Machine learning–based gait analysis to predict clinical frailty scale in elderly patients with heart failure

Abstract

Abstract Aims Although frailty assessment is recommended for guiding treatment strategies and outcome prediction in elderly patients with heart failure (HF), most frailty scales are subjective, and the scores vary among raters. We sought to develop a machine learning–based automatic rating method/system/model of the clinical frailty scale (CFS) for patients with HF. Methods and results We prospectively examined 417 elderly (≥75 years) with symptomatic chronic HF patients from 7 centres between January 2019 and October 2023. The patients were divided into derivation (n = 194) and validation (n = 223) cohorts. We obtained body-tracking motion data using a deep learning–based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the light gradient boosting machine (LightGBM) model. To evaluate the performance of this model, we calculated Cohen’s weighted kappa (CWK) and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. In the derivation and validation datasets, the LightGBM models showed excellent agreements between the actual and predicted CFSs [CWK 0.866, 95% confidence interval (CI) 0.807–0.911; ICC 0.866, 95% CI 0.827–0.898; CWK 0.812, 95% CI 0.752–0.868; ICC 0.813, 95% CI 0.761–0.854, respectively]. During a median follow-up period of 391 (inter-quartile range 273–617) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (hazard ratio 1.60, 95% CI 1.02–2.50) after adjusting for significant prognostic covariates. Conclusion Machine learning–based algorithms of automatically CFS rating are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.