Determining Predictors for Intra-abdominal Septic Complications Following Ileocolonic Resection for Crohn's Disease-Considerations in Pre-operative and Peri-operative Optimisation Techniques to Improve Outcome.

BACKGROUND AND AIMS: Intra-abdominal septic complications [IASC] following ileocolonic resection for Crohn's disease are common. Determining risk factors for these complications can aid pre-operative and peri-operative strategies to reduced morbidity. This study aims to determine the incidence and predictors of intra-abdominal septic complications following ileocolonic resection for Crohn's disease. METHODS: A single-centre, retrospective study was conducted. The clinical case notes of patients with histopathologically proven Crohn's disease, who underwent an ileocolonic resection as a one-stage or two-stage procedure, were reviewed. The primary endpoint was the formation of intra-abdominal septic complications within a 30-day post-operative time frame. RESULTS: Overall 163 patients underwent 175 ileocolonic procedures. Post-operative intra-abdominal septic complications were demonstrated in 9% [13/142] of one-stage procedures and 12% [4/33] of two-stage procedures [p = 0.2]. Post-operative IASCs following a one-stage procedure demonstrated associations with smokers [p = 0.004], intraoperative abdominal sepsis [p = 0.005], concomitant upper gastrointestinal Crohn's [p = 0.015], the presence of peri-operative anaemia [p = 0.037], hypoalbuminaemia [< 25g/l] [p = 0.04], and histologically involved margins [p = 0.001]. Multivariate analysis demonstrated the presence of intra-abdominal sepsis (hazard ratio [HR] 8.6, 95% confidence interval [CI]: 1.2 60.1] and the use of peri-operative biologicals [HR 24.6, 95% CI: 2.0-298] as independent predictors of post-operative intra-abdominal septic complications. CONCLUSIONS: This study highlights specific variables that may be contributory to poor outcome. These findings may be important when optimising patients for surgery, as well as planning an appropriate operative strategy. Further prospective studies and a larger sample size are required to validate these findings.

Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks.

A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid-water distribution coefficients (K(d)) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK(a), molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated K(d) data, were incorporated as a training set within a developed artificial neural network to predict K(d) for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined K(d) are presented which showed excellent correlation (R(2) > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K(d) for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K(d) values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.