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Abdominal Wall Reconstruction Risk Stratification Tools: A Systematic Review of the Literature
Karla Bernardi1, Gina L Adrales1, William W Hope1
1From the Surgery Department, Center for Surgical Trials and Evidence-Based Practice, University of Texas Health Science Center at Houston; and the Surgery Departments of John Hopkins University School of Medicine, New Hanover Regional Medical Center, University of Iowa, University of Nevada School of Medicine, Oregon Health and Science University, University of Kentucky Medical Center; and Ventral Hernia Outcomes Collaborative.
Background:
Ventral hernias are a common pathology encountered by surgeons. Multiple risk stratification tools have been developed in attempts to predict a patient's postoperative risk for complication. The aim of this systematic review was to identify published stratification tools, to assess their generalizability, and develop an ensemble risk score model.
Methods:
A systematic review of the literature was performed using PubMed and following the PRISMA guidelines. Two independent reviewers identified articles describing hernia stratification tools or validating an established tool. Inclusion criteria included articles that studied ventral hernia risk score models developed through expert consensus or from data of at least 500 subjects, performed a multivariable analysis of at least 500 patients, or assessed a previously reported model. Studies were grouped by primary outcome, and the odds ratios for correlated variables were compiled. Outcomes described in 4 or more articles were then stacked to generate a cumulative risk score model for patients undergoing abdominal wall repair.
Results:
A total of 20 articles were found to meet our inclusion criteria and used to develop our ensemble model. Surgical-site infection, surgical-site occurrence, and hernia recurrence were the 3 primary outcomes used to calculate our stacked cumulative risk stratification score.
Conclusions:
There are multiple risk score tools published; however, all have their strengths and limitations. For this reason, we created a composite score model with data from major articles to predict a patient's risk for postoperative complications. This model aims to ease the shared-decision making process for patients, surgeons, and institutions.
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