AIMC Topic: Postoperative Complications

Clear Filters Showing 1 to 10 of 1043 articles

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

Journal of robotic surgery
The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly when complex cases are preferentially selected for the robotic platform. We evaluated a machine learni...

Machine learning-based cardiovascular risk calculator for non-cardiac surgery.

Open heart
BACKGROUND: Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least one cardiovascular risk factor. It is estimated that the 30-day mortality is between 0.5% and 2%.The main objective of this st...

Factors associated with complication of cranioplasty: CT-based risk assessment for early failure of autologous-bone cranioplasty.

Neurosurgical review
To determine whether preoperative noncontrast CT features predict early revision after autologous bone cranioplasty and to develop a simple CT-based risk framework. We retrospectively studied adults undergoing autologous cranioplasty at a single cent...

Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms.

BMC geriatrics
BACKGROUND: Although no definitive treatment exists, 30-40% of postoperative delirium cases are preventable through early risk identification and intervention. Therefore our aim was to develop and evaluate a postoperative delirium risk prediction mod...

Application of risk prediction model to evaluate the effect of mechanical ventilation on postoperative pulmonary complications in thoracic surgery.

European journal of medical research
OBJECTIVE: This retrospective cohort study aimed to systematically evaluate the impact of different mechanical ventilation strategies on postoperative pulmonary complications (PPCs) in thoracic surgery and to establish a risk prediction model that fa...

Development and validation of a predictive model for postoperative acute respiratory distress syndrome in patients with type A aortic dissection based on the 2023 updated definition.

Respiratory research
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in cl...

Towards an AI-driven registry for postoperative complications: a proof-of-concept study evaluating the opportunities and challenges of AI models.

BMJ health & care informatics
OBJECTIVES: Postoperative complications (PCs) require substantial resources to manage and are cumbersome to monitor. Artificial intelligence (AI), particularly natural language processing (NLP), offers a potential solution by automating and streamlin...

Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.

PloS one
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...

Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.

Scientific reports
Proximal junctional failure (PJF) is a significant mechanical complication following corrective surgery for adult spinal deformity (ASD), often resulting in structural failure at the uppermost instrumented vertebra and necessitating revision surgery....

Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study.

Scientific reports
Perioperative stroke significantly impacts postoperative outcomes. Current risk stratification methods for perioperative stroke prediction lack accuracy and practicality. We aimed to develop a machine learning (ML) model that improves both accuracy a...