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Pathophysiology of Vomiting01:22

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Vomiting is a complex physiological response to expel harmful or irritating substances from the body. It's a defensive mechanism triggered by stimuli like poisons, microbial toxins, cytotoxic drugs, and mechanical abdominal distension. The process is centrally coordinated by the vomiting (or emetic) center located in the medulla of the brainstem. This area, rich in muscarinic M1, histamine H1, neurokinin 1 (NK1), and serotonin 5-HT3 receptors, coordinates the act of vomiting through...
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Neurokinin 1 (NK1) receptors are distributed across the GI tract, vagal afferents, and key CNS regions including the central vomiting center and chemoreceptor trigger zone (CTZ) Chemotherapy agents stimulate enterochromaffin cells in the gastrointestinal (GI) tract to release large amounts of substance P (SP). SP is a neuropeptide released by specific sensory nerves in response to many different stressors, including those in the GI mucosa affected by chemotherapy.  SP binds and activates...
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Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

Maxim Glebov1, Teddy Lazebnik2,3, Maksim Katsin4

  • 1Department of Anesthesiology, Sheba Medical Center, Derech Sheba 2, Ramat Gan, 52621, Israel. hlebau@gmail.com.

BMC Anesthesiology
|March 21, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models significantly improve prediction of postoperative nausea and vomiting (PONV). These advanced tools offer better patient care and outcomes compared to traditional methods.

Keywords:
Clinical machine learningPersonalised medicinePostoperative nausea and vomiting prediction

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Area of Science:

  • Anesthesiology and Perioperative Medicine
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Postoperative nausea and vomiting (PONV) is a common complication after general anesthesia, causing patient distress.
  • Existing PONV prediction scores lack satisfactory accuracy.
  • There is a need for improved prognostic models for early and delayed PONV.

Purpose of the Study:

  • To develop and validate machine learning-based prognostic models for predicting early and delayed PONV.
  • To achieve higher predictive performance than current clinical scores.
  • To enhance personalized patient care and outcomes in the postoperative period.

Main Methods:

  • Retrospective analysis of 35,003 adult patients undergoing surgery under general anesthesia.
  • Development of an ensemble machine learning model using k-fold cross-validation.
  • Data splitting into training and testing sets, preserving sociodemographic features.

Main Results:

  • Early PONV occurred in 3.82% and delayed PONV in 18.80% of patients.
  • The proposed models achieved 83.6% accuracy for early PONV and 74.8% for delayed PONV.
  • Performance surpassed the Koivuranta score by 13.0% (early) and 10.4% (delayed), validated by feature importance analysis.

Conclusions:

  • Machine learning models provide superior prediction of PONV.
  • These models facilitate personalized postoperative care strategies.
  • Improved PONV prediction leads to better patient outcomes and satisfaction.