Dataset Information

Opioid Analgesics and the Risk of Serious Infections Among Patients With Rheumatoid Arthritis: A Self-Controlled Case Series Study.

ABSTRACT: Animal studies and in vitro human studies suggest that certain opioid analgesics impair crucial immune functions. This study was undertaken to determine whether opioid use is associated with increased risk of serious infection in patients with rheumatoid arthritis (RA).We conducted a self-controlled case series analysis on a retrospective cohort of 13,796 patients with RA enrolled in Tennessee Medicaid in 1995-2009. Within-person comparisons of the risk of hospitalization for serious infection during periods of opioid use versus non-use were performed using conditional Poisson regression. Fixed confounders were accounted for by design; time-varying confounders included age and use of disease-modifying antirheumatic drugs, glucocorticoids, and proton-pump inhibitors. In additional analyses, risks associated with new opioid use, use of opioids known to have immunosuppressive properties, use of long-acting opioids, and different opioid dosages were assessed. Sensitivity analyses were performed to account for potential protopathic bias and confounding by indication.Among 1,790 patients with RA who had at least 1 hospitalization for serious infection, the adjusted incidence rate of serious infection was higher during periods of current opioid use compared to non-use, with an incidence rate ratio (IRR) of 1.39 (95% confidence interval [95% CI] 1.19-1.62). The incidence rate was also higher during periods of long-acting opioid use, immunosuppressive opioid use, and new opioid use compared to non-use (IRR 2.01 [95% CI 1.52-2.66], IRR 1.72 [95% CI 1.33-2.23], and IRR 2.38 [95% CI 1.65-3.42], respectively). Results of sensitivity analyses were consistent with the main findings.In within-person comparisons of patients with RA, opioid use was associated with an increased risk of hospitalization for serious infection.

SUBMITTER: Wiese AD

PROVIDER: S-EPMC4728045 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Opioid Analgesics and the Risk of Serious Infections Among Patients With Rheumatoid Arthritis: A Self-Controlled Case Series Study.

Wiese Andrew D AD Griffin Marie R MR Stein C Michael CM Mitchel Edward F EF Grijalva Carlos G CG

Arthritis & rheumatology (Hoboken, N.J.) 20160201 2

<h4>Objective</h4>Animal studies and in vitro human studies suggest that certain opioid analgesics impair crucial immune functions. This study was undertaken to determine whether opioid use is associated with increased risk of serious infection in patients with rheumatoid arthritis (RA).<h4>Methods</h4>We conducted a self-controlled case series analysis on a retrospective cohort of 13,796 patients with RA enrolled in Tennessee Medicaid in 1995-2009. Within-person comparisons of the risk of hospi ...[more]

PMID: 26473742

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Project description:BackgroundThe relative risk of acute kidney injury (AKI) following different infections, and whether angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) modify the risk, is unclear. We aimed to determine the risks of hospital admission with AKI following infections (urinary tract infection [UTI], lower respiratory tract infection [LRTI], and gastroenteritis) among users of antihypertensive drugs.MethodsWe used UK electronic health records from practices contributing to the Clinical Practice Research Datalink linked to the Hospital Episode Statistics database. We identified adults initiating ACEIs/ARBs or alternative antihypertensive therapy (β-blockers, calcium channel blockers, or thiazide diuretics) between April 1997 and March 2014 with at least 1 year of primary care registration prior to first prescription, who had a hospital admission for AKI, and who had a primary care record for incident UTI, LRTI, or gastroenteritis. We used a self-controlled case series design to calculate age-adjusted incidence rate ratios (IRRs) for AKI during risk periods following acute infection relative to noninfected periods (baseline).ResultsWe identified 10,219 eligible new users of ACEIs/ARBs or other antihypertensives with an AKI record. Among these, 2,012 had at least one record for a UTI during follow-up, 2,831 had a record for LRTI, and 651 had a record for gastroenteritis. AKI risk was higher following infection than in baseline noninfectious periods. The rate ratio was highest following gastroenteritis: for the period 1-7 days postinfection, the IRR for AKI following gastroenteritis was 43.4 (95% CI=34.0-55.5), compared with 6.0 following LRTI (95% CI=5.0-7.3), and 9.3 following UTI (95% CI=7.8-11.2). Increased risks were similar for different antihypertensives.ConclusionAcute infections are associated with substantially increased transient AKI risk among antihypertensive users, with the highest risk after gastroenteritis. The increase in relative risk is not greater among users of ACEIs/ARBs compared with users of other antihypertensives.

Project description:BackgroundPatients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.MethodsThis analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model.ResultsA total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only.ConclusionsBaseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction.Trial registrationClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

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Dataset Information

Opioid Analgesics and the Risk of Serious Infections Among Patients With Rheumatoid Arthritis: A Self-Controlled Case Series Study.

Publications

Opioid Analgesics and the Risk of Serious Infections Among Patients With Rheumatoid Arthritis: A Self-Controlled Case Series Study.

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