Semi-automated risk estimation using large databases: quinolones and Clostridium difficile associated diarrhea

Fluoroquinolone-associated adverse events of interest among hospitalized veterans affairs patients with community-acquired pneumonia who were treated with a fluoroquinolone: A focus on tendonitis, Clostridioides difficile infection, and aortic aneurysm

STUDY OBJECTIVE

The objectives of this study were to (i) quantify the incidence of three concerning fluoroquinolone adverse events of interest (FQAEI, i.e., adverse tendon event (TE), clostridioides difficile infection (CDI), and aortic aneurysm/dissection (AAD)), (ii) identify the patient-level factors that predict these events, and (iii) develop clinical risk scores to estimate the predicted probabilities of each FQAEI based on patient-level covariates available on clinical presentation.

DESIGN

Retrospective cohort study.

SETTING

Upstate New York Veterans' Healthcare Administration from 2011 to 2016.

PATIENTS

Hospitalized patients with community-acquired pneumonia receiving care in the Upstate New York Veterans' Healthcare Administration from 2011 to 2016.

INTERVENTION

N/A.

MEASUREMENTS

The outcomes of interest for this study were the occurrence of TE, CDI, and AAD. We also evaluated a composite of these three outcomes, FQAEI.

MAIN RESULTS

The study population consisted of 1071 patients. The overall incidence of FQAEI, TE, AAD, and CDI was 6.5%, 1.8%, 4.5%, and 0.3%, respectively. For each outcome evaluated, the probability of the event of interest was predicted by the presence of certain comorbidities, previous healthcare exposure, choice of specific FQ antibiotic, or therapy duration. Concomitant steroids, pneumonia in preceding 180 days, and creatinine clearance <30 mL/min predicted FQAEI.

CONCLUSIONS

Individual frequencies of three important FQAEIs were quantified, and risk scores were developed to estimate the probabilities of experiencing these events to help clinicians individualize treatment decisions for patients and reduce the potential risks of select FQAEIs.

A Pharmacoepidemiology Database System for Monitoring Risk Due to the Use of Medicines by New Zealand Primary Care Patients

INTRODUCTION

The use of large record-linked healthcare databases for drug safety research and surveillance is now accepted practice. New Zealand's standardized national healthcare datasets provide the potential to automate the conduct of pharmacoepidemiological studies to provide rapid validation of medicine safety signals.

OBJECTIVES

Our objectives were to describe the methodology undertaken by a semi-automated computer system developed to rapidly assess risk due to drug exposure in New Zealand's population of primary care patients and to compare results from three studies with previously published findings.

METHODS

Data from three national databases were linked at the patient level in the automated studies. A retrospective nested case-control design was used to evaluate risk for upper gastrointestinal bleeding (UGIB), acute kidney failure (AKF), and serious arrhythmia associated with individual medicines, therapeutic classes of medicines, and concurrent use of medicines from multiple therapeutic classes.

RESULTS

The patient cohort available for each study included 5,194,256 patients registered between 2007 and 2014, with a total of 34,630,673 patient-years at risk. An increased risk for UGIB was associated with non-steroidal anti-inflammatory drugs (NSAIDs) (adjusted odds ratio [AOR] 4.16, 95% confidence interval [CI] 3.90-4.43, p < 0.001) and selective serotonin reuptake inhibitors (AOR 1.39, 95% CI 1.20-1.62, p < 0.001); an increased risk for AKF was associated with NSAIDs (AOR 1.78, 95% CI 1.73-1.83, p < 0.001) and proton pump inhibitors (AOR 1.78, 95% CI 1.72-1.83, p < 0.001); and an increased risk for serious arrhythmia was associated with fluoroquinolones (AOR 1.38, 95% CI 1.26-151, p < 0.001) and penicillins (AOR 1.69, 95% CI 1.61-1.77, p < 0.001).

CONCLUSIONS

Automated case-control studies using New Zealand's healthcare datasets can replicate associations of risk with drug exposure consistent with previous findings. Their speed of conduct enables systematic monitoring of risk for adverse events associated with a wide range of medicines.

Performance of a semi-automated approach for risk estimation using a common data model for longitudinal healthcare databases

Different structures and coding schemes may limit rapid evaluation of a large pool of potential drug safety signals using multiple longitudinal healthcare databases. To overcome this restriction, a semi-automated approach utilising common data model (CDM) and robust pharmacoepidemiologic methods was developed; however, its performance needed to be evaluated. Twenty-three established drug-safety associations from publications were reproduced in a healthcare claims database and four of these were also repeated in electronic health records. Concordance and discrepancy of pairwise estimates were assessed between the results derived from the publication and results from this approach. For all 27 pairs, an observed agreement between the published results and the results from the semi-automated approach was greater than 85% and Kappa coefficient was 0.61, 95% CI: 0.19-1.00. Ln(IRR) differed by less than 50% for 13/27 pairs, and the IRR varied less than 2-fold for 19/27 pairs. Reproducibility based on the intra-class correlation coefficient was 0.54. Most covariates (>90%) in the publications were available for inclusion in the models. Once the study populations and inclusion/exclusion criteria were obtained from the literature, the analysis was able to be completed in 2-8 h. The semi-automated methodology using a CDM produced consistent risk estimates compared to the published findings for most selected drug-outcome associations, regardless of original study designs, databases, medications and outcomes. Further assessment of this approach is useful to understand its roles, strengths and limitations in rapidly evaluating safety signals.

Should quinolones come first in Helicobacter pylori therapy?

New generations of fluoroquinolones, like levofloxacin and moxifloxacin, exhibit a broad-spectrum activity against Gram-positive and Gram-negative bacteria, and have been successfully introduced into the treatment of Helicobacter pylori infection. Based on a large body of evidence, current guidelines recommend the use of levofloxacin- or moxifloxacin-containing proton-pump inhibitor (PPI) triple therapies in second-line or rescue treatment of H. pylori infection. The efficacy of standard PPI triple therapies has substantially declined during the last decade, mainly due to increasing resistance against the key antibiotics clarithromycin and metronidazole. Therefore, alternative strategies for first-line therapy of H. pylori infection have been evaluated in a considerable number of clinical trials including sequential regimens, nonbismuth quadruple regimens, and quinolone-containing PPI triple therapy regimens. The aim of this paper is to summarize the current body of evidence of levofloxacin- and moxifloxacin-containing regimens in first-line treatment of H. pylori infection, and to discuss the risks and benefits of these strategies in the light of increasing resistance of H. pylori to quinolones.

Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases

OBJECTIVE

Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines.

DESIGN

The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects.

MEASUREMENTS

The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction.

RESULTS

Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results.

CONCLUSION

A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.