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Acton EK, Hennessy S, Gelfand MA, Leonard CE, Bilker WB, Shu D, Willis AW, Kasner SE. Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations. Clin Pharmacol Ther 2024; 116:448-459. [PMID: 38860403 PMCID: PMC11262479 DOI: 10.1002/cpt.3310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/06/2024] [Indexed: 06/12/2024]
Abstract
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.
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Affiliation(s)
- Emily K. Acton
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Sean Hennessy
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Michael A. Gelfand
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
| | - Charles E. Leonard
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
| | - Warren B. Bilker
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Di Shu
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Allison W. Willis
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
| | - Scott E. Kasner
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
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Vajravelu RK, Byerly AR, Feldman R, Rothenberger SD, Schoen RE, Gellad WF, Lewis JD. Active surveillance pharmacovigilance for Clostridioides difficile infection and gastrointestinal bleeding: an analytic framework based on case-control studies. EBioMedicine 2024; 103:105130. [PMID: 38653188 PMCID: PMC11041851 DOI: 10.1016/j.ebiom.2024.105130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Active surveillance pharmacovigilance is an emerging approach to identify medications with unanticipated effects. We previously developed a framework called pharmacopeia-wide association studies (PharmWAS) that limits false positive medication associations through high-dimensional confounding adjustment and set enrichment. We aimed to assess the transportability and generalizability of the PharmWAS framework by using medical claims data to reproduce known medication associations with Clostridioides difficile infection (CDI) or gastrointestinal bleeding (GIB). METHODS We conducted case-control studies using Optum's de-identified Clinformatics Data Mart Database of individuals enrolled in large commercial and Medicare Advantage health plans in the United States. Individuals with CDI (from 2010 to 2015) or GIB (from 2010 to 2021) were matched to controls by age and sex. We identified all medications utilized prior to diagnosis and analysed the association of each with CDI or GIB using conditional logistic regression adjusted for risk factors for the outcome and a high-dimensional propensity score. FINDINGS For the CDI study, we identified 55,137 cases, 220,543 controls, and 290 medications to analyse. Antibiotics with Gram-negative spectrum, including ciprofloxacin (aOR 2.83), ceftriaxone (aOR 2.65), and levofloxacin (aOR 1.60), were strongly associated. For the GIB study, we identified 450,315 cases, 1,801,260 controls, and 354 medications to analyse. Antiplatelets, anticoagulants, and non-steroidal anti-inflammatory drugs, including ticagrelor (aOR 2.81), naproxen (aOR 1.87), and rivaroxaban (aOR 1.31), were strongly associated. INTERPRETATION These studies demonstrate the generalizability and transportability of the PharmWAS pharmacovigilance framework. With additional validation, PharmWAS could complement traditional passive surveillance systems to identify medications that unexpectedly provoke or prevent high-impact conditions. FUNDING U.S. National Institute of Diabetes and Digestive and Kidney Diseases.
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Affiliation(s)
- Ravy K Vajravelu
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
| | - Amy R Byerly
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Robert Feldman
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Scott D Rothenberger
- Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Robert E Schoen
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Walid F Gellad
- Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - James D Lewis
- Division of Gastroenterology and Hepatology, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Zafari Z, Park JE, Shah CH, dosReis S, Gorman EF, Hua W, Ma Y, Tian F. The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies. Am J Epidemiol 2024; 193:426-453. [PMID: 37851862 DOI: 10.1093/aje/kwad201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 07/27/2023] [Accepted: 10/06/2023] [Indexed: 10/20/2023] Open
Abstract
Uses of real-world data in drug safety and effectiveness studies are often challenged by various sources of bias. We undertook a systematic search of the published literature through September 2020 to evaluate the state of use and utility of negative controls to address bias in pharmacoepidemiologic studies. Two reviewers independently evaluated study eligibility and abstracted data. Our search identified 184 eligible studies for inclusion. Cohort studies (115, 63%) and administrative data (114, 62%) were, respectively, the most common study design and data type used. Most studies used negative control outcomes (91, 50%), and for most studies the target source of bias was unmeasured confounding (93, 51%). We identified 4 utility domains of negative controls: 1) bias detection (149, 81%), 2) bias correction (16, 9%), 3) P-value calibration (8, 4%), and 4) performance assessment of different methods used in drug safety studies (31, 17%). The most popular methodologies used were the 95% confidence interval and P-value calibration. In addition, we identified 2 reference sets with structured steps to check the causality assumption of the negative control. While negative controls are powerful tools in bias detection, we found many studies lacked checking the underlying assumptions. This article is part of a Special Collection on Pharmacoepidemiology.
