AIMC Topic: Postoperative Hemorrhage
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Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.
Heart, lung & circulation
Apr 2, 2025
BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and, most importantly, for clinical decision-making. This systematic review aims to ...
Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.
International journal of cardiology
Dec 22, 2024
BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead to varying degrees of bleeding, resulting in serious clinical events, including death. This study ai...
Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm.
Orthopaedics & traumatology, surgery & research : OTSR
Sep 3, 2024
INTRODUCTION: Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient's risk fact...
The Effects of Daytime Variation on Short-term Outcomes of Patients Undergoing Off-Pump Coronary Artery Bypass Grafting.
Journal of cardiothoracic and vascular anesthesia
Dec 30, 2023
OBJECTIVE: To evaluate the effects of time of surgery on the short-term outcomes of patients undergoing off-pump coronary artery bypass grafting (OPCABG).
Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery.
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...
Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.
Surgical endoscopy
Jun 13, 2023
BACKGROUND: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperat...
Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis.
Neurosurgical review
May 3, 2023
Robotic assistance has improved electrode implantation precision in stereoelectroencephalography (SEEG) for refractory epilepsy patients. We sought to assess the relative safety of the robotic-assisted (RA) procedure compared to the traditional hand-...
Risks and complications of robot-assisted radical prostatectomy (RARP) in patients receiving antiplatelet and/or anticoagulant therapy: a retrospective cohort study in a single institute.
Journal of robotic surgery
Oct 12, 2020
The objective of the study was to evaluate the risk of bleeding complications in patients undergoing robot-assisted radical prostatectomy (RARP) while taking antiplatelet (AP) and/or anticoagulant (AC) agents. We analyzed the data of 334 patients und...
Prediction of perioperative transfusions using an artificial neural network.
PloS one
Feb 24, 2020
BACKGROUND: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict...
Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.
JAMA network open
Jul 3, 2019
IMPORTANCE: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may improve clinical decisions aimed to reduce bleeding risk. Machine learning techniques, bolstered by better selection of variables, hold promise for enh...
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