Title: Surgical repair of complex ventral hernia: Development and internal validation of a prognostic model to identify recurrence.
Funder: Department of Health and Social Care

Abstract: BACKGROUND: Ventral hernia develops in 20% of patients following abdominal surgery. Complex ventral hernias (CVH) measure >10cm, are exceedingly difficult to treat & recur commonly. CVH incidence is increasing dramatically due to the &ldquo;obesity epidemic&rdquo; but surprisingly little is known regarding individual patient factors that cause recurrence. A prognostic model to identify patients destined to recur would avoid surgical morbidity (especially prevalent in obese patients) & reduce healthcare costs. In 2012 ventral hernia surgery cost the NHS &pound;100m, much wasted by recurrence. AIM: To develop & internally validate a prognostic model to identify patients at substantial risk of CVH recurrence. INVESTIGATION PLAN: DESIGN: Prognostic model development with included predictors informed by prior systematic review. Subsequent model tuning & internal validation via individual patient data (IPD) from host organisation. SYSTEMATIC REVIEW: To inform this application we have performed a systematic review to identify individual patient factors associated with CVH recurrence following apparently curative surgery: PROSPERO registration CRD42016043071. 6917 abstracts yielded 625 full papers; 174 studies extracted (31 RCT; 52 prospective cohort studies; 91 retrospective case series), 13 systematic reviews. TARGET POPULATION: Adults undergoing CVH surgery with curative intent. OUTCOMES: Primary: To develop & internally validate a multivariable model predicting CVH recurrence. Secondary: A; To externally validate any existing model identified. B; To investigate predictive capability of preoperative CT scanning, which we believe is underutilised. RESEARCH PLAN: 1: Update prior systematic review to identify most promising predictors of CVH recurrence via meta-analysis of extracted 2x2 tables, supplemented by practice guidelines & expert panel. 2: Following ethical permission, predictors identified in Stage 1 extracted from local IPD: We have identified 1180 patients who underwent CVH repair at our specialist centre, contributing 233 recurrences at 1-year. We will also extract preoperative CT imaging data performed as standard care. 3: Externally validate any existing model(s) using available predictors from our IPD. 4: Develop two new models for CVH recurrence at one-year: Model 1 using predictors identified in stage 1; model 2 enhanced with CT data. STATISTICAL METHODS: Prognostic model development using multivariable logistic regression to identify the most promising 5 to 10 predictor variables. Backwards elimination employed for up to 5 predictors & the full dataset used for development & internal validation (200 bootstrap datasets. Multiple imputation accounting for missing covariates). Results expressed as calibration, discrimination, net-benefit, & sensitivity at fixed specificity. SAMPLE SIZE: Our IPD has 232 recurrence events in 1180 patients. Fixing variables already known as important (BMI, hernia size, diabetes) will enhance power. The CT model will use discriminant analysis for proof of concept & principal component analysis to investigate combinations of CT parameters, combined as a CT score included as two or three eigenscore variables. 200 bootstraps minimum, or until estimates stabilise. PPI: Our CVH patient co-applicant will ensure a patient-centric focus & facilitate access to patients & representative groups. TIMETABLE: 24 months. BENEFITS TO PATIENTS & NHS: CVH is encountered increasingly due to progressive abdominal surgery & obesity rates. A sufficiently predictive model would avoid both surgical morbidity/mortality & costs in those patients fated to recur. 2012 NHS costs for ventral hernia surgery were &pound;100m. EXPERTISE: We combine content expertise in CVH surgery & imaging with considerable statistical expertise in systematic review, meta-analysis, & prognostic model development. Halligan is NIHR Senior Investigator. Mallett has multiple systematic/methodological reviews, Cochrane, model development/validation, & international guideline experience for prognosis research (CHARMS, PROBAST). FUTURE RESEARCH: If sufficiently predictive, we can access IPD from UK & international networks for external validation of the model. We would seek further NIHR funding and evaluation can be rapid to accelerate NHS adoption in line with RfPB aspirations.

Lay abstract: One-third of individuals undergo abdominal surgery. In 20% the abdominal wall &ldquo;gives way&rdquo; creating a hole through which fat and organs spill into a bulge called a hernia. &ldquo;Complex ventral hernia&rdquo; (CVH) describes very large hernias. CVH is increasingly common, extremely debilitating, and very difficult to repair, especially when accompanied by obesity, diabetes and smoking. Surgery is complicated further by a confusing variety of potential procedures and meshes (used to bridge the gap); good evidence indicating which to use in an individual patient is lacking. While the NHS spends &pound;100M PA repairing CVH, 30% recur. Knowing what procedure to do when, how, and even when not to operate at all would be of enormous benefit to patients and the NHS. We have reviewed existing research and found it concentrates squarely on surgical technique rather than clinical decision-making. Little is known about what factors predict whether CVH will recur after surgery in individual patients. The best way to predict individual recurrence is by using a &ldquo;multivariable prognostic model&rdquo;. This is simply a combination of factors known about the patient that gives a score indicating the chance of repair success. In addition to technique and mesh choice, factors could include patients weight, smoking history, diabetes, and hernia size/content/fat-distribution on scanning. We have brought-together experts in CVH surgery, scanning, and statistics to develop a prediction model. We have identified 1180 CVH patients operated on by us, including 233 whose hernia recurred. Following ethical approvals, we will build a model using predictive factors identified from our prior review and both develop and test this model using data from these patients medical records. If the model then appears accurate at predicting CVH recurrence we will seek additional funding to develop and refine it further using data from patients from other NHS hospitals and beyond.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: ORAL AND GASTROINTESTINAL