FEATURESImproving Shared Decision-making and Treatment Planning Through Predictive Modeling Clinical Insights on Ventral Hernia RepairKoszalinski, Rebecca S. PhD, RN, CRRN, CMSRN; Khojandi, Anahita PhD; Ramshaw, Bruce MD, FACSAuthor Information Author Affiliations: College of Nursing, University of Tennessee, Knoxville (Dr Koszalinski); College of Engineering, Industrial & Systems Engineering (Dr Khojandi); and Department of Surgery, University of Tennessee Graduate School of Medicine (Dr Ramshaw), Knoxville, TN. Dr Bruce Ramshaw has consulted and presented for WL Gore, Medtronic, Johnson & Johnson, Pacira Pharmaceuticals, Cumberland Pharmaceuticals, ConMed, and Atrium. There are no other disclosures. Corresponding author: Rebecca S. Koszalinski, PhD, RN, CRRN, CMSRN, College of Nursing, University of Tennessee, Knoxville, 1200 Volunteer Blvd, Room 231, Knoxville, TN 37996 (email@example.com). CIN: Computers, Informatics, Nursing: May 2020 - Volume 38 - Issue 5 - p 227-231 doi: 10.1097/CIN.0000000000000590 Buy Metrics Abstract Abdominal wall hernia repair, including ventral hernia repair, is one of the most common general surgical procedures. Nationally, at least 350 000 ventral hernia repairs are performed annually, and of those, 150 000 cases were identified as incisional hernias. Outcomes are reported to be poor, resulting in additional surgical repair rates of 12.3% at 5 years and as high as 23% at 10 years. Healthcare costs associated with ventral hernia repair are estimated to exceed $3 billion each year. Additionally, ventral hernia repair is often complex and unpredictable when there is a current infection or a history of infection and significant comorbidities. Accordingly, a predictive model was developed using a retrospectively collected dataset to associate the pre- and intra-operative characteristics of patients to their outcomes, with the primary goal of identifying patients at risk of developing complications a priori in the future. The benefits and implications of such a predictive model, however, extend beyond this primary goal. This predictive model can serve as an important tool for clinicians who may use it to support their clinical intuition and clarify patient need for lifestyle modification prior to abdominal wall reconstruction. This predictive model can also support shared decision-making so that a personalized plan of care may be developed. The outcomes associated with use of the predictive model may include surgical repair but may suggest lifestyle modification coupled with less invasive interventions. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.