of bias and applicability, and makes recommendations to reduce bias in future models. Materials and Methods: A systematic literature review was conducted following PRISMA guidelines and the Cochrane Prognosis Methods Group criteria. Online databases were searched in June 2020 to identify all published studies in this area. Data were extracted based on the CHARMS checklist, and studies were evaluated based on predictor variables, validation, performance in validation, risk of bias, and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Results: The final sample contained 28 articles with 35 models for evaluation. Models focused on surgical (22), medical (5), or both (8) populations. Eighteen models were internally validated, 10 were externally validated, and 7 models underwent both types. Model performance varied within and across populations. Most models used retrospective data, the median number of predictors was 8.5, and most models demonstrated risk of bias. Discussion and Conclusion: Prediction modeling studies for postacute care destinations are becoming more prolific in the literature, but model development and validation strategies are inconsistent, and performance is variable. Most models are developed using regression, but machine learning methods are increasing in frequency. Future studies should ensure the rigorous variable selection and follow TRIPOD guidelines. Only 14% of the models have been tested or implemented beyond original studies, so translation into practice requires further investigation.
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