Key result
Seismocardiography-based AnyHF score detects heart failure with ~82% diagnostic accuracy in suspected disease.
Why the study?
The study aimed to develop a seismocardiograph-based algorithm and assess its diagnostic performance in the detection of heart failure.
Does seismocardiography (SCG) accurately detect heart failure in patients referred for suspected heart failure?
Cross-Sectional (n=218)
Blinded to reference standard (TTE and blood sampling results)
Yes
Does seismocardiography (SCG) accurately detect heart failure in patients referred for suspected heart failure?
Seismocardiography is a feasible, non-invasive diagnostic tool that demonstrates high accuracy for detecting heart failure and offers significantly higher specificity for identifying HFrEF compared to NT-proBNP.
Answers are grounded in this paper and cite their sources.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“Heart failure and coronary artery disease are the two biggest reasons patients end up in front of a cardiologist, and this new research opens up a whole new frontier for our CADScor System. We built CADScor to give clinicians a fast, accurate, first-line tool for cardiac triage, and these results show we can bring that same value to heart failure, one of the most common and costly diagnoses in cardiology. That is a significant expansion of what CADScor can do for patients and for our business.”
SCG AnyHF score may aid HF detection in referred patients; hypothesis-generating until validated in prospective diagnostic studies.
The study aimed to develop a seismocardiograph (SCG)- based algorithm and assess its diagnostic performance in the detection of heart failure (HF). A total of 218 subjects were included: 198 with suspected HF and 20 with known HF with reduced ejection fraction (HFrEF) were included for testing only. Assessments were conducted using SCG, N-terminal pro b-type natriuretic peptide (NT-proBNP), electrocardiogram, NYHA classification and echocardiography. SCG-based algorithms, “AnyHF score” were developed to identify all subtypes of HF and “HFrEF-score” to identify HFrEF. Diagnostic accuracy was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver-operating characteristic curve (AUC-ROC). The AnyHF score demonstrated an AUC of 82%, sensitivity of 90.9%, specificity of 43.8%, NPV of 87.5% and PPV of 52.6% in detecting HF versus no HF. A balanced comparative analysis was performed between NT-proBNP and the HFrEF-score for detecting HFrEF versus no HF. NT-proBNP demonstrated an AUC of 94.7%, sensitivity 94.1%, specificity 68.8%, NPV 99%, and PPV 27.1%. The HFrEF-score showed an AUC of 92.9%, sensitivity 88.2%, specificity 92% (p < 0.001), NPV 98.4%, and PPV 57.7% (p = 0.007). The SCG-scores also categorized 71 patients (51%) from the no HF group, referred on suspicion of HF, as minimal risk of HF. This study suggests that SCG has potential to aid in the diagnostic process of HF. The SCG-based algorithms “AnyHF-score” and “HFrEF-score” demonstrated high diagnostic performance in identifying HF.
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Agam et al. (2026) conducted a cross-sectional in Suspected heart failure (n=218). Seismocardiography (SCG) vs. No heart failure (clinical diagnosis) was evaluated on Diagnostic accuracy (AUC-ROC) of the AnyHF score for detecting heart failure versus no heart failure (AUC 82%). The seismocardiography-based AnyHF score demonstrated an AUC of 82%, with 90.9% sensitivity and 43.8% specificity, for detecting heart failure in patients with suspected disease.
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