Machine learning–based gait analysis to predict clinical frailty scale in elderly patients with heart failure
Yoshifumi Mizuguchi, Motoki Nakao, Toshiyuki Nagai, Yuki Takahashi, Takahiro Abe, Shigeo Kakinoki, Shogo Imagawa, Kenichi Matsutani, Takahiko Saito, Masashige Takahashi, Yoshiya Kato, Hirokazu Komoriyama, Hikaru Hagiwara, Kenji Hirata, Takahiro Ogawa, Takuto Shimizu, Manabu Otsu, Kunihiro Chiyo, Toshihisa Anzai
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: = 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.