A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data

A theoretical model for detecting drug interaction with awareness of timing of exposure

Drug-drug interaction-induced (DDI-induced) adverse drug event (ADE) is a significant public health burden. Risk of ADE can be related to timing of exposure (TOE) such as initiating two drugs concurrently or adding one drug to an existing drug. Thus, real-world data based DDI detection shall be expanded to investigate precise adverse DDI with a special awareness on TOE. We developed a Sensitive and Timing-awarE Model (STEM), which was able to optimize the probability of detection and control false positive rate for mining all two-drug combinations under case-crossover design, in particular for DDIs with TOE-dependent risk. We analyzed a large-scale US administrative claims data and conducted performance evaluation analyses. We identified signals of DDIs by using STEM, in particular for DDIs with TOE-dependent risk. We also observed that STEM identified significantly more signals than the conditional logistic regression model-based (CLRM-based) methods and the Benjamini-Hochberg procedure. In the performance evaluation, we found that STEM demonstrated proper false positive control and achieved a higher probability of detection compared to CLRM-based methods and the Benjamini-Hochberg procedure. STEM has a high probability to identify signals of DDIs in high-throughput DDI mining while controlling false positive rate, in particular for detecting signals of DDI with TOE-dependent risk.

Concomitant Use of Oral Anticoagulants With Oral Dipeptidyl Peptidase-4 Inhibitors and Serious Bleeding Events

In a prior screening study, saxagliptin, a dipeptidyl peptidase-4 inhibitor (DPP-4i), was found to have an increased rate of serious bleeding when used concomitantly with several oral anticoagulants (OACs). We aimed to confirm or refute the associations between concomitant use of individual OACs and DPP-4is and serious bleeding in a large US database, using self-controlled case series (SCCS) and case-crossover (CCO) designs. The study population was eligible Medicare beneficiaries co-exposed to a DPP-4i (precipitant) and either an OAC (object drug) or lisinopril (negative control object drug) in 2016-2020. For the SCCS, we used conditional Poisson regression to estimate adjusted rate ratios (RRs) between each co-exposure (vs. not) and serious bleeding and divided the RR by the adjusted RR for the corresponding lisinopril + precipitant pair to obtain ratios of RRs (RRRs). For the CCO, we estimated the adjusted odds ratios (ORs) of exposure to the precipitant in the focal window vs. referent window using multivariable conditional logistic regression and divided the ORs in the object drug-exposed cases over the ORs in negative object drug-exposed cases to obtain the ratios of ORs (RORs). The adjusted RRRs for serious bleeding ranged from 0.32 (0.05-1.91) for apixaban/lisinopril + saxagliptin to 3.49 (1.29-9.48) for warfarin/lisinopril + linagliptin. The adjusted RORs ranged from 0.01 (0.00-0.20) for rivaroxaban/lisinopril + saxagliptin to 2.99 (0.74-12.11) for apixaban/lisinopril + linagliptin. While we could not confirm previously identified signals because of statistical imprecision, several numerically elevated estimates still warrant caution in concomitant use and further examination.

Evaluation of Drug-Drug Interactions in Pharmacoepidemiologic Research

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design

Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real-world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case-crossover design has been implemented to leverage large-scale administrative claims data for ADE detection, while controlling both observed confounding effects and short-term fixed unobserved confounding effects. Additionally, as the case-crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case-crossover design. The proposed model is able to identify signals from all ADE-subpopulation-drug combinations, while controlling for false discovery rate (FDR) and confounding effects. We applied the PMRM to an administrative claims data. We identified ADE signals in subpopulations defined by demographic variables, comorbidities, and detailed diagnosis codes. Interestingly, certain drugs were associated with a higher risk of ADE only in subpopulations, while these drugs had a neutral association with ADE in the general population. Additionally, the PMRM could control FDR at a desired level and had a higher probability to detect true ADE signals than the widely used McNemar's test. In conclusion, the PMRM is able to identify subpopulation-specific ADE signals from a tremendous number of ADE-subpopulation-drug combinations, while controlling for both FDR and confounding effects.

Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations

The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.

