Little is known about the contribution of frailty in improving patient-level prediction beyond readily available clinical information. The objective of this study is to compare the predictive ability of 129 combinations of seven frailty markers (cognition, energy, mobility, mood, nutrition, physical activity, and strength) and quantify their contribution to predictive accuracy beyond age, sex, and number of chronic diseases.
Two cohorts from the Established Populations for Epidemiologic Studies of the Elderly were used. The model with the best predictive fit in predicting 6-year incidence of disability was determined using the Akaike Information Criterion. Predictive accuracy was measured by the C statistic.
Incident disability was 23% in one cohort and 20% in the other cohort. The "best model" in each cohort was found to be a model including between five and seven frailty markers including cognition, mobility, nutrition, physical activity, and strength. Predictive accuracy of the 129 models ranged from 0.73 to 0.77 across both cohorts. Adding frailty markers to age, sex, and chronic disease increased predictive accuracy by up to 3% in both cohorts (p
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