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Chen C, Hennessy S, Brensinger CM, Dawwas GK, Acton EK, Bilker WB, Chung SP, Dublin S, Horn JR, Miano TA, Pham Nguyen TP, Soprano SE, Leonard CE. Skeletal muscle relaxant drug-drug-drug interactions and unintentional traumatic injury: Screening to detect three-way drug interaction signals. Br J Clin Pharmacol 2022; 88:4773-4783. [PMID: 35562168 PMCID: PMC9560998 DOI: 10.1111/bcp.15395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/27/2022] Open
Abstract
AIM The aim of this study was to identify skeletal muscle relaxant (SMR) drug-drug-drug interaction (3DI) signals associated with increased rates of unintentional traumatic injury. METHODS We conducted automated high-throughput pharmacoepidemiologic screening of 2000-2019 healthcare data for members of United States commercial and Medicare Advantage health plans. We performed a self-controlled case series study for each drug triad consisting of an SMR base-pair (i.e., concomitant use of an SMR with another medication), and a co-dispensed medication (i.e., candidate interacting precipitant) taken during ongoing use of the base-pair. We included patients aged ≥16 years with an injury occurring during base-pair-exposed observation time. We used conditional Poisson regression to calculate adjusted rate ratios (RRs) with 95% confidence intervals (CIs) for injury with each SMR base-pair + candidate interacting precipitant (i.e., triad) versus the SMR-containing base-pair alone. RESULTS Among 58 478 triads, 29 were significantly positively associated with injury; confounder-adjusted RRs ranged from 1.39 (95% CI = 1.01-1.91) for tizanidine + omeprazole with gabapentin to 2.23 (95% CI = 1.02-4.87) for tizanidine + diclofenac with alprazolam. Most identified 3DI signals are new and have not been formally investigated. CONCLUSION We identified 29 SMR 3DI signals associated with increased rates of injury. Future aetiologic studies should confirm or refute these SMR 3DI signals.
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Affiliation(s)
- Cheng Chen
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Colleen M. Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Ghadeer K. Dawwas
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
| | - Emily K. Acton
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Warren B. Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | | | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute (Seattle, WA, US)
- Department of Epidemiology, School of Public Health, University of Washington (Seattle, WA, US)
| | - John R. Horn
- Department of Pharmacy, School of Pharmacy, University of Washington (Seattle, WA, US)
| | - Todd A. Miano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Thanh Phuong Pham Nguyen
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Samantha E. Soprano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Charles E. Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
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Nguyen TPP, Soprano SE, Hennessy S, Brensinger CM, Bilker WB, Miano TA, Acton EK, Horn JR, Chung SP, Dublin S, Oslin DW, Wiebe DJ, Leonard CE. Population-based signals of benzodiazepine drug interactions associated with unintentional traumatic injury. J Psychiatr Res 2022; 151:299-303. [PMID: 35526445 PMCID: PMC9513701 DOI: 10.1016/j.jpsychires.2022.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022]
Abstract
Benzodiazepine receptor agonists and related medications, such as Z-drugs and dual orexin receptor antagonists (BZDs), have been associated with unintentional traumatic injury due to their central nervous system (CNS)-depressant effects. Drug-drug interactions (DDIs) may contribute to the known relationship between BZD use and unintentional traumatic injury, yet evidence is still lacking. We conducted high-throughput pharmacoepidemiologic screening using the self-controlled case series design in a large US commercial health insurance database to identify potentially clinically relevant DDI signals among new users of BZDs. We used conditional Poisson regression to estimate rate ratios (RRs) between each co-exposure (vs. not) and unintentional traumatic injury (primary outcome), typical hip fracture (secondary outcome), and motor vehicle crash (secondary outcome). We identified 48 potential DDI signals (1.1%, involving 39 unique co-dispensed drugs), i.e., with statistically significant elevated adjusted RRs for injury. Signals were strongest for DDI pairs involving zolpidem, lorazepam, temazepam, alprazolam, eszopiclone, triazolam, and clonazepam. We also identified four potential DDI signals for typical hip fracture, but none for motor vehicle crash. Many signals have biologically plausible explanations through additive or synergistic pharmacodynamic effects of co-dispensed antidepressants, opioids, or muscle relaxants on CNS depression, impaired psychomotor and cognitive function, and/or somnolence. While other signals that lack an obvious mechanism may represent true associations that place patients at risk of injury, it is also prudent to consider the roles of chance, reverse causation, and/or confounding by indication, which merit further exploration. Given the high-throughput nature of our investigation, findings should be interpreted as hypothesis generating.