Risk of fall or fracture with concomitant use of prescription opioids and other medications in osteoarthritis patients

BACKGROUND

Osteoarthritis (OA) patients taking prescription opioids for pain are at increased risk of fall or fracture, and the concomitant use of interacting drugs may further increase the risk of these events.

AIMS

To identify prescription opioid-related medication combinations associated with fall or fracture.

MATERIALS & METHODS

We conducted a case-crossover-based screening of two administrative claims databases spanning 2003 through 2021. OA patients were aged 40 years or older with at least 365 days of continuous enrollment and 90 days of continuous prescription opioid use before their first eligible fall or fracture event. The primary analysis quantified the odds ratio (OR) between fall and non-opioid medications dispensed in the 90 days before the fall date after adjustment for prescription opioid dosage and confounding using a case-time-control design. A secondary analogous analysis evaluated medications associated with fracture. The false discovery rate (FDR) was used to account for multiple testing.

RESULTS

We identified 41 693 OA patients who experienced a fall and 24 891 OA patients who experienced a fracture after at least 90 days of continuous opioid therapy. Top non-opioid medications by ascending p-value with OR > 1 for fall were meloxicam (OR 1.22, FDR = 0.08), metoprolol (OR 1.06, FDR >0.99), and celecoxib (OR 1.13, FDR > 0.99). Top non-opioid medications for fracture were losartan (OR 1.20, FDR = 0.80), alprazolam (OR 1.14, FDR > 0.99), and duloxetine (OR 1.12, FDR = 0.97).

CONCLUSION

Clinicians may seek to monitor patients who are co-prescribed drugs that act on the central nervous system, especially in individuals with OA.

Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults

INTRODUCTION

Polypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations).

METHODS

A cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE.

RESULTS

We investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes.

CONCLUSIONS

We demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.

Drug-Drug Interactions and the Risk of Emergency Hospitalizations: A Nationwide Population-Based Study

BACKGROUND

Several studies suggest a significant risk of hospitalization because of drug-drug interactions in the general population. However, to our knowledge, this risk has never been measured precisely in a large population.

OBJECTIVE

We aimed to estimate the risk of emergency hospitalization associated with exposure to the contraindicated concomitant use of interacting drugs in the general population.

METHODS

A self-controlled case-series analysis was carried out on a cohort of 150,000 subjects randomly selected from the French national health insurance database, between 01/01/2016 and 31/12/2016. Exposure to the contraindicated concomitant use of interacting drugs was defined as the overlapping period of dispensings of drugs contraindicated because of clinically meaningful drug-drug interactions. The main outcome, incidence rate ratios, comparing the incidence rate of emergency hospitalizations during each category of exposure time periods with that during the reference period, was estimated using the conditional Poisson regression model.

RESULTS

Over the study period, 967 subjects were exposed to at least one contraindicated concomitant use of interacting drug and 177 had been exposed and presented at least one emergency hospitalization. Compared to the unexposed follow-up time, the risk of emergency hospitalization increased during exposure to contraindicated concomitant use of interacting drug periods (incidence rate ratio: 2.41; 95% confidence interval 1.55-3.76). This could translate into 7200 (4500-8900) potentially preventable emergency hospitalizations yearly in France.

CONCLUSIONS

We evidenced an almost 2.5-fold increase in the risk of emergency hospitalizations during periods of exposure to contraindicated concomitant use of interacting drugs, with a potential public health impact exceeding 7000 preventable hospitalizations yearly in France. These results confirm the need to reinforce training in prescription practices and tools for prevention concerning contraindicated concomitant use of interacting drugs. These would especially concern drugs involved in an increase in long QT syndrome when associated such as citalopram, and highly prescribed drugs with a risk of overdose if co-prescribed with cytochrome P450 inhibitors, such as antigout and lipid-lowering drugs.

