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

Abstract

The aim of this study was to identify procedure-specific risk factors independently associated with incisional hernia (IH) and demonstrate the feasibility of preoperative risk stratification through the use of an IH risk calculator app and decision-support interface. IH occurs after 10% to 15% of all abdominal surgeries (AS) and remains among the most challenging, seemingly unavoidable complications. However, there is a paucity of readily available, actionable tools capable of predicting IH occurrence at the point-of-care. Patients (n = 29,739) undergoing AS from 2005 to 2016 were retrospectively identified within inpatient and ambulatory databases at our institution. Surgically treated IH, complications, and costs were assessed. Predictive models were generated using regression analysis and corroborated using a validation group. The incidence of operative IH was 3.8% (N = 1127) at an average follow-up of 57.9 months. All variables were weighted according to β-coefficients generating 8 surgery-specific predictive models for IH occurrence, all of which demonstrated excellent risk discrimination (C-statistic = 0.76-0.89). IH occurred most frequently after colorectal (7.7%) and vascular (5.2%) surgery. The most common occurring risk factors that increased the likelihood of developing IH were history of AS (87.5%) and smoking history (75%). An integrated, surgeon-facing, point-of-care risk prediction instrument was created in an app for preoperative estimation of hernia after AS. Operative IH occurred in 3.8% of patients after nearly 5 years of follow-up in a predictable manner. Using a bioinformatics approach, risk models were transformed into 8 unique surgery-specific models. A risk calculator app was developed which stakeholders can access to identify high-risk IH patients at the point-of-care.