Temporal validation of a clinical prediction rule for distinguishing locomotive syndromes in community-dwelling older adults: A cross-sectional study from the DETECt-L study

Abstract

Objectives

Clinical prediction rules are used to discriminate patients with locomotive syndrome and may enable early detection. This study aimed to validate the clinical predictive rules for locomotive syndrome in community-dwelling older adults.

Methods

We assessed the clinical prediction rules for locomotive syndrome in a cross-sectional setting. The age, sex, and body mass index of participants were recorded. Five physical function tests–grip strength, single-leg standing time, timed up-and-go test, and preferred and maximum walking speeds–were measured as predictive factors. Three previously developed clinical prediction models for determining the severity of locomotive syndrome were assessed using a decision tree analysis. To assess validity, the sensitivity, specificity, likelihood ratio, and post-test probability of the clinical prediction rules were calculated using receiver operating characteristic curve analysis for each model.

Results

Overall, 280 older adults were included (240 women; mean age, 74.8 ± 5.2 years), and 232 (82.9%), 68 (24.3%), and 28 (10.0%) participants had locomotive syndrome stages ≥ 1, ≥ 2, and = 3, respectively. The areas under the receiver operating characteristics curves were 0.701, 0.709, and 0.603, in models 1, 2, and 3, respectively. The accuracies of models 1 and 2 were moderate.

Conclusions

These findings indicate that the models are reliable for community-dwelling older adults.