Epidural Analgesia Provides Better Pain Management After Live Liver Donation: A Retrospective Study

Despite the increase in surgical volumes of live liver donation, there has been very little documentation of the postoperative pain experience. The primary aim of this study was to examine the difference in acute postoperative pain intensity and adverse effects between patients who received intravenous patient-controlled analgesia (IV PCA) or patient-controlled epidural analgesia (PCEA) for pain control after live liver donation surgery. A retrospective chart review was performed of 226 consecutive patients who underwent right living donor hepatic surgery at the Toronto General Hospital, Toronto, Canada. Patients who received as their primary postoperative analgesic modality IV PCA (n = 158) were compared to patients who received PCEA (n = 68). Demographic profiles for the 2 groups were similar with respect to age, sex, and body mass index at the time of surgery. For the first 3 postoperative days, pain intensity was significantly lower in patients who received epidural analgesia (P 4) was reported more frequently in the IV PCA group (P < 0.05) along with increased sedation (P < 0.05). Pruritus was reported more frequently in the PCEA group of patients compared to the IV PCA group (P < 0.05). Significant between-group differences were not found for the incidence of postoperative vomiting, the time at which patients began fluid intake, the time to initial ambulation, or the length of hospital stay. In conclusion, epidural analgesia provides better postoperative pain relief, less sedation, but more pruritus than IV PCA after live liver donation

Methods for identifying health state transitions from administrative data: the case of metastasis in prostate cancer

Introduction Health administrative data are a rich source of population-based information, useful for building state transition models for medical decision making. These models require identification of health state transitions and associated times. Indirect methods are needed to predict this information, as it is rarely available in administrative data. Objectives and Approach We considered a set of criteria to identify transitions to metastasis for prostate cancer patients in administrative data, utilizing dates of diagnostic and medical billing codes for secondary malignancy, palliative radiation therapy, chemotherapy and bone disorders or procedures. We evaluated the criteria using the true date of metastasis from medical charts of 195 patients linked to health care administrative data in Ontario, Canada. We also built a recursive partitioning tree to optimally combine these criteria and construct rules for identifying metastatic patients. For the evaluation, both misclassification and discrepancy between true and predicted dates for the true positives were considered. Results Criteria involving chemotherapy drugs or hospital visits with secondary malignancy ICD10 diagnosis gave the best results, with high sensitivity and specificity. Criteria involving bone related problems, radiation therapy or diagnosis of metastatic cancer in physician billing data were very specific but not sensitive. The criterion involving prescriptions for narcotics was sensitive but not specific. The fitted tree was parsimonious involving only two of the criteria, while improving the accuracy over individual criteria. Most criteria gave a “delayed” prediction, with criterion based on chemotherapy giving on average the smallest delay, as well as exhibiting the least variability. Criteria involving narcotics and bone related problems predicted metastasis date very prematurely, probably triggered by conditions other than prostate cancer. Conclusion/Implications Several criteria from administrative databases satisfactorily classified prostate cancer patients with metastasis. A classification tree was built and improved the results over single criteria, demonstrating the added benefits in using advanced statistical learning methods for this task. However, “transition to metastasis” dates were predicted inaccurately, often with significant delay

Patient and practice level factors associated with seasonal influenza vaccine uptake among at-risk adults in England, 2011 to 2016:An age-stratified retrospective cohort study

We sought to gain insights into the determinants of seasonal influenza vaccine (SIV) uptake by conducting an age-stratified analysis (18-64 and 65+) of factors associated with SIV uptake among at-risk adults registered to English practices. Records for at-risk English adults between 2011 and 2016 were identified using the Clinical Practice Research Datalink database. SIV uptake was assessed annually. The associations of patient, practice, and seasonal characteristics with SIV uptake were assessed via cross-sectional and longitudinal analyses, using mixed-effects and general estimating equation logistic regression models. Overall SIV uptake was 35.3% and 74.0% for adults 18-64 and 65+, respectively. Relative to white patients, black patients were least likely to be vaccinated (OR18-64: 0.82 (95% CI: 0.80, 0.85); OR65+: 0.59 (95% CI: 0.56, 0.62)), while Asian patients among 18-64 year olds were most likely to be vaccinated (OR18-64: 1.10 (95% CI: 1.07, 1.13)). Females were more likely than males to be vaccinated among 18-64 year olds (OR18-64: 1.19 (95% CI: 1.18, 1.20)). Greater socioeconomic deprivation was associated with decreased odds of uptake among older patients (OR65+: 0.74 (95% CI: 0.71, 0.77)). For each additional at-risk condition, odds of uptake increased (OR18-64: 2.33 (95% CI: 2.31, 2.36); OR65+: 1.39 (95% CI: 1.38, 1.39)). Odds of uptake were highest among younger patients with diabetes (OR18-64: 4.25 (95% CI: 4.18, 4.32)) and older patients with chronic respiratory disease (OR65+: 1.60 (95% CI: 1.58, 1.63)), whereas they were lowest among morbidly obese patients of all ages (OR18-64: 0.68 (95% CI: 0.67, 0.70); OR65+: 0.97 (95% CI: 0.94, 0.99)). Prior influenza season severity and vaccine effectiveness were marginally predictive of uptake. Our age-stratified analysis uncovered SIV uptake disparities by ethnicity, sex, age, socioeconomic deprivation, and co-morbidities, warranting further attention by GPs and policymakers alike

