Development and validation of a physical frailty phenotype index-based model to estimate the frailty index

Yong-Hao Pua; Laura Tay; Ross Allan Clark; Julian Thumboo; Ee-Ling Tay; Shi-Min Mah; Pei-Yueng Lee; Yee-Sien Ng

doi:10.1186/s41512-023-00143-3

Development and validation of a physical frailty phenotype index-based model to estimate the frailty index

Diagn Progn Res. 2023 Mar 21;7(1):5. doi: 10.1186/s41512-023-00143-3.

Authors

Yong-Hao Pua^#^{1

2}, Laura Tay^#³, Ross Allan Clark⁴, Julian Thumboo^{5

6

7}, Ee-Ling Tay⁸, Shi-Min Mah⁸, Pei-Yueng Lee⁹, Yee-Sien Ng^{10

11

12}

Affiliations

¹ Department of Physiotherapy, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore. gmspuyh@nus.edu.sg.
² Medicine Academic Programme, Duke-NUS Graduate Medical School, Singapore, Singapore. gmspuyh@nus.edu.sg.
³ Department of General Medicine (Geriatric Medicine), Sengkang General Hospital, Singapore, Singapore.
⁴ School of Health and Behavioural Science, University of the Sunshine Coast, Sunshine Coast, Australia.
⁵ Medicine Academic Programme, Duke-NUS Graduate Medical School, Singapore, Singapore.
⁶ Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore.
⁷ Health Services Research & Evaluation, SingHealth Office of Regional Health, Singapore, Singapore.
⁸ Department of Physiotherapy, Sengkang General Hospital, Singapore, Singapore.
⁹ Organization Planning and Performance, Singapore General Hospital, Singapore, Singapore.
¹⁰ Geriatric Education and Research Institute, Singapore, Singapore.
¹¹ Duke-NUS Medical School, Singapore, Singapore.
¹² Department of Rehabilitation Medicine, Singapore General Hospital and Sengkang General Hospital, Singapore, Singapore.

^# Contributed equally.

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 k_w, 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.

Keywords: Frailty index; Frailty phenotype; Frailty scales; Geriatrics; Prediction.

Abstract

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