Key result
ARIC-HF score outperforms WATCH-DM and AI-ECG for detecting stage B heart failure in T2DM.
Why the study?
Cardiovascular risk in type 2 diabetes mellitus is heterogeneous, creating a need for an effective selection process to identify patients for echocardiographic screening of stage B heart failure. It was uncertain whether conventional clinical risk scores (ARIC-HF and WATCH-DM) or an artificial intelligence-enhanced ECG could improve patient selection.
Does an AI-enhanced ECG or conventional clinical risk scores (ARIC-HF, WATCH-DM) accurately identify stage B heart failure on echocardiographic screening in older patients with type 2 diabetes?
Observational (n=176)
Does an AI-enhanced ECG or conventional clinical risk scores (ARIC-HF, WATCH-DM) accurately identify stage B heart failure on echocardiographic screening in older patients with type 2 diabetes?
Answers are grounded in this paper and cite their sources.
Although the ARIC-HF clinical score outperformed both WATCH-DM and AI-ECG in screening older type 2 diabetes patients for stage B heart failure, none of the tools demonstrated sufficient diagnostic accuracy for reliable standalone use.
Background Early identification of stage B heart failure (SBHF) in individuals with type 2 diabetes mellitus (T2DM) may allow timely initiation of cardioprotective therapies and help prevent progression to symptomatic heart failure. However, cardiovascular risk in T2DM is heterogeneous, and a selection process for echocardiography would be desirable. Purpose To evaluate whether conventional clinical risk scores (ARIC-HF for the general population and WATCH-DM for individuals with diabetes) or an artificial intelligence–enhanced electrocardiogram (AI-ECG) could improve selection of patients for echocardiographic screening. Methods Clinical data, 12-lead AI-ECG and 2D echocardiography (2DE) were obtained prospectively in patients with T2DM, age >65 years enrolled in the CD-AIM trial. Risk of HF was quantified using the ARIC-HF and WATCH-DM risk scores. The AI-ECG score was derived from a continuous wavelet transform signal processing to provide new frequency and energy information to detect cardiac relaxation abnormalities. SBHF was defined as impaired global longitudinal strain (GLS<16%) or diastolic dysfunction (DD), defined as E/e’≥14 and LAVi >34 ml/m². Results Among 176 participants, ARIC-HF ranged from 0.03% to 25.52%, and WATCH-DM from 1 to 20. SBHF was identified in 46 (26.1%) patients. Impaired GLS was present in 38 (21.6%) participants, and DD in 46 (26.1%). Using optimised thresholds (ARIC-HF >2.2%, WATCH-DM ≥7, AI-ECG ≥0.61), all three tools showed high sensitivity but limited specificity (figure). ARIC-HF demonstrated the strongest diagnostic performance, with AUC values of 0.61–0.67 across endpoints, outperforming both WATCH-DM and AI-ECG (AUC 0.54–0.57). Conclusion ARIC-HF provided the strongest risk stratification among the three approaches, although its discrimination remained moderate. None of the investigated tools demonstrated sufficient accuracy for reliable SBHF detection alone. The limited diagnostic performance of AI-ECG indicates that further refinement is required before it can be reliably implemented as an independent screening tool.
No takes yet. Share an insight, caveat, or question.
Wegrzyn-Witek et al. (2026) conducted an observational in Type 2 diabetes mellitus (screening for stage B heart failure) (n=176). ARIC-HF risk score, WATCH-DM risk score, and AI-assisted 12-lead ECG (AI-ECG) vs. 2D echocardiography for stage B heart failure detection (impaired GLS <16% or diastolic dysfunction) was evaluated on Detection of stage B heart failure (defined as impaired global longitudinal strain <16% or diastolic dysfunction with E/e' ≥14 and LAVi >34 ml/m²) (AUC 0.61-0.67 (ARIC-HF) vs AUC 0.54-0.57 (WATCH-DM and AI-ECG)). The ARIC-HF score outperformed WATCH-DM and AI-ECG for detecting stage B heart failure in T2DM (AUC 0.61–0.67 vs 0.54–0.57), though all tools showed limited specificity.
Synapse has enriched 10 related papers. Consider them for comparative context: