Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network

Chieh Chun Huang, Shih Hsien Sung, Wei Ting Wang, Yin Yuan Su, Chi Jung Huang, Tzu Yu Chu, Shao Yuan Chuang, Chern En Chiang, Chen Huan Chen, Chen Ching Lin*, Hao Min Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case–control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality along with rehospitalization. To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the hold-out cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF.

Original languageEnglish
JournalPhysical and Engineering Sciences in Medicine
DOIs
StateAccepted/In press - 2024

Keywords

  • Deep learning
  • Heart failure
  • Pressure waveform
  • Pulse wave analysis

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