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Several prediction models for cognitive frailty (CF) in older adults have been developed. However, the existing models have varied in predictors and performances, and the methodological quality still needs to be determined.

We aimed to summarize and critically appraise the reported multivariable prediction models in older adults with CF.

PubMed, Embase, Cochrane Library, Web of Science, Scopus, PsycINFO, CINAHL, China National Knowledge Infrastructure, and Wanfang Databases were searched from the inception to March 1, 2022. Included models were descriptively summarized and critically appraised by the Prediction Model Risk of Bias Assessment Tool (PROBAST).

A total of 1,535 articles were screened, of which seven were included in the review, describing the development of eight models. Most models were developed in China (n = 4, 50.0%). The most common predictors were age (n = 8, 100%) and depression (n = 4, 50.0%). Seven models reported discrimination by the C-index or area under the receiver operating curve (AUC) ranging from 0.71 to 0.97, and four models reported the calibration using the Hosmer–Lemeshow test and calibration plot. All models were rated as high risk of bias. Two models were validated externally.

There are a few prediction models for CF. As a result of methodological shortcomings, incomplete presentation, and lack of external validation, the models’ usefulness still needs to be determined. In the future, models with better prediction performance and methodological quality should be developed and validated externally.