A significant proportion of patients with COVID-19 develop acute respiratory distress syndrome (ARDS) with high risk of death. The efficacy of veno-venous extracorporeal membrane oxygenation (VV-ECMO) for COVID-19 on longer-term outcomes, unlike in other viral pneumonias, is unknown. In this study, we aimed to compare the 6 month mortality of patients receiving VV-ECMO support for COVID-19 with a historical viral ARDS cohort. Fifty-three consecutive patients with COVID-19 ARDS admitted for VV-ECMO to the Royal Brompton Hospital between March 17, 2020 and May 30, 2020 were identified. Mortality, patient characteristics, complications, and ECMO parameters were then compared to a historical cohort of patients with non-COVID-19 viral pneumonia. At 6 months survival was significantly higher in the COVID-19 than in the non-COVID-19 viral pneumonia cohort (84.9% vs. 66.0%, p = 0.040). Patients with COVID-19 had an increased Murray score (3.50 vs. 3.25, p = 0.005), a decreased burden of organ dysfunction (sequential organ failure score score [8.76 vs. 10.42, p = 0.004]), an increased incidence of pulmonary embolism (69.8% vs. 24.5%, p < 0.001) and in those who survived to decannulation longer ECMO runs (19 vs. 11 days, p = 0.001). Our results suggest that survival in patients supported with EMCO for COVID-19 are at least as good as those treated for non-COVID-19 viral ARDS.

OBJECTIVES Because the mortality rate is very low in thoracic surgery, its use as a quality discriminator is limited. Acute kidney injury (AKI) is a candidate measure because it is associated with increased rates of morbidity and mortality and is partly preventable. The incidence of AKI after thoracic surgery is not well documented. We conducted an audit to determine the incidence and outcomes of AKI. This audit became a pilot project, and the results indicate the feasibility of a larger study. METHODS Retrospective data on renal function post-thoracic surgery were collected at a tertiary cardiothoracic unit over 12 months. Renal impairment was classified according to the Kidney Disease Improving Global Outcomes criteria. RESULTS Of 568 patients (mean = 59  ±  SD 18; 38% women), AKI was diagnosed in 86 (15.1%) within 72 h post-thoracic surgery based on the Kidney Disease Improving Global Outcomes staging system (stage 1, n = 55; stage 2, n = 25; stage 3, n = 6). Significant differences were found in postoperative length of stay (3 vs 5 days; P < 0.001) of patients with and without AKI. There was a significant difference between the age groups of patients with and without AKI (P < 0.05) in the open surgical group but not in the group having video-assisted thoracoscopic surgery (VATS). There was no significant difference in the mortality rates between patients with and without AKI. CONCLUSIONS The incidence of AKI after thoracic surgery was 15.1%. AKI was associated with longer hospital stays and was more likely in ≥60-year-old patients after open surgery than after VATS. Reducing AKI could improve patient outcomes. We propose that AKI may be a useful quality measure in thoracic surgery. We are developing a multicentre audit based on this approach.

No abstract

Background This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. Methods A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. Results Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. Conclusion ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.