The Year in Cardiothoracic Transplant Anesthesia: Selected Highlights From 2022 Part I: Lung Transplantation.

These highlights focus on the research in lung transplantation (LTX) that was published in 2022 and includes the assessment and optimization of candidates for LTX, donor optimization, the use of organs from donation after circulatory death, and outcomes when using marginal or novel donors; recipient factors affecting LTX, including age, disease, the use of extracorporeal life support; and special situations, such as coronavirus disease2019, pediatric LTX, and retransplantation. The remainder of the article focuses on the perioperative management of LTX, including the perioperative risk factors for acute renal failure (acute kidney injury); the incidence and management of phrenic nerve injury, delirium, and pain; and the postoperative management of hyperammonemia, early postoperative infections, and the use of donor-derived cell-free DNA to detect rejection.

Evaluation of machine learning models as decision aids for anesthesiologists.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

Successful recovery after amniotic fluid embolism in a patient undergoing vacuum-assisted vaginal delivery.

Amniotic fluid embolism (AFE) is a rare, catastrophic emergency that requires prompt recognition and treatment. Despite early recognition and supportive therapy, the morbidity and mortality remain high. We report a case of AFE after vacuum-assisted vaginal delivery resulting in hemodynamic collapse and subsequent multiorgan failure. Management included mechanical ventilation, extracorporeal membrane oxygenation, and continuous veno-venous hemodialysis. The patient was able to make a full recovery with minimal sequelae. In AFE with multiorgan failure, extracorporeal membrane oxygenation and continuous veno-venous hemodialysis can be valuable therapies. Proper management requires effective communication and the combined efforts of physicians of several disciplines.