AIMC Topic: Postoperative Hemorrhage

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Evaluation of postoperative bleeding risk after dental extractions in patients on antithrombotic medication: A comparison of machine learning and clinical experience.

Clinical oral investigations
OBJECTIVES: The aim of this study was to identify high-risk dental extractions in patients taking antiplatelet (AP) medication or anticoagulants (ACs) and to compare an experienced surgeon's decisions with machine learning (ML) algorithms.

Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms.

BMC medical informatics and decision making
BACKGROUND: Traditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimens...

Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study.

BMJ open
OBJECTIVES: To develop and validate a machine learning model for precise risk stratification of postpancreaticoduodenectomy haemorrhage (PPH), enabling early identification of high-risk patients to guide clinical intervention.

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

Heart, lung & circulation
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
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
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
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
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
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
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-...