Can We Predict Incisional Hernia?: Development of a Surgery-specific Decision-Support Interface.

Area of Science:

• Surgical outcomes research
• Predictive analytics in medicine
• Health informatics

Background:

• Incisional hernia (IH) complicates 10-15% of abdominal surgeries (AS), posing a significant challenge.
• Current methods lack readily available tools for predicting IH at the point-of-care.

Purpose of the Study:

• Identify procedure-specific risk factors for incisional hernia (IH).
• Develop a preoperative risk stratification tool for IH using a risk calculator app.

Main Methods:

• Retrospective analysis of 29,739 patients undergoing AS from 2005-2016.
• Regression analysis to generate predictive models for IH.
• Development of a surgeon-facing, point-of-care risk prediction app.

Main Results:

• The incidence of operative IH was 3.8% at an average follow-up of 57.9 months.
• Eight surgery-specific predictive models demonstrated excellent risk discrimination (C-statistic 0.76-0.89).
• Key risk factors included prior abdominal surgery and smoking history.

Conclusions:

• Incisional hernia (IH) is predictable after abdominal surgery (AS).
• Surgery-specific risk models and a risk calculator app can identify high-risk patients.
• The developed app facilitates point-of-care risk assessment for IH.