Machine Learning Models Predict Recurrence, Complications Associated with Hernia Repair

By Jonathan Springston, Editor, Relias Media

A novel machine learning algorithm recently helped surgeons better understand risks and outcomes associated with abdominal wall reconstruction and hernia repair.

Hundreds of thousands of these procedures are performed in the United States annually. However, despite its routine nature, this procedure can lead to complications, such as hospital readmissions, surgical site events, and the need for another surgery — all of which cost patients and health systems millions of dollars in excess spending.

Thus, researchers at the University of Texas MD Anderson Cancer Center in Houston studied data of more than 700 patients who underwent this procedure between March 2005 and June 2019. They were searching for complications that occurred 30 days after initial discharge: hernia recurrence, surgical site complications, and unplanned readmission.

The team developed machine learning algorithms to predict outcomes. These algorithms included information about characteristics of the operation, possible patient outcomes, demographics, and health conditions. The models were 85% accurate for predicting hernia recurrence, 84% accurate for predicting 30-day hospital readmission, and 72% accurate for predicting surgical site occurrence. After digging deeper, investigators uncovered common factors that contributed to complications, including obesity and the use of the bridged repair technique.

"It's really important for surgeons to understand what the risk factors are to abdominal wall reconstruction," Charles E. Butler, MD, FACS, senior author, said in a statement. "It is such a common problem that surgeons have to deal with in just about every subspecialty of surgery. It puts tremendous financial, emotional, and physical strains on the healthcare system and on the patients who are affected as well as the surgeons dealing with these problems."

Referencing a previously published paper, Abbas M. Hassan, MD, who worked with Butler and others on this machine learning study, noted a 1% reduction in hernia recurrence could save $30 million every year. "Reduction in complications is really one of the paramount goals of abdominal wall reconstruction," Hassan said. "Patients who develop a complication may require readmission or reoperation, and this results in increased morbidity and mortality and healthcare costs, as well as reduced quality of life. This becomes a critical concern when we care for patients with cancer who are immunocompromised."

For more on this and related subjects, be sure to read the latest issues of ED Management and Medical Ethics Advisor.