AI tool guides management of elevated blood sugar during surgery

Patients whose blood glucose levels spike during surgery are at heightened risk for poor overall outcomes. A new AI tool has proven effective at predicting, prior to surgery, which patients will have the problem while under the knife.

Such informed anticipation can help surgical teams plan ahead for optimal resource allocation and targeted glucose management in the OR for these patients.

A study documenting the tool’s development, testing and suggested applications is running Methods of Information in Medicine.

Senior author Bala Nair, PhD, of the University of Washington in Seattle and colleagues built and validated several separate prediction tools using a dataset of perioperative records from more then 6,500 patients.

Comparing the tools against one another, the team found all those using machine learning were more accurate than a conventional linear regression model.

The best of the machine-learning algorithms, an extreme gradient boosting model, had the smallest median prediction error and the narrowest interquartile error range.

The researchers implemented this model as a web application called “Hyper-G” and demonstrated its usefulness at the point of care.  

“Machine learning models are able to accurately predict peak glucose levels during surgery,” Nair et al. concluded. “Implementation of such a model as a web-based application can facilitate optimal decision-making and advance planning of glucose management strategies.”

Dave Pearson

Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.

Around the web

With generative AI coming into its own, AI regulators must avoid relying too much on principles of risk management—and not enough on those of uncertainty management.

Cardiovascular devices are more likely to be in a Class I recall than any other device type. The FDA's approval process appears to be at least partially responsible, though the agency is working to make some serious changes. We spoke to a researcher who has been tracking these data for years to learn more. 

Updated compensation data includes good news for multiple subspecialties. The new report also examines private equity's impact on employment models and how much male cardiologists earn compared to females.

Trimed Popup
Trimed Popup