Abstract Body (Do not enter title and authors here): Background: SimpleSense (Nanowear, New York, NY) is an FDA-cleared artificial intelligence (AI) based wearable diagnostic platform with multiparametric monitoring including 2-leads of ECG, thoracic impedance, heart sounds, blood pressure, posture and activity (Fig. 1 A). We explored the correlation between SimpleSense-derived and invasive hemodynamics (HD) parameters during right heart catheterization (RHC). The SimpleSense parameters assessed included amplitudes and width of heart sounds S1 and S2, ECG amplitudes, R to S1 and S2 times (Fig. 1 B), and pulse ejection period (PEP). PEP is a surrogate measurement that combines the pre-ejection period and left ventricular ejection time (Fig. 1 C).

Methods: A single-center prospective feasibility study was conducted after IRB approval. Adult patients >18 yrs undergoing RHC were screened and enrolled after informed consent (NCT05629533). SimpleSense recorded data on the patients throughout the RHC procedure. We performed an exploratory analysis to identify potential correlations between the SimpleSense data and HD parameters utilizing maching learning.

Results: Data from 11 subjects (8 Males, age:64.3 ± 10.23 yrs.) included 39 thermodilution cardiac outputs (CO) and 11 pulmonary artery pressures (PAP). The mean ± SD values of CO, cardiac index (CI), Systolic PAP, Diastolic PAP, and mean PAP were 4.4±1.9 l/min, 2.19±0.96 L/min/m², 37.27±17.32 mmHg, 12.09±5.03 mmHg, and 22.72±8.42 mmHg respectively. Multiple SimpleSense derived data show statistically significant correlations with HD parameters (Fig. 2). The PEP metric exhibited the highest correlation with mean PAP and systolic PAP.

Conclusion: The results highlight SimpleSense’s potential as a non-invasive AI tool that could revolutionize management by offering a reliable alternative to traditional invasive RHC. With further validation, SimpleSense may provide continuous, real-time hemodynamic information, significantly enhancing patient care and outcomes.