Pregabalin reduces postoperative opioid consumption and pain for 1 week after hospital discharge, but does not affect function at 6 weeks or 3 months after total hip arthroplasty

BACKGROUND: This study examined whether a perioperative regimen of pregabalin added to celecoxib improved pain scores and functional outcomes postdischarge up to 3 months after total hip arthroplasty (primary outcome) and acute postoperative pain and adverse effects (secondary outcomes). METHODS: One hundred and eighty-four patients were enrolled in a randomized, double-blind, placebo-controlled study. Two hours before receiving a spinal anaesthetic and undergoing surgery, patients received celecoxib 400 mg p.o. and were randomly assigned to receive either pregabalin 150 mg p.o. or placebo p.o. After surgery, patients received pregabalin 75 mg or placebo twice daily in hospital and for 7 days after discharge. Patients also received celecoxib 200 mg every 12 h for 72 h and morphine i.v. patient-controlled analgesia for 24 h. Pain and function were assessed at baseline, 6 weeks, and 3 months after surgery. RESULTS: There was no difference between groups in physical function or incidence and intensity of chronic pain 3 months after total hip arthroplasty. The pregabalin group used less morphine [mean (sd): 39.85 (28.1) mg] than the placebo group [54.01 (31.2) mg] in the first 24 h after surgery (P<0.01). Pain scores were significantly lower in the pregabalin group vs the placebo group on days 1-7 after hospitaldischarge, and the pregabalin group required less adjunctive opioid medication (Percocet) 1 week after hospital discharge (P<0.05). CONCLUSIONS: Perioperative administration of pregabalin did not improve pain or physical function at 6 weeks or 3 months after total hip arthroplasty. Perioperative administration of pregabalin decreased opioid consumption in hospital and reduced daily pain scores and adjunct opioid consumption for 1 week after discharge.Department of Anaesthesia at the University of Toronto (Merit Awards to H.C. and C.M.); Canadian Institute of Health Research Fellowship (to H.C.); Canada Research Chair in Health Psychology at York University (to J. Katz); Pfizer Canada (physician-initiated peer-reviewed Neuropathic Pain Competition)

Prediction of Drosophila melanogaster gene function using Support Vector Machines

Abstract Background While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross‐validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un‐annotated genes. A total of approximately 5043 different genes, or about one‐third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un‐annotated. Results 39 Gene Ontology Biological Process (GO‐BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO‐BP term for 1422 previously un‐annotated genes or about 77% of the un‐annotated genes represented on the microarray and about 19% of all of the un‐annotated genes in the D. melanogaster genome. Conclusions Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure

The association between immune checkpoint or BRAF/MEK inhibitor therapy and uveitis in patients with advanced cutaneous melanoma

Background Treatment with immune checkpoint and BRAF/MEK inhibitors has significantly improved the survival of patients with advanced cutaneous melanoma and other metastatic malignancies. Therapy-related uveitis is a rare ocular adverse event, which may potentially lead to legal blindness. The epidemiology of treatment-related uveitis is currently insufficiently known. Patients and methods In this cohort study, we asked whether exposure to either immune checkpoint or BRAF/MEK inhibitors was associated with a higher risk of developing uveitis compared with the general population. Based on a Bayesian framework, we estimated the probability of developing uveitis with a right-censored, exponential survival model using data from the Zurich Melanoma Registry. The registry included all adult patients treated for advanced cutaneous melanoma between January 2008 and December 2018 at the University Hospital of Zurich, Switzerland. Results In total, 304 patients (64%) were treated with immune checkpoint and 186 patients (38%) with BRAF/MEK inhibitors. Median follow-up time was 74 days (interquartile range: 57–233 days). Eleven patients developed uveitis and 30 patients died. We estimated the probability of developing uveitis per year in the general population as 0.05% (95% credibility interval [CrI]: 0.02%–0.1%). Corresponding posterior probabilities of treatment-related uveitis were 3.48% (95% CrI: 0.93%–7.49%) and 5.04% (95% CrI: 2.07%–9.19%) for immune checkpoint or BRAF/MEK inhibitors (posterior probability for difference: 76%). Conclusions Immune checkpoint and particularly BRAF/MEK inhibitor therapies are associated with an increase in the risk of developing uveitis. Treatment-related uveitis is not associated with systemic adverse events of immune checkpoint or BRAF/MEK inhibitors