Key result
An FDA-approved AI echocardiographic tool detected invasively confirmed HFpEF with 88% specificity and 86% PPV, but only 30% sensitivity and 33% NPV.
Why the study?
To externally validate an FDA-approved AI tool for detecting HFpEF using echocardiographic video clips against invasive haemodynamic criteria in patients with unexplained dyspnoea.
Does an FDA-approved AI echocardiographic tool accurately detect invasively defined HFpEF in patients referred for unexplained dyspnoea?
Cohort (n=47)
No
Does an FDA-approved AI echocardiographic tool accurately detect invasively defined HFpEF in patients referred for unexplained dyspnoea?
Answers are grounded in this paper and cite their sources.
Against gold-standard invasive exercise haemodynamics, the FDA-approved AI echocardiographic tool shows high specificity (88%-100%) and PPV (86%-100%) to rule in HFpEF and identify advanced haemodynamic derangements, but its low sensitivity (27%-30%) precludes its use to rule out HFpEF.
Introduction To externally validate an FDA-approved artificial intelligence (AI) tool for detecting heart failure with preserved ejection fraction (HFpEF) using echocardiographic video clips, comparing its performance to invasive haemodynamic criteria in a real-world referral cohort with unexplained dyspnoea. Methods We retrospectively analysed 47 patients who underwent transthoracic echocardiography (TTE) and right heart catheterization (RHC), including 28 with both rest and exercise haemodynamics. The AI model evaluated apical 4-chamber video data and classified outputs as HFpEF, no HFpEF, or non-diagnostic, without any other clinical data. The primary outcome was invasively defined HFpEF: pulmonary capillary wedge pressure (PCWP) ≥15 mmHg at rest or ≥25 mmHg with exercise. Secondary analysis used a PCWP/cardiac output (CO) slope >2 mmHg/L/min. We assessed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results Among patients with exercise RHC (n = 28), 71% met haemodynamic HFpEF criteria. The AI tool demonstrated sensitivity of 30%, specificity of 88%, PPV of 86%, and NPV of 33%. Using the PCWP/CO slope (n = 25), specificity and PPV were 100%, sensitivity 27%, and NPV 16%. AI-positive patients had significantly higher resting PCWP (20 vs 15 mmHg, P = .029), mean PA pressure (29 vs 24 mmHg, P = .02), and PVR (2.1 vs 1.3 WU, P = .002). In patients with indeterminate H2FPEF scores (n = 25), the AI correctly identified 80% of those with invasively confirmed HFpEF. Model performance was consistent across TTE-RHC intervals <365 and <90 days. Conclusions This AI model demonstrated high specificity and positive predictive value (PPV) for detecting HFpEF, reliably identifying patients with more advanced haemodynamic abnormalities. Its performance remained robust across variable intervals between transthoracic echocardiography (TTE) and right heart catheterization (RHC), and in patients with indeterminate clinical scores. Due to limited sensitivity, the tool is best utilized to enrich identification of patients with clearly abnormal haemodynamics rather than to exclude HFpEF, particularly in early or borderline cases. While broader use as a screening tool is promising, prospective validation studies are necessary to confirm its utility in general populations.
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Sachar et al. (2026) conducted a cohort in Suspected heart failure with preserved ejection fraction (HFpEF) with unexplained dyspnoea (n=47). FDA-approved artificial intelligence (AI) echocardiographic screening tool vs. Invasive haemodynamic criteria via right heart catheterization (and AI negative cohort) was evaluated on Invasively defined HFpEF (pulmonary capillary wedge pressure [PCWP] >=15 mmHg at rest or >=25 mmHg with exercise) (Sensitivity 30%, Specificity 88%, PPV 86%, NPV 33%, LR+ 2.4, LR- 0.8). An FDA-approved AI echocardiographic tool detected invasively confirmed HFpEF with 88% specificity and 86% PPV, but only 30% sensitivity and 33% NPV.
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