Files and links (2)
Published VersionCC BY V4.0, Open Access
Journal article
Development and validation of a physical frailty phenotype index-based model to estimate the frailty index
Diagnostic and Prognostic Research, Vol.7(1), pp.1-11
2023
PMID: 36941719
Abstract
Background:
The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors—an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.
Methods:
A sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI (“model-based PFP”). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.
Results:
The model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R2, 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen’s kw, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18–28%) net increase in FI-defined “prefrail/frail” participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.
Conclusion:
The developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.
Details
- Title
- Development and validation of a physical frailty phenotype index-based model to estimate the frailty index
- Authors
- Yong-Hao Pua (Corresponding Author) - Singapore General HospitalLaura Tay (Author) - Sengkang General HospitalRoss Allan Clark (Author) - University of the Sunshine Coast, Queensland, School of Health - Public HealthJulian Thumboo (Author) - Singapore General HospitalEe-Ling Tay (Author) - Sengkang General HospitalShi-Min Mah (Author) - Sengkang General HospitalPei-Yueng Lee (Author) - Singapore General HospitalYee-Sien Ng (Author) - Sengkang General Hospital
- Publication details
- Diagnostic and Prognostic Research, Vol.7(1), pp.1-11
- Publisher
- BioMed Central Ltd.
- Date published
- 2023
- DOI
- 10.1186/s41512-023-00143-3
- ISSN
- 2397-7523
- PMID
- 36941719
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Organisation Unit
- School of Health - Public Health
- Language
- English
- Record Identifier
- 99712279202621
- Output Type
- Journal article
Metrics
10 File views/ downloads
50 Record Views