Milrinone and INO both decrease pulmonary hypertension individually, and the combination produces additive effects. Combination therapy may produce potent and selective pulmonary vasodilation during the treatment of pulmonary hypertension.

Combination therapy with an intravenous inovasodilator and inhaled nitric oxide (NO) may be appropriate in patients with pulmonary hypertension and associated right ventricular failure. We examined whether dobutamine and inhaled NO would have additive pulmonary vasodilator effects in experimental pulmonary hypertension. Pulmonary hypertension was produced in anesthetized, mechanically ventilated rabbits by infusion of U46619, a thromboxane analogue. Dobutamine was administered in increasing doses (2.5-20 microg/kg/min) with and without inhaled NO (40 ppm). Dobutamine produced dose-dependent decreases in pulmonary vascular resistance (PVR) and mean arterial pressure (MAP) and increases in cardiac output (CO). Inhaled NO alone decreased pulmonary artery pressure (PAP) and PVR with no effect on MAP or CO. The effects of dobutamine and inhaled NO were additive, so that at each dose of dobutamine, inhaled NO decreased PAP and PVR with no effect on systemic hemodynamics. This study suggests that the combination of dobutamine and inhaled NO should produce additive pulmonary vasodilation in patients with pulmonary hypertension and associated right ventricular dysfunction.

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Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH Background/Introduction Reducing electrophysiological signal noise is essential for diagnosis, mapping and ablation, yet most approaches are suboptimal. Template matching requires libraries of known signal types, that are difficult to obtain. Beat averaging can reduce noise, yet cannot be applied to single beats and obscures beat-to-beat variations. Beat smoothing can lose critical and subtle signal features. We set out to use neural networks (NN) based on encoder-decoders, which are able to extract key signal features and hence reconstruct them without noise and artifact. Purpose We hypothesised that electrograms with varying sources of artifact can be denoised using autoencoder neural networks. We further hypothesised that this could be achieved in a small data set by developing the method in a larger dataset of related signals, then using transfer learning. We tested this approach for atrial monophasic action potentials (MAPs) that have verifiable shapes. Methods The NN was first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; fig 1.A): 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641, 21 patients, 67±5y, 13 women; fig D,F,H). Results The autoencoder was able to learn key features of MAPs, and hence reconstruct them without artifacts. NN learned ventricular MAPs with similarity coefficient 0.91±0.16, Pearson correlation 0.99± 0.01 (fig A) and learned key features (upstroke, triangular descent, terminus) to reduce noise (fig B-C). Applying this trained NN to atrial MAPs, the approach automatically eliminated ventricular artifact (fig E), high frequency noise (fig G), truncation (fig I), saturation and other artifacts. After fine-tuning, the NN reconstructed atrial MAPs with Pearson correlation = 0.99±0.01 (p<0.001). Conclusions Machine learned encoder-decoders are powerful tools that can automatically eliminate diverse types of noise in single beats by learning essential signal features. Transfer learning makes this possible without large datasets for training, even from signals in a different cardiac chamber. This approach may have far-reaching applications for mapping and ablation.