Diagnostic and Prognostic Research (Mar 2023)

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
https://doi.org/10.1186/s41512-023-00143-3
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 11

Abstract

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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-R 2, 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.

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