Abdalla, Abdelmohaymin

ABSTRACTHepatopulmonary syndrome (HPS) poses a significant challenge in liver transplant recipients, mostly resulting in persistent hypoxaemia postoperatively. We present the case of a 59‐year‐old male with decompensated metabolic dysfunction associated with steatohepatitis (MASH) cirrhosis, who had liver transplantation due to end‐stage liver disease complicated by HPS‐related hypoxaemia. Despite successful transplantation, the patient continued to experience severe hypoxaemia, requiring high supplemental oxygen despite maximal conservative treatment per guidelines. Potential causes for hypoxaemia post‐liver transplant have been excluded; HPS as the sole cause of hypoxaemia was proved with a repeat shunt study, which estimated the shunt at 59%. Treatment with inhaled Treprostinil led to a remarkable improvement in oxygenation, facilitating the successful weaning of oxygen supplementation. The patient's hypoxaemia improved to full recovery upon discharge. This case highlights the persistence of hypoxaemia post‐liver transplantation in HPS patients and underscores the potential role of inhaled Treprostinil as a novel therapeutic approach to address this complication.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.

With the previous worldwide initial coronavirus disease 2019 (COVID-19) pandemic, a notable rise in spontaneous pneumomediastinum with/without pneumothorax (SPP) has been noted. Most cases were initially reported as complications secondary to barotrauma from mechanical ventilation (MV) with COVID-19. However, with the Delta strain, starting from December 2020, there have been multiple reports of SPP. The SPP is an uncommon complication outside use of assisted ventilation with either noninvasive positive pressure ventilation (NIPPV) or MV. COVID-19 has been linked to higher incidence of SPP without use of NIPPV or MV. We present a series of 5 cases with a polymerase chain reaction–confirmed COVID-19 diagnostic testing whose hospital course was complicated by SPP unrelated to the use of either NIPPV or MV.