AIMC Topic: Postoperative Complications
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Machine Learning Algorithms Exceed Comorbidity Indices in Prediction of Short-Term Complications After Hip Fracture Surgery.
The Journal of the American Academy of Orthopaedic Surgeons
Nov 19, 2024
BACKGROUND: Hip fractures are among the most morbid acute orthopaedic injuries often due to accompanying patient frailty. The purpose of this study was to determine the reliability of assessing surgical risk after hip fracture through machine learnin...
Preoperative prediction of post hepatectomy liver failure after surgery for hepatocellular carcinoma on CT-scan by machine learning and radiomics analyses.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Nov 15, 2024
INTRODUCTION: No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learnin...
Predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma: nomograms based on deep learning analysis of gadoxetic acid-enhanced MRI.
European radiology
Nov 12, 2024
OBJECTIVES: This study aimed to develop nomograms for predicting post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC), using deep learning analysis of Gadoxetic acid-enhanced hepatobiliary (HBP) MRI.
Development and Validation of a Machine Learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days.
World neurosurgery
Nov 11, 2024
BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction model...
Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery.
International journal of medical informatics
Nov 9, 2024
BACKGROUND: The assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional analyses often fall short in timely and systematic identification of clinical quality issues. This stu...
Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.
The spine journal : official journal of the North American Spine Society
Nov 4, 2024
BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore cruc...
Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.
International urology and nephrology
Nov 2, 2024
PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted kidney and the quality of life of patients. This study aims to construct a machine learning model for ...
The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.
World journal of urology
Nov 1, 2024
PURPOSE: To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.
Leveraging machine learning to enhance postoperative risk assessment in coronary artery bypass grafting patients with unprotected left main disease: a retrospective cohort study.
International journal of surgery (London, England)
Nov 1, 2024
BACKGROUND: Risk stratification for patients undergoing coronary artery bypass surgery (CABG) for left main coronary artery (LMCA) disease is essential for informed decision-making. This study explored the potential of machine learning (ML) methods t...
Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: a retrospective cohort study.
International journal of surgery (London, England)
Nov 1, 2024
BACKGROUND: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction mo...
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