Opioid Drug-Drug-Drug Interactions and Unintentional Traumatic Injury: Screening to Detect Three-Way Drug Interaction Signals

Growing evidence suggests that drug interactions may be responsible for much of the known association between opioid use and unintentional traumatic injury. While prior research has focused on pairwise drug interactions, the role of higher-order (i.e., drug-drug-drug) interactions (3DIs) has not been examined. We aimed to identify signals of opioid 3DIs with commonly co-dispensed medications leading to unintentional traumatic injury, using semi-automated high-throughput screening of US commercial health insurance data. We conducted bi-directional, self-controlled case series studies using 2000-2015 Optum Data Mart database. Rates of unintentional traumatic injury were examined in individuals dispensed opioid-precipitant base pairs during time exposed vs unexposed to a candidate interacting precipitant. Underlying cohorts consisted of 16-90-year-olds with new use of opioid-precipitant base pairs and ≥1 injury during observation periods. We used conditional Poisson regression to estimate rate ratios adjusted for time-varying confounders, and semi-Bayes shrinkage to address multiple estimation. For hydrocodone, tramadol, and oxycodone (the most commonly used opioids), we examined 16,024, 8185, and 9330 drug triplets, respectively. Among these, 75 (0.5%; hydrocodone), 57 (0.7%; tramadol), and 42 (0.5%; oxycodone) were significantly positively associated with unintentional traumatic injury (50 unique base precipitants, 34 unique candidate precipitants) and therefore deemed potential 3DI signals. The signals found in this study provide valuable foundations for future research into opioid 3DIs, generating hypotheses to motivate crucially needed etiologic investigations. Further, this study applies a novel approach for 3DI signal detection using pharmacoepidemiologic screening of health insurance data, which could have broad applicability across drug classes and databases.

Translational aspects of cytochrome P450-mediated drug-drug interactions: A case study with clopidogrel

Multimorbidity, polypharmacotherapy and drug interactions are increasingly common in the ageing population. Many drug-drug interactions (DDIs) are caused by perpetrator drugs inhibiting or inducing cytochrome P450 (CYP) enzymes, resulting in alterations of the plasma concentrations of a victim drug. DDIs can have a major negative health impact, and in the past, unrecognized DDIs have resulted in drug withdrawals from the market. Signals to investigate DDIs may emerge from a variety of sources. Nowadays, standard methods are widely available to identify and characterize the mechanisms of CYP-mediated DDIs in vitro. Clinical pharmacokinetic studies, in turn, provide experimental data on pharmacokinetic outcomes of DDIs. Physiologically based pharmacokinetic (PBPK) modelling utilizing both in vitro and in vivo data is a powerful tool to predict different DDI scenarios. Finally, epidemiological studies can provide estimates on the health outcomes of DDIs. Thus, to fully characterize the mechanisms, clinical effects and implications of CYP-mediated DDIs, translational research approaches are required. This minireview provides an overview of translational approaches to study CYP-mediated DDIs, going beyond regulatory DDI guidelines, and an illustrative case study of how the DDI potential of clopidogrel was unveiled by combining these different methods.

Population-Based Signals of Antidepressant Drug Interactions Associated With Unintentional Traumatic Injury

Antidepressants are very widely used and associated with traumatic injury, yet little is known about their potential for harmful drug interactions. We aimed to identify potential drug interaction signals by assessing concomitant medications (precipitant drugs) taken with individual antidepressants (object drugs) that were associated with unintentional traumatic injury. We conducted pharmacoepidemiologic screening of 2000-2015 Optum Clinformatics data, identifying drug interaction signals by performing self-controlled case series studies for antidepressant + precipitant pairs and injury. We included persons aged 16-90 years codispensed an antidepressant and ≥ 1 precipitant drug(s), with an injury during antidepressant therapy. We classified antidepressant person-days as either precipitant-exposed or precipitant-unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to calculate confounder adjusted rate ratios (RRs) and accounted for multiple estimation via semi-Bayes shrinkage. We identified 330,884 new users of antidepressants who experienced an injury. Among such persons, we studied concomitant use of 7,953 antidepressant + precipitant pairs. Two hundred fifty-six (3.2%) pairs were positively associated with injury and deemed potential drug interaction signals; 22 of these signals had adjusted RRs > 2.00. Adjusted RRs ranged from 1.06 (95% confidence interval: 1.00-1.12, P = 0.04) for citalopram + gabapentin to 3.06 (1.42-6.60) for nefazodone + levonorgestrel. Sixty-five (25.4%) signals are currently reported in a seminal drug interaction knowledgebase. We identified numerous new population-based signals of antidepressant drug interactions associated with unintentional traumatic injury. Future studies, intended to test hypotheses, should confirm or refute these potential interactions.