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Affiliation(s)
- Thanh Phuong Pham Nguyen
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Samantha E. Soprano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
| | - Colleen M. Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Warren B. Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Todd A. Miano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Emily K. Acton
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - John R. Horn
- Department of Pharmacy, School of Pharmacy, University of Washington (Seattle, WA, US)
| | | | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington (Seattle, WA, US),Department of Epidemiology, School of Public Health, University of Washington (Seattle, WA, US)
| | - David W. Oslin
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz Veterans Administration Medical Center (Philadelphia, PA, US)
| | - Douglas J. Wiebe
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US),Penn Injury Science Center, University of Pennsylvania (Philadelphia, PA, US)
| | - Charles E. Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US),Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US),Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
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Acton EK, Hennessy S, Brensinger CM, Bilker WB, Miano TA, Dublin S, Horn JR, Chung S, Wiebe DJ, Willis AW, Leonard CE. Opioid Drug-Drug-Drug Interactions and Unintentional Traumatic Injury: Screening to Detect Three-Way Drug Interaction Signals. Front Pharmacol 2022; 13:845485. [PMID: 35620282 PMCID: PMC9127150 DOI: 10.3389/fphar.2022.845485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- Emily K. Acton
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Colleen M. Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Warren B. Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Todd A. Miano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, United States
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States
| | - John R. Horn
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, United States
| | - Sophie Chung
- AthenaHealth, Inc., Watertown, MA, United States
| | - Douglas J. Wiebe
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
- Penn Injury Science Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Allison W. Willis
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
| | - Charles E. Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
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7
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Dawwas GK, Hennessy S, Brensinger CM, Acton EK, Bilker WB, Chung S, Dublin S, Horn JR, Manis MM, Miano TA, Oslin DW, Pham Nguyen TP, Soprano SE, Wiebe DJ, Leonard CE. Signals of Muscle Relaxant Drug Interactions Associated with Unintentional Traumatic Injury: A Population-Based Screening Study. CNS Drugs 2022; 36:389-400. [PMID: 35249204 PMCID: PMC9375100 DOI: 10.1007/s40263-022-00909-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/17/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Use of muscle relaxants is rapidly increasing in the USA. Little is understood about the role of drug interactions in the known association between muscle relaxants and unintentional traumatic injury, a clinically important endpoint causing substantial morbidity, disability, and death. OBJECTIVE We examined potential associations between concomitant drugs (i.e., precipitants) taken with muscle relaxants (affected drugs, i.e., objects) and hospital presentation for unintentional traumatic injury. METHODS In a series of self-controlled case series studies, we screened to identify drug interaction signals for muscle relaxant + precipitant pairs and unintentional traumatic injury. We used Optum's de-identified Clinformatics® Data Mart Database, 2000-2019. We included new users of a muscle relaxant, aged 16-90 years, who were dispensed at least one precipitant drug and experienced an unintentional traumatic injury during the observation period. We classified each observation day as 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 estimate rate ratios adjusting for time-varying confounders and then accounted for multiple estimation via semi-Bayes shrinkage. RESULTS We identified 74,657 people who initiated muscle relaxants and experienced an unintentional traumatic injury, in whom we studied concomitant use of 2543 muscle relaxant + precipitant pairs. After adjusting for time-varying confounders, 16 (0.6%) pairs were statistically significantly and positively associated with injury, and therefore deemed signals of a potential drug interaction. Among signals, semi-Bayes shrunk, confounder-adjusted rate ratios ranged from 1.29 (95% confidence interval 1.04-1.62) for baclofen + sertraline to 2.28 (95% confidence interval 1.14-4.55) for methocarbamol + lamotrigine. CONCLUSIONS Using real-world data, we identified several new signals of potential muscle relaxant drug interactions associated with unintentional traumatic injury. Only one among 16 signals is currently reported in a major drug interaction knowledge base. Future studies should seek to confirm or refute these signals.
