International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicti...
OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery.
Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and i...
The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
Dec 29, 2023
OBJECTIVE: Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. Ho...
Journal of cardiothoracic and vascular anesthesia
Dec 29, 2023
OBJECTIVES: To investigate the effect of retrograde autologous priming (RAP) on coagulation function using rotation thromboelastometry (ROTEM) in patients undergoing valvular cardiac surgery.
Journal of clinical monitoring and computing
Dec 27, 2023
Cardiac aortic surgery is an extremely complicated procedure that often requires large volume blood transfusions during the operation. Currently, it is not possible to accurately estimate the intraoperative blood transfusion volume before surgery. Th...
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...
The Journal of thoracic and cardiovascular surgery
Nov 29, 2023
BACKGROUND: The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac s...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.