Coprescription of Opioids With Other Medications and Risk of Opioid Overdose

Polypharmacy is common among patients taking prescription opioids long-term, and the codispensing of interacting medications may further increase opioid overdose risk. To identify nonopioid medications that may increase opioid overdose risk in this population, we conducted a case-crossover-based screening of electronic claims data from IBM MarketScan and Optum Clinformatics Data Mart spanning 2003 through 2019. Eligible patients were 18 years of age or older and had at least 180 days of continuous enrollment and 90 days of prescription opioid use immediately before an opioid overdose resulting in an emergency room visit or hospitalization. The main analysis quantified the odds ratio (OR) between opioid overdose and each nonopioid medication dispensed in the 90 days immediately before the opioid overdose date after adjustment for prescription opioid dosage and benzodiazepine codispensing. Additional analyses restricted to patients without cancer diagnoses and individuals who used only oxycodone for 90 days immediately before the opioid overdose date. The false discovery rate (FDR) was used to account for multiple testing. We identified 24,866 individuals who experienced opioid overdose. Baclofen (OR 1.56; FDR < 0.01; 95% confidence interval (CI), 1.29 to 1.89), lorazepam (OR 1.53; FDR < 0.01; 95% CI, 1.25 to 1.88), and gabapentin (OR 1.16; FDR = 0.09; 95% CI, 1.04 to 1.28), among other nonopioid medications, were associated with opioid overdose. Similar patterns were observed in noncancer patients and individuals who used only oxycodone. Interventions may focus on prescribing safer alternatives when a potential for interaction exists.

Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data

Self-controlled designs, specifically the case-crossover (CCO) and the self-controlled case series (SCCS), are increasingly utilized to generate real-world evidence (RWE) on drug-drug interactions (DDIs). Although these designs share the advantages and limitations of within-individual comparison, they also have design-specific assumptions. It is not known to what extent the differences in assumptions lead to different results in RWE DDI analyses. Using a nationwide US commercial healthcare insurance database (2006-2016), we compared the CCO and SCCS designs, as they are implemented in DDI studies, within five DDI-outcome examples: (1) simvastatin + clarithromycin and muscle-related toxicity; (2) atorvastatin + valsartan, and muscle-related toxicity; and (3-5) dabigatran + P-glycoprotein inhibitor (clarithromycin, amiodarone, and verapamil) and bleeding. Analyses were conducted within person-time exposed to the object drug (statins and dabigatran) and adjusted for bias associated with the inhibiting drugs via control groups of individuals unexposed to the object drug. The designs yielded similar estimates in most examples, with SCCS displaying better statistical efficiency. With both designs, results varied across sensitivity analyses, particularly in CCO analyses with small number of exposed individuals. Analyses in controls revealed substantial bias that may be differential across DDI-exposed and control individuals. Thus, both designs showed no association between amiodarone or verapamil and bleeding in dabigatran-exposed but revealed strong positive associations in controls. Overall, bias adjustment via a control group had a larger impact on results than the choice of a design, highlighting the importance and challenges of appropriate control group selection for adequate bias control in self-controlled analyses of DDIs.

Drug-Drug Interactions and Prescription Appropriateness at Hospital Discharge: Experience with COVID-19 Patients

BACKGROUND

Patients with coronavirus disease 2019 (COVID-19) are often elderly, with comorbidities, and receiving polypharmacy, all of which are known factors for potentially severe drug-drug interactions (DDIs) and the prescription of potentially inappropriate medications (PIMs).

OBJECTIVE

The aim of this study was to assess the risk of DDIs and PIMs in COVID-19 patients at hospital discharge.

METHOD

Patients with a proven diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who were hospitalized between 21 February and 30 April 2020, treated with at least two drugs, and with available information regarding pharmacological treatments upon admission and at discharge were considered. The appropriateness of drug prescriptions was assessed using INTERcheck®.

RESULTS

A significant increase in the prescription of proton pump inhibitors and heparins was found when comparing admission with hospital discharge (from 24 to 33% [p < 0.05] and from 1 to 17% [p < 0.01], respectively). The increased prescription of heparins at discharge resulted in a highly significant increase in the potentially severe DDIs mediated by this class of drugs. 51% of COVID-19 patients aged > 65 years had at least one PIM upon admission, with an insignificant increment at discharge (58%).

