Abstract Body (Do not enter title and authors here): Background:
Invasive hemodynamics are the gold standard for diagnosis of heart failure with preserved ejection fraction (HFpEF). A novel, FDA-approved artificial intelligence (AI) technology that uses a single, 4-chamber transthoracic echocardiogram (TTE) image to screen patients for HFpEF shows promise as a non-invasive tool to assist in diagnosis. Development of right ventricular (RV) dysfunction is a sign of a more advanced HFpEF. Advanced RV hemodynamic parameters, beyond pulmonary arterial pressures (PAP), have not been well studied in HFpEF. We sought to correlate advanced RV hemodynamic parameters in patients screened for HFpEF with this AI screening tool.

Method:
We retrospectively evaluated two cohorts of patients with suspected HFpEF that underwent TTE and RHC at our institution. The most recent TTE for each patient was screened using the AI-based analysis tool and was reported as either “suggestive” or “non-suggestive” of HFpEF – labeled as “positive” or “negative,” respectively. Mean PAP, pulmonary vascular resistance (PVR), pulmonary artery pulsatility index (PAPI), RV cardiac power output (RV-CPO), RV myocardial performance score (RV-MPS), and right atrial pressure to pulmonary capillary wedge pressure ratio (RA:PCWP) were calculated using invasive hemodynamic parameters at rest, and exercise when available. RV-CPO was calculated as [(mean PAP-RAP) x cardiac output] /451, and RV-MPS was calculated as (RV-CPO x PAP)x1.5. Median values were calculated. AI positive and negative groups were compared using Student’s t-test.

Results:
A total of 47 patients (82% women, 79% Black, average EF 62%) were included, with 23 undergoing subsequent exercise RHC. There were 18 (38%) that screened positive for HFpEF, and 29 (62%) screened negative by TTE AI software. Positive patients had a significantly higher mean PAP (median 31 vs 23 mmHg, p=0.01), PVR (2.1 vs 1.3 WU, p=0.02), and RV-CPO (0.26 vs. 0.17, p=0.04) than patients who were screened negative. There were no significant differences in PAPI, RV-MPS, and RA:PCWP at rest. There were no significant differences in mean PAP, PVR, PAPI RV-CPO, RV-MPS, or RA:PCWP with exercise.

Conclusion:
Patients screened positive for HFpEF by a novel AI TTE software had significantly higher PAP and RV-CPO at rest, but no differences in PAPI, RV-MPS, or RA:PCWP ratio. This tool may help identify more advanced HFpEF.