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Journal of Nursing ›› 2023, Vol. 30 ›› Issue (19): 45-50.doi: 10.16460/j.issn1008-9969.2023.19.045

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Risk prediction model for cognitive frailty in elderly: a systematic review

ZHOU Fei-yang1,2, DENG Lu1, LONG Ke-yu2, YANG Ting-ting2, XIE Lin-lin2, LV Qing1, GUO Chun-bo1   

  1. 1. Teaching and Research Office of Clinical Nursing, the Second Xiangya Hospital of Central South University, Changsha 410011, China;
    2. Xiangya Nursing School, Central South University, Changsha 410013, China
  • Received:2023-09-10 Online:2023-10-10 Published:2023-11-07

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Abstract: Objective To systematically review the risk prediction models of cognitive frailty in the elderly, and to provide evidence support for the risk prediction of cognitive frailty. Methods Systematic search of the literature regarding risk prediction models of cognitive frailty in the elderly in PubMed, Web of Science, the Cochrane Library, Embase, CINAHL, CNKI, Wanfang database, and SinoMed was conducted. The search time was from the inception of the database to August 2023, and the language was limited to Chinese and English. Literature screening was conducted according to the inclusion and exclusion criteria, and data extraction and quality evaluation of the included literature were carried out by two researchers independently. The information of author, year of publication, country, follow-up time, study object, study type, predictors, model construction method, and predictive efficacy was extracted. Results Sixteen studies were included, all of which were studies on model development. The prevalence of cognitive frailty in the elderly ranged from 4.8% to 65.3%. The applicability was good in included studies, but the overall risk of bias was high. Predictors reported repeatedly by multiple models were age, nutrition, activity of daily living and multimorbidity. Conclusion Risk prediction model of cognitive frailty in the elderly is still in the developmental stage, and some models have significant methodological flaws and a high risk of bias. Future research should focus on the establishment of comprehensive risk prediction models and their validation with the help of big data to form an informative risk warning system with high feasibility.

Key words: elderly people, cognitive frailty, prediction model, risk assessment, systematic review

CLC Number: 

  • R473.59
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