CONCLUSION

An increased number of prescribed drugs was observed in COVID-19 patients discharged from our hospital. The addition of heparins is appropriate according to the current literature, while the use of proton pump inhibitors is more controversial. Particular attention should be paid to the risk of bleeding complications linked to heparin-based DDIs.

Comparison of Self-controlled Designs for Evaluating Outcomes of Drug-Drug Interactions: Simulation Study

BACKGROUND

Self-controlled designs, both case-crossover and self-controlled case series, are well suited for evaluating outcomes of drug-drug interactions in electronic healthcare data. Their comparative performance in this context, however, is unknown.

METHODS

We simulated cohorts of patients exposed to two drugs: a chronic drug (object) and a short-term drug (precipitant) with an associated interaction of 2.0 on the odds ratio scale. We analyzed cohorts using case-crossover and self-controlled case series designs evaluating exposure to the precipitant drug within person-time exposed to the object drug. Scenarios evaluated violations of key design assumptions: (1) time-varying, within-person confounding; (2) time trend in precipitant drug exposure prevalence; (3) nontransient precipitant exposure; and (4) event-dependent object drug discontinuation.

RESULTS

Case-crossover analysis produced biased estimates when 30% of patients persisted on the precipitant drug (estimated OR 2.85) and when the use of the precipitant drug was increasing in simulated cohorts (estimated OR 2.56). Self-controlled case series produced biased estimates when patients discontinued the object drug following the occurrence of an outcome (estimated incidence ratio [IR] of 2.09 [50% of patients stopping therapy] and 2.22 [90%]). Both designs yielded similarly biased estimates in the presence of time-varying, within-person confounding.

CONCLUSION

In settings with independent or rare outcomes and no substantial event-dependent censoring (<50%), self-controlled case series may be preferable to case-crossover design for evaluating outcomes of drug-drug interactions. With frequent event-dependent drug discontinuation, a case-crossover design may be preferable provided there are no time-related trends in drug exposure.

Association between opioid analgesic plus benzodiazepine use and death: A case-crossover study

OBJECTIVE

We aimed to investigate whether concomitant use of benzodiazepines and opioids is associated with an increased risk of death in a population-based case-crossover setting.

METHODS

We conducted a case-crossover study using the National Sample Cohort database. We introduced a 30-day hazard period before the onset of death and three consecutive previous 30-day control periods with a 30-day washout period. The use of opioids and/or benzodiazepines during the hazard period was compared with that in the three control periods. We performed the conditional logistic regression analysis to estimate the adjusted odds ratios (aORs) and their 95% confidence intervals (CIs).

RESULTS

A total of 13,161 individuals who previously used benzodiazepines or opioids and died were included in the study. The risk of death was higher in patients with concomitant use of benzodiazepines and opioids (aOR, 1.86; 95% CI, 1.71-2.02) than in those who used either benzodiazepines or opioids only. In the subgroup analysis among concomitant users, the mortality risks were highest in patients aged less than 20 years (aOR, 3.85; 95% CI, 1.65-8.99), male patients (aOR, 2.20; 95% CI, 1.93-2.51), and patients with renal disease (aOR, 2.42; 95% CI, 1.57-3.74).

CONCLUSION

In this study, concomitant use of benzodiazepines and opioids was associated with a higher risk of death compared with use of a single drug. The risks and benefits of co-prescribing of benzodiazepines and opioids must be weighed carefully.

Screening to identify signals of opioid drug interactions leading to unintentional traumatic injury

BACKGROUND

Efforts to minimize harms from opioid drug interactions may be hampered by limited evidence on which drugs, when taken concomitantly with opioids, result in adverse clinical outcomes.

OBJECTIVE

To identify signals of opioid drug interactions by identifying concomitant medications (precipitant drugs) taken with individual opioids (object drugs) that are associated with unintentional traumatic injury DESIGN: We conducted pharmacoepidemiologic screening of Optum Clinformatics Data Mart, identifying drug interaction signals by performing confounder-adjusted self-controlled case series studies for opioid + precipitant pairs and injury.

