ArticleBMC geriatrics2026
Development and independent external validation of a novel nomogram to predict cognitive frailty in older adult patients with heart failure.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Models for identifying cognitive frailty in hospitalized older adults with chronic heart failure in China: a systematic review.Frontiers in public health · 2026Pooled it
- Prevalence and associated factors of cognitive frailty in older adults with heart failure: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesTo develop and independently externally validate a novel, simple, and validated tool to assess the risk of cognitive frailty in older adult patients with heart failure based on these factors.
designA cross-sectional study. DATA SOURCES: A novel nomogram risk prediction model was developed by recruiting older adult patients with heart failure for data collection from October 2022 to August 2023 at a university general hospital in China. Independent external validation of the developed model was performed on patients collected from March 2023 to June 2023 at a university general hospital at the same level.
methodsUnivariate and multivariate logistic regression analyses were performed for variables that may influence the prevalence of cognitive frailty. The fit and predictive performance of the nomogram risk prediction model was evaluated based on the area under the characteristic curve of the subjects and decision curve analysis.
resultsThe overall prevalence of cognitive frailty in older adult patients with heart failure was 44.5%. The results showed that the predictive ability of the model development cohort was 0.807(95%CI = 0.7640.851, P<0.001). And the predictive ability of the validation cohort was 0.770(95%CI = 0.6670.874, P<0.001), indicating that the model has high accuracy.
conclusionsThe results showed satisfactory predictive performance. The use of this tool helps clinical caregivers to quickly and intuitively identify patients at high risk for the development of cognitive frailty on admission, thus providing evidence to support the early development and implementation of personalized strategies.
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