Prediction models in hernia repair: a systematic review.

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BACKGROUND: Hernias can present with unique challenges when it comes to management and repair. Prediction models can be a useful tool for clinicians to better anticipate and understand the severity of a hernia, the type of surgical technique, or presurgical planning that may be required to treat the patient, and the risk of complications. Our goal is to evaluate and consolidate prediction models in hernia repair present in the literature for which physicians can reference to best improve patient outcomes and postoperative management.

METHODS: We performed a literature search in PubMed using keywords, "rectus width to defect width ratio," "predicting myofascial release," "computed tomography hernia repair prediction," "component separation radiology prediction hernia," "fat volume and hernia repair," "body morphometrics and Query hernia repair," "body morphometrics and reherniation," "computed tomography findings and risk of emergency hernia repair," "loss of domain and hernia radiology," and "volumetry and hernia repair." We searched for publications that used radiographic parameters to predict hernia severity, interventions, and outcomes.

RESULTS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we found twenty-three studies related to prediction models in hernia repair published between 2000 and 2021. We summarized studies pertaining to predicting acute care, predicting operative planning with loss of domain and component separation, predicting complications, paraesophageal hernia predictions, and predicting postoperative respiratory complications.

CONCLUSION: Radiographic prediction models can be an objective and efficient way for surgeons to analyze hernias and better understand a patient's situation so that they can inform patients about the best treatment options and the risk of complications.




Medicine and Health Sciences




Department of Surgery, USF-LVHN SELECT Program, USF-LVHN SELECT Program Students

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