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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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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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Shi Y, Chiang CW, Unroe KT, Oyarzun-Gonzalez X, Sun A, Yang Y, Hunold KM, Caterino J, Li L, Donneyong M, Zhang P. Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults. Drug Saf 2024; 47:93-102. [PMID: 37935996 PMCID: PMC11256269 DOI: 10.1007/s40264-023-01370-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 11/09/2023]
Abstract
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.
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Affiliation(s)
- Yi Shi
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Kathleen T Unroe
- School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Aging Research, Regenstrief Institute, Indianapolis, IN, USA
| | | | - Anna Sun
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Yuedi Yang
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA
| | - Katherine M Hunold
- Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA
| | - Jeffrey Caterino
- Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Macarius Donneyong
- College of Pharmacy, The Ohio State University, 500 West 12th Ave., Columbus, OH, USA.
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA.
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Hennessy S, Berlin JA. Real-World Trends in the Evaluation of Medical Products. Am J Epidemiol 2023; 192:1-5. [PMID: 36217921 DOI: 10.1093/aje/kwac172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 01/11/2023] Open
Abstract
There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods. Developments in these areas are thought to improve the value of the evidence produced by such studies, and are prompting greater use of real-world evidence to inform clinical, regulatory, and reimbursement decisions.
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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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Chen C, Hennessy S, Brensinger CM, Acton EK, Bilker WB, Chung SP, Dawwas GK, Horn JR, Miano TA, Pham Nguyen TP, Leonard CE. Population-based screening to detect benzodiazepine drug-drug-drug interaction signals associated with unintentional traumatic injury. Sci Rep 2022; 12:15569. [PMID: 36114250 PMCID: PMC9481644 DOI: 10.1038/s41598-022-19551-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/31/2022] [Indexed: 11/08/2022] Open
Abstract
Drug interactions involving benzodiazepines and related drugs (BZDs) are increasingly recognized as a contributor to increased risk of unintentional traumatic injury. Yet, it remains unknown to what extent drug interaction triads (3DIs) may amplify BZDs' inherent injury risk. We identified BZD 3DI signals associated with increased injury rates by conducting high-throughput pharmacoepidemiologic screening of 2000-2019 Optum's health insurance data. Using self-controlled case series design, we included patients aged ≥ 16 years with an injury while using a BZD + co-dispensed medication (i.e., base pair). During base pair-exposed observation time, we identified other co-dispensed medications as candidate interacting precipitants. Within each patient, we compared injury rates during time exposed to the drug triad versus to the base pair only using conditional Poisson regression, adjusting for time-varying covariates. We calculated rate ratios (RRs) with 95% confidence intervals (CIs) and accounted for multiple estimation via semi-Bayes shrinkage. Among the 65,123 BZD triads examined, 79 (0.1%) were associated with increased injury rates and considered 3DI signals. Adjusted RRs for signals ranged from 3.01 (95% CI = 1.53-5.94) for clonazepam + atorvastatin with cefuroxime to 1.42 (95% CI = 1.00-2.02, p = 0.049) for alprazolam + hydrocodone with tizanidine. These signals may help researchers prioritize future etiologic studies to investigate higher-order BZD interactions.
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Affiliation(s)
- Cheng Chen
- Center for Real-World Effectiveness and Safety of Therapeutics, 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
| | - Sean Hennessy
- Center for Real-World Effectiveness and Safety of Therapeutics, 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 Real-World Effectiveness and Safety of Therapeutics, 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 Real-World Effectiveness and Safety of Therapeutics, 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 Real-World Effectiveness and Safety of Therapeutics, 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
| | | | - Ghadeer K Dawwas
- Center for Real-World Effectiveness and Safety of Therapeutics, 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
| | - John R Horn
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Todd A Miano
- Center for Real-World Effectiveness and Safety of Therapeutics, 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
| | - Thanh Phuong Pham Nguyen
- Center for Real-World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and Biostatistics, 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, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Charles E Leonard
- Center for Real-World Effectiveness and Safety of Therapeutics, 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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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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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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Nam YH, Brensinger CM, Bilker WB, Leonard CE, Hennessy S. Investigation of Concomitant Use of Alzheimer's Disease Drugs with Sulfonylureas and Serious Hypoglycemia. Epidemiology 2021; 32:e3-e5. [PMID: 33002962 PMCID: PMC7708459 DOI: 10.1097/ede.0000000000001263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Young Hee Nam
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
| | - Colleen M Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
| | - Warren B Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
| | - Charles E Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
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11
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Bykov K, Franklin JM, Li H, Gagne JJ. Comparison of Self-controlled Designs for Evaluating Outcomes of Drug-Drug Interactions: Simulation Study. Epidemiology 2020; 30:861-866. [PMID: 31430267 DOI: 10.1097/ede.0000000000001087] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
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.
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Affiliation(s)
- Katsiaryna Bykov
- From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jessica M Franklin
- From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Hu Li
- Eli Lilly and Company, Indianapolis, IN
| | - Joshua J Gagne
- From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
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12
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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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13
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Vajravelu RK, Scott FI, Mamtani R, Li H, Moore JH, Lewis JD. Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data. J Am Med Inform Assoc 2019; 25:780-789. [PMID: 29378062 DOI: 10.1093/jamia/ocx162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 12/31/2017] [Indexed: 12/20/2022] Open
Abstract
Objective Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods. Materials and Methods We used The Health Improvement Network database to study medications associated with Clostridium difficile infection (CDI). We performed case-control studies for each medication in The Health Improvement Network to obtain odds ratios (ORs) for association with CDI. We then calculated the association of each pharmacologic class with CDI using logistic regression and MCEA. We also performed simulation studies in which we assessed the sensitivity and specificity of logistic regression compared to MCEA for ORs 0.1-2.0. Results When analyzing pharmacologic classes using logistic regression, 47 of 110 pharmacologic classes were identified as associated with CDI. When analyzing pharmacologic classes using MCEA, only fluoroquinolones, a class of antibiotics with biologically confirmed causation, and heparin products were associated with CDI. In simulation, MCEA had superior specificity compared to logistic regression across all tested effect sizes and equal or better sensitivity for all effect sizes besides those close to null. Discussion Although these results demonstrate the promise of MCEA, additional studies that include inpatient administered medications are necessary for validation of the algorithm. Conclusions In clinical and simulation studies, MCEA demonstrated superior sensitivity and specificity for identifying pharmacologic classes associated with CDI compared to logistic regression.
