Frailty risk prediction models for patients undergoing maintenance hemodialysis in China: a systematic review
Abstract Objective To promote the application of high-quality frailty risk prediction models in the field of debilitation among Chinese patients undergoing MHD, and to provide a basis for optimisation and improvement of future studies. Methods A literature search was conducted in Chinese and English...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | BMC Nephrology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12882-025-03990-y |
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| Summary: | Abstract Objective To promote the application of high-quality frailty risk prediction models in the field of debilitation among Chinese patients undergoing MHD, and to provide a basis for optimisation and improvement of future studies. Methods A literature search was conducted in Chinese and English databases (PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang, VIP, SinoMed) and the cutoff date for which was April 30, 2024. Literature characteristics, types of studies, predictors, model construction methods and results were analysed and compared. Results Ten studies met the inclusion criteria, and seven were focused on model development and validation. A total of 12 predictive models were included across these 10 studies; three of these were solely model development studies, while seven were both model development and validation. The area under the curve (AUC) for the subjects’ operating characteristics was > 0.7 in all ten studies. The most frequently identified predictors in the models included age, nutritional status, the presence of multimorbidity, gender, and depression. While the overall applicability of the ten studies was deemed satisfactory, it is important to note that all studies exhibited a high risk of bias, particularly concerning the data analysis component. Conclusion The frailty risk prediction models for patients undergoing maintenance hemodialysis have demonstrated satisfactory applicability; however, they are all associated with a significant risk of bias and lack comprehensive external validation. To develop more accurate and practical prediction models, future studies must rely on large-sample, multicenter prospective cohort studies and adhere to a rigorous study design. Clinical trial number Not applicable. |
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| ISSN: | 1471-2369 |