An artificial intelligence echocardiographic tool demonstrated high specificity (88%) and positive predictive value (86%) but low sensitivity (30%) for detecting invasively defined HFpEF.
Observational (n=47)
Does an artificial intelligence echocardiographic tool accurately detect heart failure with preserved ejection fraction compared to invasive haemodynamics in patients with unexplained dyspnoea?
An AI echocardiographic tool demonstrated high specificity and positive predictive value for detecting HFpEF, making it useful for ruling in advanced haemodynamic abnormalities but limited for ruling out the condition due to low sensitivity.
Abstract 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.
Sachar et al. (Tue,) conducted a observational in Heart failure with preserved ejection fraction (HFpEF) (n=47). Artificial intelligence (AI) echocardiographic tool vs. Invasive haemodynamic criteria (right heart catheterization) was evaluated on Invasively defined HFpEF (PCWP ≥15 mmHg at rest or ≥25 mmHg with exercise). An artificial intelligence echocardiographic tool demonstrated high specificity (88%) and positive predictive value (86%) but low sensitivity (30%) for detecting invasively defined HFpEF.