DOI: 10.1161/circ.148.suppl_1.16327 ISSN: 0009-7322

Abstract 16327: Machine Learning-Based Prediction of Clinical Frailty Scale Based on Gait Analysis in Elderly Patients With Heart Failure

Motoki Nakao, Toshiyuki Nagai, Yoshifumi Mizuguchi, Yuki Takahashi, Takahiro Abe, SHIGEO KAKINOKI, Shogo Imagawa, Kenichi Matsutani, Takahiko Saito, Masashige Takahashi, Kenji Hirata, Takahiro Ogawa, Takuto Shimizu, Manabu Otsu, Kunihiro Chiyo, Toshihisa Anzai
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Background: 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: We prospectively examined 358 elderly (≥75 years) with symptomatic chronic HF patients (mean age 82.3±5.2 years, left ventricular ejection fraction 58 [IQR 45-67]%) from six sites between January 2019 and September 2022. The CFS was evaluated based on the modified Delphi method by 10 independent trained cardiologists as the gold standard. We obtained body-tracking motion data using OpenPose ® , a deep learning-based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the extremely randomized trees (ERT) model. To evaluate the performance of this model, we calculated Cohen’s weighted kappa and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. The studied patients were divided into derivation (from a university hospital) and validation (from other five sites) cohorts. Results: In the derivation (n = 181) and validation (n = 177) cohorts, the ERT model showed sufficient agreement between the actual and predicted CFSs (kappa 0.864, ICC 0.864 and kappa 0.796, ICC 0.796, respectively) ( Figure ). During a median follow-up period of 421 (IQR 280-616) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (HR 1.97, 95% CI 1.21-3.20) adjusted for the MAGGIC mortality risk prediction score, N-terminal pro-brain natriuretic peptide, and serum albumin levels.

Conclusions: Machine learning-based algorithms that automatically rate the CFS are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.

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