Construction and validation of cognitive frailty risk prediction model for elderly patients with multimorbidity in Chinese community based on non-traditional factors

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

Cognitive frailty (CF) is the conjunction of cognitive impairment without dementia and physical frailty. While predictors of each element are well-researched, mechanisms of their co-occurrence have not been integrated, particularly in terms of relationships between social, psychological, and biological factors. This interdisciplinary scoping review set out to categorise a heterogenous multidisciplinary literature to identify potential pathways and mechanisms of CF, and research gaps. Studies were included if they used the definition of CF OR focused on conjunction of cognitive impairment and frailty (by any measure), AND excluded studies on specific disease populations, interventions, epidemiology or prediction of mortality. Searches used Web of Science, PubMed and Science Direct. Search terms included "cognitive frailty" OR (("cognitive decline" OR "cognitive impairment") AND (frail*)), with terms to elicit mechanisms, predictors, causes, pathways and risk factors. To ensure inclusion of animal and cell models, keywords such as "behavioural" or "cognitive decline" or "senescence", were added. 206 papers were included. Descriptive analysis provided high-level categorisation of determinants from social and environmental through psychological to biological. Patterns distinguishing CF from Alzheimer's disease were identified and social and psychological moderators and mediators of underlying biological and physiological changes and of trajectories of CF development were suggested as foci for further research.

Prediction models for cognitive frailty in community-dwelling older adults: A scoping review

OBJECTIVES

This review investigates the current status of cognitive frailty risk prediction models for community-dwelling older adults, aiming to explore the shortcomings and provide insights for model optimisation.

METHODS

We adhered to the PRISMA guidelines for scoping review and followed the Joanna Briggs Institute Manual for Evidence Synthesis.

RESULTS

This article includes a total of 10 studies, revealing a prevalence of cognitive frailty ranging from 4.8 % to 39.6 %. The methods used for model construction included both logistic regression and machine learning. The predictors varied across the models, with age, education level, gender, and physical activity level being the most frequently cited factors.

CONCLUSIONS

While most models showed good applicability, all models displayed a high risk of bias. Future endeavors should concentrate on leveraging existing tools to ensure standardization in development and conducting rigorous evaluations of prediction models for cognitive frailty in community-dwelling older adults.

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

OBJECTIVE

This study sought to develop and validate a 6-year risk prediction model in older adults with cognitive frailty (CF).

METHODS

In the secondary analysis of Chinese Longitudinal Healthy Longevity Survey (CLHLS), participants from the 2011-2018 cohort were included to develop the prediction model. The CF was assessed by the Chinese version of Mini-Mental State Exam (CMMSE) and the modified Fried criteria. The stepwise regression was used to select predictors, and the logistic regression analysis was conducted to construct the model. The model was externally validated using the temporal validation method via the 2005-2011 cohort. The discrimination was measured by the area under the curve (AUC), and the calibration was measured by the calibration plot. A nomogram was conducted to vividly present the prediction model.

RESULTS

The development dataset included 2420 participants aged 60 years or above, and 243 participants suffered from CF during a median follow-up period of 6.91 years (interquartile range 5.47-7.10 years). Six predictors, namely, age, sex, residence, body mass index (BMI), exercise, and physical disability, were finally used to develop the model. The model performed well with the AUC of 0.830 and 0.840 in the development and external validation datasets, respectively.

CONCLUSION

The study could provide a practical tool to identify older adults with a high risk of CF early. Furthermore, targeting modifiable factors could prevent about half of the new-onset CF during a 6-year follow-up.

Prevalence and related factors of cognitive frailty in diabetic patients in China: a systematic review and meta-analysis

Objective

Cognitive frailty (CF) is characterized by physical frailty and potentially reversible cognitive impairment without Alzheimer's disease and other dementias. Clarifying the prevalence and related factors of cognitive frailty can help researchers understand its epidemiological status and formulate intervention measures. This study aims to conduct a systematic review and meta-analysis of the prevalence and related factors of CF in diabetic patients in Chinas to better understand the current status of CF in diabetic patients in China and develop effective intervention measures for related factors.

Methods

PubMed, Web of Science, Embase, Cochrane Library, CNKI, Weipu(VIP), WANFANG, China Biology Medicine (CBM) and DUXIU were searched to collect epidemiological data on Chinese diabetic patients. Articles published through May 29, 2023, were searched. The number of diabetes with CF and the total number of diabetes in the included studies were extracted to estimate the prevalence of diabetes with CF. For factors related to diabetes with CF, odds ratios (OR) and 95% confidence intervals (CI) were used for estimation.

Results

A total of 248 records were screened, of which 18 met the inclusion criteria. The results of meta-analysis showed that the prevalence of Chinese diabetic patients with CF was 25.8% (95% CI = 19.7 to 31.9%). Subgroup analysis showed that hospital prevalence was higher than in the community and in women than in men. Combined estimates showed that depression, malnutrition, advanced age (≥70, ≥80), combined chronic diseases ≥4 and glycated hemoglobin ≥8.5 were risk factors for CF in diabetics patients in China, with regular exercise and high education level (≥ college) as protective factors.

Conclusion

Cognitive frailty was common in diabetic patients in China. Such populations should be screened early and intervened with relevant factors.Systematic review registration: A systematic review of this study evaluated the registered websites as https://www.crd.york.ac.uk/PROSPERO/, CRD42023431396.

Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults

BACKGROUND

Cognitive frailty (CF) is a complex and heterogeneous clinical syndrome that indicates the onset of neurodegenerative processes and poor prognosis. In order to prevent the occurrence and development of CF in real world, we intended to develop and validate a simple and timely diagnostic instrument based on comprehensive geriatric assessment that will identify patients with potentially reversible CF (PRCF).

METHODS

750 community-dwelling individuals aged over 60 years were randomly allocated to either a training or validation set at a 4:1 ratio. We used the operator regression model offering the least absolute data dimension shrinkage and feature selection among candidate predictors. PRCF was defined as the presence of physical pre-frailty, frailty, and mild cognitive impairment (MCI) occurring simultaneously. Multivariate logistic regression was conducted to build a diagnostic tool to present data as a nomogram. The performance of the tool was assessed with respect to its calibration, discrimination, and clinical usefulness.

RESULTS

PRCF was observed in 326 patients (43%). Predictors in the tool were educational background, coronary heart disease, handgrip strength, gait speed, instrumental activity of daily living (IADL) disability, subjective cognitive decline (SCD) and five-times-sit-to-stand test. The diagnostic nomogram-assisted tool exhibited good calibration and discrimination with a C-index of 0.805 and a higher C-index of 0.845 in internal validation. The calibration plots demonstrated strong agreement in both the training and validation sets, while decision curve analysis confirmed the nomogram's efficacy in clinical practice.

CONCLUSIONS

This tool can effectively identify older adults at high risk for PRCF, enabling physicians to make informed clinical decisions and implement proper patient-centered individual interventions.