Observational studyJournal of Korean medical science2026
Screening for Left Ventricular Dysfunction in Sepsis Using a Smartphone ECG Analysis Application: A Multicenter Validation Study.
Observational study in Journal of Korean medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundSepsis-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.
methodsIn 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.
resultsIn 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,
conclusionAI-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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.