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Affiliation(s)
- Ghadeer K. Dawwas
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colleen M. Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily K. Acton
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Warren B. Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA,Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - John R. Horn
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Melanie M. Manis
- Department of Pharmacy Practice, McWhorter School of Pharmacy, Samford University, Birmingham, AL, USA
| | - Todd A. Miano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W. Oslin
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz Veterans Administration Medical Center, Philadelphia, PA, USA
| | - Thanh Phuong Pham Nguyen
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Samantha E. Soprano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Douglas J. Wiebe
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA,Penn Injury Science Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Charles E. Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
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8
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Leonard CE, Brensinger CM, Acton EK, Miano TA, Dawwas GK, Horn JR, Chung S, Bilker WB, Dublin S, Soprano SE, Phuong Pham Nguyen T, Manis MM, Oslin DW, Wiebe DJ, Hennessy S. Population-Based Signals of Antidepressant Drug Interactions Associated With Unintentional Traumatic Injury. Clin Pharmacol Ther 2021; 110:409-423. [PMID: 33559153 PMCID: PMC8316258 DOI: 10.1002/cpt.2195] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/14/2021] [Indexed: 11/11/2022]
Abstract
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.
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Affiliation(s)
- Charles E. Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Center for Therapeutic Effectiveness Research, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
| | - Colleen M. Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Emily K. Acton
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Todd A. Miano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Ghadeer K. Dawwas
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
| | - John R. Horn
- Department of Pharmacy, School of Pharmacy, University of Washington (Seattle, WA, US)
| | | | - Warren B. Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute (Seattle, WA, US)
- Department of Epidemiology, School of Public Health, University of Washington (Seattle, WA, US)
| | - Samantha E. Soprano
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Thanh Phuong Pham Nguyen
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
| | - Melanie M. Manis
- Department of Pharmacy Practice, McWhorter School of Pharmacy, Samford University (Birmingham, AL, US)
| | - David W. Oslin
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz Veterans Administration Medical Center (Philadelphia, PA, US)
| | - Douglas J. Wiebe
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
- Penn Injury Science Center, University of Pennsylvania (Philadelphia, PA, US)
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania (Philadelphia, PA, US)
- Center for Therapeutic Effectiveness Research, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
- Leonard Davis Institute of Health Economics, University of Pennsylvania (Philadelphia, PA, US)
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, PA, US)
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9
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Bykov K, Li H, Kim S, Vine SM, Re VL, Gagne JJ. Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data. Clin Pharmacol Ther 2021; 109:1353-1360. [PMID: 33245789 PMCID: PMC8058240 DOI: 10.1002/cpt.2119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 11/16/2020] [Indexed: 12/28/2022]
Abstract
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.
