Key result
Machine learning frailty rating from smartphone gait analysis links to ~60% higher all-cause mortality.
Why the study?
Frailty assessment guides management in elderly patients with heart failure, but most frailty scales are subjective with inter-rater variability.
Does a machine learning-based gait analysis model accurately predict the clinical frailty scale and associate with all-cause death in elderly patients with heart failure?
Observational (n=223)
Does a machine learning-based gait analysis model accurately predict the clinical frailty scale and associate with all-cause death in elderly patients with heart failure?
Effect estimate: HR 1.60 (95% CI 1.02-2.50)
A smartphone-based machine learning model using gait analysis can accurately predict clinical frailty scale scores, which independently associate with all-cause mortality in elderly heart failure patients.
May aid smartphone frailty screening in HF; hypothesis-generating pending prospective validation.
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.
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Mizuguchi et al. (2023) conducted an observational in Heart failure (n=223). Machine learning-based automatic rating of clinical frailty scale (CFS) was evaluated on All-cause death (HR 1.60, 95% CI 1.02-2.50). A machine learning-based automatic rating of the clinical frailty scale using smartphone gait analysis was independently associated with a higher risk of all-cause death (HR 1.60; 95% CI 1.02-2.50).
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