Machine Learning Model Identifies High-Risk Surgical Patients

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A machine learning model developed by researchers at the University of Pittsburgh and UPMC has been shown to accurately identify patients who are at high risk for complications after surgery. The model, which was trained on data from over 1.25 million surgical patients, was able to predict mortality and major adverse cardiac and cerebrovascular events (MACCE) with an accuracy of 97%.

The model, which is called the UPMC Preoperative Surgical Risk Predictor, is based on a gradient-boosted decision tree algorithm. It uses 132 different variables from the electronic health record to predict a patient’s risk of complications, including age, sex, race, comorbidities, and previous surgical history.

The model was validated against a set of 200,000 patients who underwent surgery at UPMC. The results showed that the model was able to identify patients who were at high risk of complications with an accuracy of 97%. This is significantly higher than the accuracy of the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator, which is currently the most widely used tool for predicting surgical risk.

The UPMC Preoperative Surgical Risk Predictor is currently being used at 20 UPMC hospitals. The model is expected to help clinicians identify patients who are at high risk of complications so that they can receive appropriate prehabilitation and monitoring. This could help to improve patient outcomes and reduce the risk of death after surgery.

The development of the UPMC Preoperative Surgical Risk Predictor is a significant advance in the field of surgical risk prediction. The model is more accurate than existing tools and could help to improve patient outcomes. The model is also flexible and can be used to predict risk for different types of surgery. This makes it a valuable tool for clinicians who are responsible for caring for surgical patients.

Machine Learning Model

Here are some of the benefits of using the UPMC Preoperative Surgical Risk Predictor:

  • It can help to identify patients who are at high risk of complications before surgery.
  • This allows clinicians to take steps to reduce the risk of complications, such as prehabilitation and monitoring.
  • This could help to improve patient outcomes and reduce the risk of death after surgery.
  • The model is more accurate than existing tools.
  • It is flexible and can be used to predict risk for different types of surgery.

The UPMC Preoperative Surgical Risk Predictor is a promising new tool that has the potential to improve patient outcomes. It is currently being used at 20 UPMC hospitals, and it is expected to be rolled out to other hospitals in the future.

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