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Affiliation(s)
- Katsiaryna Bykov
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hu Li
- Global Patient Safety, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Sangmi Kim
- Global Patient Safety, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Seanna M. Vine
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Vincent Lo Re
- Division of Infectious Diseases, Department of Medicine and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joshua J. Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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10
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Pham Nguyen TP, Brensinger CM, Bilker WB, Hennessy S, Leonard CE. Evaluation of serious bleeding signals during concomitant use of clopidogrel and hypnotic drugs. Biomed Pharmacother 2021; 139:111559. [PMID: 33845372 DOI: 10.1016/j.biopha.2021.111559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/25/2021] [Accepted: 03/27/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND In a previous drug-drug interaction (DDI) screening study intended to generate hypotheses, clopidogrel + either eszopiclone or zolpidem (vs. clopidogrel alone) were associated with serious bleeding. OBJECTIVES To confirm or refute these DDI signals and examine associations with other hypnotics in an independent population of United States Medicaid beneficiaries METHODS: We employed a bi-directional self-controlled case series design in eligible individuals concomitantly exposed to one of 12 hypnotics (precipitants, exposures of interest) plus either clopidogrel (the object drug) or pravastatin (the negative control object drug). The outcome was hospital presentation with serious bleeding. Using conditional Poisson regression, we calculated confounder-adjusted rate ratios (RRs) and 95% confidence intervals for serious bleeding during clopidogrel + precipitant use (vs. clopidogrel alone). To distinguish a DDI from a precipitant's inherent effect on bleeding, we divided effect measures by the adjusted RR for the corresponding pravastatin + precipitant pair to obtain ratios of RR (RRRs). RESULTS Among 23,194 users of clopidogrel and 3824 of pravastatin who experienced serious bleeding during an active prescription for one of these agents, confounder-adjusted RRRs for serious bleeding were 6.63 (0.39-113.01) and 0.77 (0.53-1.11) with eszopiclone and zolpidem, respectively, whereas confounder-adjusted RRRs for other hypnotics ranged from 0.18 (0.04-0.85) for triazolam to 1.79 (0.16-20.44) for zaleplon. Statistical imprecision therefore precluded us from confirming or refuting these prior signals with eszopiclone and zolpidem. CONCLUSIONS While we could not confirm or refute previously identified DDI signals, numerically elevated RRRs for serious bleeding with several clopidogrel + hypnotic pairs warrant further examination.
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Affiliation(s)
- Thanh Phuong Pham Nguyen
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Translational Center for Excellence for Neuroepidemiology and Neurological Outcomes Research, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Colleen M Brensinger
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Warren B Bilker
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sean Hennessy
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Therapeutic Effectiveness Research, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Charles E Leonard
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Therapeutic Effectiveness Research, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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11
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Wu VCC, Wang CL, Huang YT, Tu HT, Kuo CF, Chen SW, Wen MS, Kuo CC, Chang SH. Bleeding associated with co-administration of clopidogrel and ACEi in patients undergoing PCI and DAPT. Atherosclerosis 2021; 324:76-83. [PMID: 33831672 DOI: 10.1016/j.atherosclerosis.2021.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 02/16/2021] [Accepted: 03/18/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND AIMS The coprescription of an angiotensin-converting enzyme inhibitor (ACEi) with clopidogrel reportedly increases bleeding risk. However, studies have not described such an increase in cases of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI). METHODS We analyzed electronic medical records of patients with discharge records of having undergone DAPT after PCI from a national health insurance claims database for January 1, 2006 to December 31, 2014. The date of PCI was the index date, and the primary outcome was major bleeding. The unit of analysis was one person-quarter. We compared patients who were prescribed with those not prescribed an ACEi in the cohort. A Poisson model with inverse probability of treatment weighting was fitted using generalized estimating equations to measure the risk of outcomes. RESULTS In total, 193,258 patients underwent DAPT after PCI; 46% had a coprescription of an ACEi. After screening, 170,775 patients (479,263 person-quarters) remained for analysis. The mean patient age was 65 ± 13 years, and 73.43% were men. In total, 79,739 prescriptions of an ACEi were written: 57%, 14.21%, 8.88%, 7.17%, and 4.68% were for captopril, ramipril, enalapril, perindopril, and imidapril, respectively. A concomitant prescription of an ACEi with clopidogrel was not associated with increased bleeding risk (adjusted rate ratio: 1.08, 99% confidence interval: 0.99-1.17). CONCLUSIONS The coadministration of an ACEi with clopidogrel after PCI is common. In this real-world cohort study, such coadministration was not associated with an increased risk of major bleeding in patients undergoing DAPT after PCI.
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Affiliation(s)
- Victor Chien-Chia Wu
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Chun-Li Wang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Yu-Tung Huang
- Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Hui-Tzu Tu
- Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Chang-Fu Kuo
- Division of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Taiwan; Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Shao-Wei Chen
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Ming-Shien Wen
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan
| | - Chi-Ching Kuo
- Institute of Organic and Polymeric Materials, National Taipei University of Technology, Taipei, Taiwan.
| | - Shang-Hung Chang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan; Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan; Graduate Institute of Nursing, Chang Gung University of Science and Technology, Taoyuan City, Taiwan.
