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Our top priority is providing value to members. Your Member Services team is here to ensure you maximize your ACS member benefits, participate in College activities, and engage with your ACS colleagues. It's all here.

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Clinical Updates

JACS Study Makes a Case for Machine Learning to Predict Ventral Hernia Complications

Machine learning (ML) models developed by surgeons at The University of Texas MD Anderson Cancer Center in Houston have shown a high level of accuracy in predicting which patients are most likely to have a hernia recurrence or other complications, according to a study in the Journal of the American College of Surgeons (JACS).

"We found that the machine learning algorithm, trained by using our own data, could accurately predict occurrence of complications after complex abdominal wall repair," said lead author Abbas M. Hassan, MD. "It was also able to identify factors associated with poor outcomes."

The ML models achieved the following accuracy rates:

  • 85% for predicting hernia recurrence
  • 72% for predicting surgical site occurrence
  • 84% for predicting 30-day hospital readmission

Dr. Hassan and colleagues said this study is the first to describe the use of ML to predict postoperative complications of abdominal wall reconstruction.

The study team, led by Charles E. Butler, MD, FACS, used the data from 725 patients to develop nine supervised ML algorithms that they found successfully predicted outcomes. The models considered patient demographics and characteristics, such as smoking status and other health conditions. The models also considered patient outcomes and characteristics of the operation itself, such as surgical technique.

Many patients experience discomfort and distress if they develop a hernia as well as if their hernia recurs after an operation to repair it fails. "Any information that we can have to help predict some of these adverse outcomes and potentially avoid or mitigate them will be a huge benefit to the patients, their outcomes, and to the financial well-being of the healthcare system," Dr. Butler said.

The authors said they believe the models can be improved and made more generalizable in subsequent iterations and are embarking on a multicenter study to validate the models and develop an integrated tool that uses these models as well as clinical and imaging data to provide a robust prediction tool. "Our hope is that this tool will be integrated in the future in the electronic medical record and mobile interfaces," Dr. Hassan said.

Read more, view an infographic, and watch an interview with Dr. Hassan.