Analysis of the clinical indications for opiate use in inflammatory bowel disease

Background/Aims: Opiate use for inflammatory bowel disease (IBD), particularly high-dose (HD) use, is associated with increased mortality. It's assumed that opiate use is directly related to IBD-related complaints, although this hasn't been well defined. Our goal was to determine the indications for opiate use as a first step in developing strategies to prevent or decrease opiate use.Methods: A retrospective cohort was formed of adults who were diagnosed with IBD and for whom outpatient evaluations from 2009 to 2014 were documented. Opiate use was defined if opiates were prescribed for a minimum of 30 days over a 365-day period. Individual chart notes were then reviewed to determine the clinical indication(s) for low-dose (LD) and HD opiate use.Results: After a search of the electronic records of 1,109,277 patients, 3,226 patients with IBD were found. One hundred four patients were identified as opiate users, including 65 patients with Crohn's and 39 with ulcerative colitis; a total of 134 indications were available for these patients. IBD-related complaints accounted for 49.25% of the opiate indications, with abdominal pain (23.13%) being the most common. Overall, opiate use for IBD-related complaints (81.40% vs. 50.82%; P=0.0014) and abdominal pain (44.19% vs. 19.67%; P=0.0071) was more common among HD than among LD.Conclusions: Our findings show that most IBD patients using opiates, particularly HD users, used opiates for IBD-related complaints. Future research will need to determine the degree to which these complaints are related to disease activity and to formulate non-opiate pain management strategies for patients with both active and inactive IBD

Classification and identification of soot source with principal component analysis and back-propagation neural network

Identification of soot sources is significant in fire investigation and forensic science. In this paper, principal component analysis (PCA) and a back-propagation (BP) neural network model have been used to classify and identify the soot samples from three different kinds of combustible material. Diesel, polystyrene and acrylonitrile butadiene styrene were burnt under the controlled combustion conditions in small-scale burn tests. Based on the matrix data from the GC-MS analysis data, two principal components have been obtained from PCA analysis with the cumulative energy content of 90.21%. Three different kinds of soot sample can be classified with 100% accuracy. A BP neural network model for predicting and identifying the soot source has been further developed. Accurate identification of the unknown samples has been achieved with this trained BP model. This pilot study indicates that PCA and BP neural network methods have potential in the analysis of soot to identify its principle pre-combustion source material. © 2013 Australian Academy of Forensic Sciences

Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis.

Background & aimsWe created and validated a clinical decision support tool (CDST) to predict outcomes of vedolizumab therapy for ulcerative colitis (UC).MethodsWe performed logistic regression analyses of data from the GEMINI 1 trial, from 620 patients with UC who received vedolizumab induction and maintenance therapy (derivation cohort), to identify factors associated with corticosteroid-free remission (full Mayo score of 2 or less, no subscore above 1). We used these factors to develop a model to predict outcomes of treatment, which we called the vedolizumab CDST. We evaluated the correlation between exposure and efficacy. We validated the CDST in using data from 199 patients treated with vedolizumab in routine practice in the United States from May 2014 through December 2017.ResultsAbsence of exposure to a tumor necrosis factor (TNF) antagonist (+3 points), disease duration of 2 y or more (+3 points), baseline endoscopic activity (moderate vs severe) (+2 points), and baseline albumin concentration (+0.65 points per 1 g/L) were independently associated with corticosteroid-free remission during vedolizumab therapy. Patients in the derivation and validation cohorts were assigned to groups of low (CDST score, 26 points or less), intermediate (CDST score, 27-32 points), or high (CDST score, 33 points or more) probability of vedolizumab response. We observed a statistically significant linear relationship between probability group and efficacy (area under the receiver operating characteristic curve, 0.65), as well as drug exposure (P < .001) in the derivation cohort. In the validation cohort, a cutoff value of 26 points identified patients who did not respond to vedolizumab with high sensitivity (93%); only the low and intermediate probability groups benefited from reducing intervals of vedolizumab administration due to lack of response (P = .02). The vedolizumab CDST did not identify patients with corticosteroid-free remission during TNF antagonist therapy.ConclusionsWe used data from a trial of patients with UC to develop a scoring system, called the CDST, which identified patients most likely to enter corticosteroid-free remission during vedolizumab therapy, but not anti-TNF therapy. We validated the vedolizumab CDST in a separate cohort of patients in clinical practice. The CDST identified patients most likely to benefited from reducing intervals of vedolizumab administration due to lack of initial response. ClinicalTrials.gov no: NCT00783718