Prediction model for cognitive frailty in older adults: A systematic review and critical appraisal

Abstract

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.

Understanding exogenous factors and biological mechanisms for cognitive frailty: a multidisciplinary scoping review

“…Such findings may allow targeted support to those at increased risk. Peng et al (2023) examined indicators of progression from multi-morbidity to CF in a community sample, emphasising factors that could be easily assessed: education, marital status, living alone, exercise, intellectual activity, social activity, fall history, and sleep were all predictors of CF.…”

“…Meta-analysis could only be conducted on older age and history of falls, and though factors including sociodemographic, health, and blood-brain alterations were explored, potential mechanisms were not. Huang et al, (2023) also reviewed CF prediction models, concluding that most included age, depression, physical exercise, education and chronic disease. Facal et al, (2019) focused on conceptual definitions of CF, the concept of brain and cognitive reserve, neuropathology, and important yet understated relationships between motor signs of ageing, cognitive functions and CF reversibility.…”

“…Meta-analysis could only be conducted on older age and history of falls, and though factors including sociodemographic, health, and blood-brain alterations were explored, potential mechanisms were not. Huang et al, (2023) also reviewed CF prediction models, concluding that most included age, depression, physical exercise, education and chronic disease.…”

Development and validation of prediction model for older adults with cognitive frailty

“…In order to identify individuals at high risk of CF and to facilitate the implementation of appropriate preventive measures and interventions [12][13][14], some prediction models have been developed [15][16][17]. There were some limitations in existed CF prediction models, such as selecting predictors based on univariable analysis and lacking calibration and external validation [15].…”

“…In order to identify individuals at high risk of CF and to facilitate the implementation of appropriate preventive measures and interventions [12][13][14], some prediction models have been developed [15][16][17]. There were some limitations in existed CF prediction models, such as selecting predictors based on univariable analysis and lacking calibration and external validation [15]. For instance, Peng et al [16] developed and internally validated a prediction model for diagnosing CF in elderly Chinese patients with multimorbidity, incorporating non-traditional factors.…”