Imagine having a crystal ball that could predict cognitive frailty in older adults. Well, researchers have been hard at work developing prediction models to do just that! A systematic review and critical appraisal of these models revealed some interesting findings. The models varied in terms of the factors they considered and how well they performed. It turns out that age and depression were the most commonly used predictors in these models. The models showed promising discriminative ability, with C-index or AUC values ranging from 0.71 to 0.97. However, there were some concerns about the methodological quality of these models, as all of them were rated as having a high risk of bias. Only two models were externally validated, highlighting the need for further validation in future research. Overall, these prediction models provide a glimpse into the future of cognitive frailty assessment, but more work needs to be done to improve their performance and ensure their accuracy. If you want to dive deeper into the details, check out the full article!

BackgroundSeveral 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.ObjectivesWe aimed to summarize and critically appraise the reported multivariable prediction models in older adults with CF.MethodsPubMed, 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).ResultsA 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.ConclusionThere 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.Systematic review registrationwww.crd.york.ac.uk/prospero, identifier CRD42022323591.

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