Single-center ventral hernia repair with porcine dermis collagen implant.

INTRODUCTION: This study aims to evaluate the outcomes and utilization of porcine acellular dermal collagen implant (PADCI) during VHR at a large tertiary referral center. METHODS: Records of 5485 patients who underwent VIHR from June 1995 to August 2014 were retrospectively reviewed to identify patients >18 years of age who had VIHR with PADCI reinforcement. Use of multiple mesh reinforcement products, inguinal hernias, and hiatal hernias were exclusion criteria. The primary outcome was hernia recurrence, and secondary outcomes were early complications and surgical site occurrences (SSOs). Uni- and multivariate analyses assessed risk factors for recurrence after PADCI reinforced VIHR. RESULTS: There were 361 patients identified (54.5% female, mean age of 56.7 ± 12.5 years, and mean body mass index (BMI) of 33.0 ± 9.9 kg/m2). Hypertension (49.5%), diabetes (24.3%), and coronary artery disease (14.4%) were the most common comorbidities, as was active smoking (20.7%). Most were classified as American Association of Anesthesiologists (ASA) Class 3 (61.7%). Hernias were distributed across all grades of the ventral hernia working group (VHWG) grading system: grade I 93 (25.7%), grade II 51 (14.1%), grade III 113 (31.3%), and grade IV 6 (1.6%). Most VIHR were performed from an open approach (96.1%), and were frequently combined with concomitant surgical procedures (47.9%). Early postoperative complications (first 30 days) were reported in 39.0%, with 71 being SSO. Of the 19.7% of patients with SSO, there were 31 who required procedural intervention. After a mean follow-up of 71.5 ± 20.5 months, hernia recurrence was documented in 34.9% of patients. Age and male gender were predictors of recurrence on multivariate analysis. CONCLUSION: To the best of our knowledge, this is the largest retrospective single institutional study evaluating PADCI to date. Hernias repaired with PADCI were frequently in patients undergoing concomitant operations. Reinforcement with PADCI may be considered a temporary closure, with a relatively high recurrence rate, especially among patients who are older, male, and undergo multiple explorations in a short perioperative period.

Is it better to combine predictions?

We have compared the accuracy of the individual protein secondary structure prediction methods: PHD, DSC, NNSSP and Predator against the accuracy obtained by combing the predictions of the methods. A range of ways of combing predictions were tested: voting, biased voting, linear discrimination, neural networks and decision trees. The combined methods that involve 'learning' (the non-voting methods) were trained using a set of 496 non-homologous domains; this dataset was biased as some of the secondary structure prediction methods had used them for training. We used two independent test sets to compare predictions: the first consisted of 17 non-homologous domains from CASP3 (Third Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction); the second set consisted of 405 domains that were selected in the same way as the training set, and were non-homologous to each other and the training set. On both test datasets the most accurate individual method was NNSSP, then PHD, DSC and the least accurate was Predator; however, it was not possible to conclusively show a significant difference between the individual methods. Comparing the accuracy of the single methods with that obtained by combing predictions it was found that it was better to use a combination of predictions. On both test datasets it was possible to obtain a approximately 3% improvement in accuracy by combing predictions. In most cases the combined methods were statistically significantly better (at P = 0.05 on the CASP3 test set, and P = 0.01 on the EBI test set). On the CASP3 test dataset there was no significant difference in accuracy between any of the combined method of prediction: on the EBI test dataset, linear discrimination and neural networks significantly outperformed voting techniques. We conclude that it is better to combine predictions.