Abstract
Background Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition
of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM).
Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution
waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs
to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG
features was superior to NT-proBNP, as well as a conventional screening tool—the Atherosclerosis Risk in Communities
(ARIC) HF risk score, in SBHF screening among patients with T2DM.
Methods Participants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal
strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography.
Supervised machine learning was adopted to identify the optimal combination of ewECG extracted
features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy
of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF.
Results SBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female)
and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area
under the curve (AUC) of 0.81 (95% CI 0.787–0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC
0.56, 95% CI 0.44–0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56–0.79, p = 0.002). ewECG features were also led
to robust models screening for DD (AUC 0.74, 95% CI 0.73–0.74), reduced GLS (AUC 0.76, 95% CI 0.73–0.74) and LVH
(AUC 0.90, 95% CI 0.88–0.89).
Conclusions Machine learning based modelling using additional ewECG extracted features are superior to NTproBNP
and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy
for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram
to confirm diagnosis.
Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 12617000116325
Link to full article: Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus | Cardiovascular Diabetology | Full Text (biomedcentral.com)
