ArticleAging clinical and experimental research2022
Nomogram for predicting the 12-year risk of ADL disability among older adults.
Article in Aging clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and external validation of a machine learning model for three-tier disability risk stratification in older adults.Frontiers in public health · 2026Article
- Assessment of disability in the older adults using electronic health record-based data: a machine learning approach.Frontiers in public health · 2026Article
- Lagged effects of risk factors on the disability of older adults: A distributed lag non-linear model approach.International journal of nursing sciences · 2026Article
- Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset.BMC geriatrics · 2025Article
- Observational
- Construction of disability risk prediction model for the elderly based on machine learning.Scientific reports · 2025Article
- Editor's note: Disability in older people during wars: the Ukrainian war.Aging clinical and experimental research · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPrevious studies have identified plenty of risk factors for activities of daily living (ADL). However, there are no reliable and widely available prediction models for ADL disability up to now. This study aimed to develop and validate a nomogram for predicting the 12-year risk of ADL disability in older adults.
methodsData from 4,809 participants in the English Longitudinal Study of Ageing (ELSA) and 18,620 participants in the Survey of Health, Ageing and Retirement in Europe (SHARE) were used as training set and validation set, respectively. We used the least absolute shrinkage and selection operator (LASSO) and Cox regression to screen the predictors and develop the nomogram. The P value, concordance index (C-index), integrated area under the ROC (receiver operating characteristic) curve (AUC) and calibration curves were used to validate the nomogram.
resultsDuring 12 years, 30.0% (n = 1,441) participants developed ADL disability in the training set, while the corresponding percentages were 18.5% in the validation set (n = 3,445). After screening, 13 variables were contained in the final prediction model. In ADL nomogram, the C-index and AUC were 0.744 ± 0.013 and 0.793 in internal valid ation, respectively, while in external validation, the C-index and AUC were 0.755 ± 0.009 and 0.796.
conclusionsThis study developed and validated a nomogram that predicts functional disability. The application of the predictive model could have important implications for patient prognosis and health care.
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