Key result
AI smartphone ECG app outperforms NT-proBNP in detecting LV dysfunction in sepsis with ~0.91 AUC.
Why the study?
Sepsis-induced LV dysfunction complicates resuscitation, while echocardiography is often delayed and NT-proBNP has limited diagnostic accuracy in this setting.
Does an AI-driven smartphone ECG analysis application accurately detect left ventricular dysfunction in adult patients with sepsis compared to NT-proBNP?
Observational (n=425)
Yes
Does an AI-driven smartphone ECG analysis application accurately detect left ventricular dysfunction in adult patients with sepsis compared to NT-proBNP?
Absolute Event Rate: 0.911% vs 0.637%
p-value: p=<0.001
An AI-driven smartphone ECG application provides a rapid, accessible, and highly accurate screening tool for detecting left ventricular dysfunction in sepsis patients, significantly outperforming standard NT-proBNP testing.
May support QCG-LVDys for LV dysfunction rule-out in ECG workflows; leaves open routine use pending prospective validation.
Background: Sepsis-induced left ventricular (LV) dysfunction complicates fluid resuscitation, increasing the risk of pulmonary edema and worsening outcomes.Echocardiographic assessment is often delayed, and natriuretic peptides such as N-terminal pro-B-type natriuretic peptide (NT-proBNP) have limited diagnostic accuracy in this setting.We evaluated a smartphone-based electrocardiogram (ECG) analysis application for screening LV dysfunction in patients with sepsis.Methods: In this multicenter retrospective observational study, we included 425 adult sepsis patients from three university hospitals who underwent both a 12-lead ECG and transthoracic echocardiography within 72 hours of emergency department admission.ECG images displayed on a monitor were photographed and analyzed using an artificial intelligence (AI)-based smartphone application (ECG Buddy), which generated a quantitative digital biomarker for LV dysfunction (quantitative electrocardiogram scores for LV dysfunction [QCG-LVDys]).LV dysfunction was defined as a left ventricular ejection fraction 40%.The diagnostic performance of QCG-LVDys for detecting LV dysfunction was evaluated using area under the receiver operating characteristic curve (ROC-AUC).A predefined comparison with NT-proBNP was performed in the single center where NT-proBNP was routinely measured.Results: In the overall multicenter cohort, QCG-LVDys showed high diagnostic performance for LV dysfunction, with an ROC-AUC of 0.901.Using an optimized threshold (37.35),QCG-LVDys achieved a sensitivity of 89.6%, specificity of 75.1%, and negative predictive value (NPV) of 97.5%.In the NT-proBNP subset, QCG-LVDys outperformed NT-proBNP (ROC-AUC 0.911 vs. 0.637, P < 0.001).Higher QCG-LVDys values were associated with increased lactate levels (P = 0.002) and 28-day in-hospital mortality (P < 0.001).Conclusion: AI-driven ECG analysis via a smartphone app provided a rapid, accessible, and accurate screening tool for LV dysfunction in sepsis and outperformed NT-proBNP.Its high NPV suggests potential utility for early risk stratification and for informing cautious hemodynamic management, although prospective studies are needed to define its role in sepsis treatment protocols.
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Park et al. (2026) conducted an observational in Sepsis (n=425). Smartphone-based ECG analysis application (ECG Buddy™) vs. NT-proBNP was evaluated on ROC-AUC for detecting left ventricular dysfunction (LVEF ≤ 40%) in the NT-proBNP subset (95% CI 0.869-0.953, p=<0.001). The AI-driven smartphone ECG application QCG-LVDys demonstrated high diagnostic performance for detecting left ventricular dysfunction in sepsis patients, significantly outperforming NT-proBNP (ROC-AUC 0.911 vs 0.637, p<0.001).
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