Mizuguchi Yoshifumi, Nakao Motoki, Nagai Toshiyuki, Takahashi Yuki, Abe Takahiro, Kakinoki Shigeo, Imagawa Shogo, Matsutani Kenichi, Saito Takahiko, Takahashi Masashige, Kato Yoshiya, Komoriyama Hirokazu, Hagiwara Hikaru, Hirata Kenji, Ogawa Takahiro, Shimizu Takuto, Otsu Manabu, Chiyo Kunihiro, Anzai Toshihisa
Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan.
Department of Cardiology, Otaru Kyokai Hospital, Hokkaido, Japan.
Eur Heart J Digit Health. 2023 Dec 20;5(2):152-162. doi: 10.1093/ehjdh/ztad082. eCollection 2024 Mar.
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.
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 ( = 194) and validation ( = 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.
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.
尽管建议对老年心力衰竭（HF）患者进行衰弱评估以指导治疗策略和预测预后，但大多数衰弱量表是主观的，且评分在评估者之间存在差异。我们试图开发一种基于机器学习的针对HF患者的临床衰弱量表（CFS）自动评分方法/系统/模型。
我们前瞻性地研究了2019年1月至2023年10月期间来自7个中心的417例年龄≥75岁的有症状慢性HF老年患者。将患者分为推导队列（n = 194）和验证队列（n = 223）。我们使用基于深度学习的姿势估计库，通过智能手机摄像头获取身体跟踪运动数据。使用轻梯度提升机（LightGBM）模型从包括步态参数在内的128个关键特征计算预测的CFS。为了评估该模型的性能，我们计算了预测的和实际的CFS之间的科恩加权kappa（CWK）和组内相关系数（ICC）。在推导和验证数据集中，LightGBM模型在实际和预测的CFS之间显示出极好的一致性[CWK 0.866，95%置信区间（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]。在中位随访期391（四分位间距273 - 617）天期间，在调整了显著的预后协变量后，较高的预测CFS与全因死亡风险较高独立相关（风险比1.60，95% CI 1.02 - 2.50）。
基于机器学习的CFS自动评分算法是可行的，且预测的CFS与老年HF患者的全因死亡风险相关。