SETTING

Beneficiaries of a major United States-based commercial health insurer during 2000-2015 PATIENTS: Persons aged 16-90 years co-dispensed an opioid and ≥1 precipitant drug(s), with an unintentional traumatic injury event during opioid therapy, as dictated by the case-only design EXPOSURE: Precipitant-exposed (vs. precipitant-unexposed) person-days during opioid therapy.

OUTCOME

Emergency department or inpatient International Classification of Diseases discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to generate confounder adjusted rate ratios. We accounted for multiple estimation via semi-Bayes shrinkage.

RESULTS

We identified 25,019, 12,650, and 10,826 new users of hydrocodone, tramadol, and oxycodone who experienced an unintentional traumatic injury. Among 464, 376, and 389 hydrocodone-, tramadol-, and oxycodone-precipitant pairs examined, 20, 17, and 16 (i.e., 53 pairs, 34 unique precipitants) were positively associated with unintentional traumatic injury and deemed potential drug interaction signals. Adjusted rate ratios ranged from 1.23 (95 % confidence interval: 1.05-1.44) for hydrocodone + amoxicillin-clavulanate to 4.21 (1.88-9.42) for oxycodone + telmisartan. Twenty (37.7 %) of 53 signals are currently reported in a major drug interaction knowledgebase.

LIMITATIONS

Potential for reverse causation, confounding by indication, and chance CONCLUSIONS: We identified previously undescribed and/or unappreciated signals of opioid drug interactions associated with unintentional traumatic injury. Subsequent etiologic studies should confirm (or refute) and elucidate these potential drug interactions.

Bias in case-crossover studies of medications due to persistent use: A simulation study

PURPOSE

The case-crossover design is increasingly used to evaluate the effects of chronic medications; however, as traditionally implemented in pharmacoepidemiology, with referent period preceding the outcome, it may lead to bias in the presence of persistent exposures. We aimed to evaluate the extent and magnitude of bias in case-crossover analyses of chronic and persistent exposures, using simulations.

METHODS

We simulated cohorts with either 30-day, 180-day, or 2-year exposure duration; and with varying degrees of persistence (10%, 30%, 50%, 70%, or 90% of patients not stopping exposure). We evaluated all scenarios under the null and the scenario with 30% persistence under varying exposure effects (odds ratios of 0.25 to 4.0). Cohorts were analyzed using conditional logistic regression that compared the odds of exposure on the outcome day to the odds of exposure on a referent day 30 days prior to the outcome. We further implemented the case-time-control design to evaluate its ability to adjust for bias from persistence.

RESULTS

Case-crossover analyses produced unbiased estimates across all scenarios without persistent users, regardless of exposure duration. In scenarios where some patients persisted on treatment, case-crossover analyses resulted in upward bias, which increased with increasing proportion of persistent users, but did not vary substantially in relation to the magnitude of the true effect. Case-time-control analyses removed bias in all scenarios.

CONCLUSIONS

Investigators should be aware of bias due to treatment persistence in unidirectional case-crossover analyses of chronic medications, which can be remedied with a control group of similarly persistent noncases.

Drug Interactions in Space: a Cause for Concern?

PURPOSE

Crewmembers aboard the International Space Station (ISS) have free access to an increasing number of medications within medical kits. The aim of the current study was to assess the number, severity and reliability of potential drug-drug interactions (DDIs) involving those medications.

METHODS

We evaluated the information obtained from clinical decision support systems. Searches for potential DDIs were applied to published lists of medications available to US astronauts in medical kits aboard the ISS.

RESULTS

A total of 311 potential DDIs were identified by Lexi-Interact, of which approximately half were recognized by Micromedex as well. Major, moderate and minor interactions consisted 23.5%, 68.5% and 8.0% of entries, respectively. The reliability of 71.1% of alerts was fair. Commonly used drugs, including zolpidem and zaleplon, were involved in multiple potential interactions that were classified as major based on additive CNS depression.

CONCLUSIONS

Most potential DDIs likely to be encountered in space are unestablished even in terrestrial medicine and their assignment is based on class-effects. Yet, some drug combinations may be associated with clinically-relevant consequences. Future DDI rating should be adjusted to space-related outcomes. Until that happens, it would be advisable to avoid non-established drug combinations in space when possible.