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12
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Meid AD, Ruff C, Wirbka L, Stoll F, Seidling HM, Groll A, Haefeli WE. Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data. Clin Epidemiol 2020; 12:1223-1234. [PMID: 33173350 PMCID: PMC7646479 DOI: 10.2147/clep.s274466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/08/2020] [Indexed: 01/02/2023] Open
Abstract
When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.
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Affiliation(s)
- Andreas D Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Carmen Ruff
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Lucas Wirbka
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Felicitas Stoll
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany
| | - Hanna M Seidling
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany.,Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg 69120, Germany
| | - Andreas Groll
- Department of Statistics, TU Dortmund University, Dortmund 44227, Germany
| | - Walter E Haefeli
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg 69120, Germany.,Cooperation Unit Clinical Pharmacy, University of Heidelberg, Heidelberg 69120, Germany
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13
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Leonard CE, Brensinger CM, Pham Nguyen TP, Horn JR, Chung S, Bilker WB, Dublin S, Soprano SE, Dawwas GK, Oslin DW, Wiebe DJ, Hennessy S. Screening to identify signals of opioid drug interactions leading to unintentional traumatic injury. Biomed Pharmacother 2020; 130:110531. [PMID: 32739738 DOI: 10.1016/j.biopha.2020.110531] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/07/2020] [Accepted: 07/11/2020] [Indexed: 12/16/2022] Open
Abstract
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.
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Affiliation(s)
- Charles E Leonard
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Center for Therapeutic Effectiveness Research, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - Colleen M Brensinger
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Thanh Phuong Pham Nguyen
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - John R Horn
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, United States
| | - Sophie Chung
- AthenaHealth, Inc., Watertown, MA, United States
| | - Warren B Bilker
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States
| | - Samantha E Soprano
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ghadeer K Dawwas
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David W Oslin
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz Veterans Administration Medical Center, Philadelphia, PA, United States
| | - Douglas J Wiebe
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Injury Science Center, University of Pennsylvania, Philadelphia, PA, United States
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Center for Therapeutic Effectiveness Research, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Zhou M, Leonard CE, Brensinger CM, Bilker WB, Kimmel SE, Hecht TEH, Hennessy S. Pharmacoepidemiologic Screening of Potential Oral Anticoagulant Drug Interactions Leading to Thromboembolic Events. Clin Pharmacol Ther 2020; 108:377-386. [PMID: 32275326 DOI: 10.1002/cpt.1845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/19/2020] [Indexed: 12/14/2022]
Abstract
Drug-drug interactions (DDIs) with oral anticoagulants may lead to under-anticoagulation and increased risk of thromboembolism. Although warfarin is susceptible to numerous DDIs, few studies have examined DDIs resulting in thromboembolism or those involving direct-acting oral anticoagulants (DOACs). We aimed to identify medications that increase the rate of hospitalization for thromboembolic events when taken concomitantly with oral anticoagulants. We conducted a high-throughput pharmacoepidemiologic screening study using Optum Clinformatics Data Mart, 2000-2016. We performed self-controlled case series studies among adult users of oral anticoagulants (warfarin, dabigatran, rivaroxaban, apixaban, and edoxaban) with at least one hospitalization for a thromboembolic event. Among eligible patients, we identified all oral medications frequently co-prescribed with oral anticoagulants as potential interacting precipitants. Conditional Poisson regression was used to estimate rate ratios comparing precipitant exposed vs. unexposed time for each anticoagulant-precipitant pair. To minimize within-person confounding by indication for the precipitant, we used pravastatin as a negative control object drug. Multiple estimation was adjusted using semi-Bayes shrinkage. We screened 1,622 oral anticoagulant-precipitant drug pairs and identified 226 (14%) drug pairs associated with statistically significantly elevated risk of thromboembolism. Using pravastatin as the negative control object drug, this list was reduced to 69 potential DDI signals for thromboembolism, 33 (48%) of which were not documented in the DDI knowledge databases Lexicomp and/or Micromedex. There were more DDI signals associated with warfarin than DOACs. This study reproduced several previously documented oral anticoagulant DDIs and identified potential DDI signals that deserve to be examined in future etiologic studies.
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Affiliation(s)
- Meijia Zhou
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Charles E Leonard
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Colleen M Brensinger
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Warren B Bilker
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Stephen E Kimmel
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Todd E H Hecht
- Division of General Internal Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Sean Hennessy
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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