Development of a new travellers' diarrhoea clinical severity classification and its utility in confirming rifamycin-SV efficacy

BACKGROUND travellers' diarrhoea (TD) is frequently reported with incidence up to 40% in high-risk destinations. Previous studies showed that the number of loose stools alone is inadequate to holistically predict the severity of TD. To improve the prediction of prognosis and to optimize treatments, a simple risk-based clinical severity classification has been developed. METHODS pooled baseline data of signs and symptoms and number of loose stools from 1098 subjects enrolled in two double-blind Phase 3 trials of rifamycin-SV were analyzed with correlation, multiple correspondence analyses, prognostic factor criteria, and Contal and O'Quigley method to generate a TD severity classification (mild, moderate and severe). The relative importance of this classification on resolution of TD was assessed by Cox proportional model hazard model on the time to last unformed stool (TLUS). RESULTS the analysis showed that TLUS were longer for the severe [hazard ratio (HR) 0.24; P < 0.001; n = 173] and moderate (HR 0.54; P = 0.0272; n = 912) vs mild. Additionally, when the treatment assigned in the studies was investigated in the severity classification, the results yielded that rifamycin-SV significantly shortened TLUS vs placebo for all subjects (HR 1.9; P = 0.0006), severe (HR 5.9; P = 0.0232) and moderate (HR 1.7; P = 0.0078) groups and was as equally efficacious as ciprofloxacin for all subjects, moderate and severe groups (HRs: 0.962, 0.9, 1.2; all P = NS, respectively). When reassessed by this classification, rifamycin-SV showed consistent efficacy with the Phase 3 studies. CONCLUSIONS this newly developed TD clinical severity classification demonstrated strong prognostic value and clinical utility by combining patients' multiple signs and symptoms of enteric infection and number of loose stools to provide a holistic assessment of TD. By expanding on the current classification by incorporating patient reported outcomes in addition to TLUS, a classification like the one developed, may help optimize patient selection for future clinical studies

Assessing the Association of Pioglitazone Use and Bladder Cancer Through Drug Adverse Event Reporting

OBJECTIVE\u2014To analyze the association between pioglitazone use and bladder cancer through a spontaneous adverse event reporting system for medications. RESEARCH DESIGN AND METHODS\u2014Case/noncase bladder cancer reports associated with antidiabetic drug use were retrieved from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) between 2004 and 2009 and analyzed by the reporting odds ratio (ROR). RESULTS\u2014Ninety-three reports of bladder cancer were retrieved, corresponding to 138 drug reaction pairs (pioglitazone, 31; insulin, 29; metformin, 25; glimepiride, 13; exenatide, 8; others, 22). RORwas indicative of a definite risk for pioglitazone (4.30 [95%CI 2.82\u20136.52]), and a much weaker risk for gliclazide and acarbose, with very few cases being treated with these two drugs (6 and 4, respectively). CONCLUSIONS\u2014In agreement with preclinical and clinical studies, AERS analysis is consistent with an association between pioglitazone and bladder cancer. This issue needs constant epidemiologic surveillance and urgent definition by more specific studies

Eluxadoline Benefits Patients With Irritable Bowel Syndrome With Diarrhea in a Phase 2 Study

Background & AimsSimultaneous agonism of the μ-opioid receptor and antagonism of the δ-opioid receptor can reduce abdominal pain and diarrhea in patients with irritable bowel syndrome with diarrhea (IBS-D) without constipating side effects. We evaluated the efficacy and safety of a minimally absorbed, μ-opioid receptor agonist and δ-opioid receptor antagonist (eluxadoline) in a phase 2 study in patients with IBS-D.MethodsWe randomly assigned 807 patients to groups that received oral placebo twice daily or 5, 25, 100, or 200 mg oral eluxadoline for 12 weeks. The primary end point was clinical response at week 4, defined by a mean reduction in daily pain score from baseline of ≥30%, and of at least 2 points on 0−10 scale, as well as a stool consistency score of 3 or 4 on the Bristol Stool Scale (1–7) for at least 66% of daily diary entries during that week.ResultsSignificantly more patients receiving 25 mg (12.0%) or 200 mg (13.8%) eluxadoline met the primary end point of clinical response than patients given placebo (5.7%; P < .05). Patients receiving eluxadoline at 100 mg and 200 mg also had greater improvements in bowel movement frequency and urgency, global symptoms, quality of life, and adequate relief assessments (P < .05). Additionally, patients receiving 100 mg (28.0%) or 200 mg (28.5%) eluxadoline were significantly more likely than those receiving placebo (13.8%; P < .005) to meet the US Food and Drug Administration response end point during the full 12 weeks of the study. Eluxadoline was well tolerated with a low incidence of constipation.ConclusionsIn a phase 2 study of the mixed μ-opioid receptor agonist/δ-opioid receptor antagonist eluxadoline vs placebo in patients with IBS-D, patients given eluxadoline were significantly more likely to be clinical responders, based on a composite of improvement in abdominal pain and stool consistency. Further study of eluxadoline is warranted to assess its potential as a treatment for IBS-D. ClinicalTrials.gov number, NCT0113027

Hospital Mortality in Critically Ill Patients

In a multicenter observational cohort of patients-admitted to intensive care units (ICU), we assessed whether creatinine elevation prior to dialysis initiation in acute kidney injury (AKI-D) further discriminates risk-adjusted mortality. AKI-D was categorized into four groups (Grp) based on creatinine elevation aer ICU admission but before dialysis initiation: Grp I &gt; 0.3 mg/dL to &lt;2-fold increase, Grp II ≥2 times but &lt;3 times increase, Grp III ≥3-fold increase in creatinine, and Grp IV none or &lt;0.3 mg/dl increase. Standardized mortality rates (SMR) were calculated by using a validated risk-adjusted mortality model and expressed with 95% con�dence intervals (CI). 2,744 patients developed AKI-D during ICU stay; 36.7%, 20.9%, 31.2%, and 11.2% belonged to groups I, II, III, and IV, respectively. SMR showed a graded increase in Grp I, II, and III (1.40 (95% CI, 1.29-1.42), 1.84 (1.66-2.04), and 2.25 (2.07-2.45)) and was 0.98 (0.78-1.20) in Grp IV. In ICU patients with AKI-D, degree of creatinine elevation prior to dialysis initiation is independently associated with hospital mortality. It is the lowest in those experiencing minor or no elevations in creatinine and may represent reversible �uid-electrolyte disturbances

Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p

Front Pharmacol

Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias. Our main aim was to develop a method having the same advantages as multiple regression approaches in dealing with bias, while relying on the statistical multiple comparison framework as regards decision thresholds, by considering false discovery rate (FDR)-based decision rules. We investigate four PS estimation methods in high dimension: a gradient tree boosting (GTB) algorithm from machine-learning and three variable selection algorithms. For each (drug, adverse event) pair, the PS is then applied as adjustment covariate or by using two kinds of weighting: inverse proportional treatment weighting and matching weights. The different versions of the new approach were compared to a univariate approach, which is a disproportionality method, and to two penalized multiple logistic regression approaches, directly applied on spontaneous reporting data. Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000-2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches

Statin-Associated Muscular and Renal Adverse Events: Data Mining of the Public Version of the FDA Adverse Event Reporting System

OBJECTIVE: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events