ArticleGerontology & geriatric medicine
Development and Validation of a Risk Prediction Model for Frailty in Patients with Chronic Diseases.
Article in Gerontology & geriatric medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Time-dependent metabolic effects of portion-control plate interventions in adults with chronic diseases: a systematic review and meta-analysis.BMC primary care · 2026Pooled it
- Construction and validation of oral frailty risk assessment model in elderly hospitalized patients.BMC oral health · 2026Article
- Development and Validation of a Predictive Model for the Risk of Postoperative Frailty in Elderly Patients With Benign Brain Tumors.Brain and behavior · 2026Article
- Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model Performance, and Clinical Translation Gap.Clinical interventions in aging · 2026Article
- Association between alpha-1-acid glycoprotein and frailty in US females aged 20-49 years: analysis of NHANES data (2015-2023).Scientific reports · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations.
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