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Affiliation(s)
- Ravy K Vajravelu
- Division of Gastroenterology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frank I Scott
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Division of Gastroenterology, Department of Medicine, University of Colorado Denver School of Medicine, Aurora, CO, USA
| | - Ronac Mamtani
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA.,Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James D Lewis
- Division of Gastroenterology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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14
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Zhou M, Leonard CE, Bilker WB, Hennessy S. The Self-Controlled Case Series Design as a Viable Alternative to Studying Clinically Relevant Drug Interactions. Clin Pharmacol Ther 2019; 107:321-322. [PMID: 31637695 DOI: 10.1002/cpt.1631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/12/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Meijia Zhou
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles E Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Warren B Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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15
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Salem MG, Aziz YMA, Elewa M, Elshihawy HA, Said MM. Synthesis and molecular modeling of novel non-sulfonylureas as hypoglycemic agents and selective ALR2 inhibitors. Bioorg Med Chem 2019; 27:3383-3389. [DOI: 10.1016/j.bmc.2019.06.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/11/2019] [Accepted: 06/12/2019] [Indexed: 11/24/2022]
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16
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Leonard CE, Zhou M, Brensinger CM, Bilker WB, Soprano SE, Pham Nguyen TP, Nam YH, Cohen JB, Hennessy S. Clopidogrel Drug Interactions and Serious Bleeding: Generating Real-World Evidence via Automated High-Throughput Pharmacoepidemiologic Screening. Clin Pharmacol Ther 2019; 106:1067-1075. [PMID: 31106397 DOI: 10.1002/cpt.1507] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 04/23/2019] [Indexed: 12/19/2022]
Abstract
Few population-based studies have examined bleeding associated with clopidogrel drug-drug interactions (DDIs). We sought to identify precipitant drugs taken concomitantly with clopidogrel (an object drug) that increased serious bleeding rates. We screened 2000-2015 Optum commercial health insurance claims to identify DDI signals. We performed self-controlled case series studies for clopidogrel plus precipitant pairs, examining associations with gastrointestinal bleeding or intracranial hemorrhage. To distinguish native bleeding effects of a precipitant, we reexamined associations using pravastatin as a negative control object drug. Among 431 analyses, 28 clopidogrel plus precipitant pairs were statistically significantly positively associated with serious bleeding. Ratios of rate ratios ranged from 1.13-3.94. Among these pairs, 13 were expected given precipitant drugs alone increased and/or were harbingers of serious bleeding. The remaining 15 pairs constituted new DDI signals, none of which are currently listed in two major DDI knowledge bases.
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Affiliation(s)
- Charles E Leonard
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Meijia Zhou
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Colleen M Brensinger
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Warren B Bilker
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samantha E Soprano
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thanh Phuong Pham Nguyen
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Young Hee Nam
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jordana B Cohen
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sean Hennessy
- Department of Biostatistics, Epidemiology, and Informatics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Bykov K, Schneeweiss S, Glynn RJ, Mittleman MA, Gagne JJ. A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data. Clin Pharmacol Ther 2019; 106:238-244. [PMID: 30663781 DOI: 10.1002/cpt.1376] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/03/2018] [Indexed: 12/31/2022]
Abstract
We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.
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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.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Robert J Glynn
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Murray A Mittleman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Joshua J Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Nam YH, Brensinger CM, Bilker WB, Leonard CE, Han X, Hennessy S. Serious Hypoglycemia and Use of Warfarin in Combination With Sulfonylureas or Metformin. Clin Pharmacol Ther 2018; 105:210-218. [PMID: 29885251 DOI: 10.1002/cpt.1146] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 05/28/2018] [Indexed: 01/11/2023]
Abstract
Prior research suggests that warfarin, when given concomitantly with some sulfonylureas, may increase the risk of serious hypoglycemia. However, the clinical significance remains unclear. We examined rate ratios (RRs) for the association between serious hypoglycemia and concomitant use of warfarin with either sulfonylureas or metformin using a self-controlled case series design and US Medicaid claims (supplemented with Medicare claims) from 1999 to 2011. Across all risk windows combined, warfarin was associated with an elevated rate of serious hypoglycemia when given concomitantly with glimepiride (RR, 1.47; 95% confidence interval (CI), 1.07-2.02) and metformin (RR, 1.73; 95% CI, 1.38-2.16). Particularly in the late risk window (>120 days since beginning concomitancy), most of the RRs for warfarin were elevated: glipizide (RR, 1.72; 95% CI, 1.29-2.29), glyburide (RR, 1.57; 95% CI, 1.15-2.15), metformin (RR, 2.26; 95% CI, 1.67-3.05), and glimepiride (RR, 1.56; 95% CI, 0.97-2.50). These results are consistent with a previously hypothesized hypoglycemic effect of warfarin in patients with type 2 diabetes through inhibition of the carboxylation of osteocalcin.
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Affiliation(s)
- Young Hee Nam
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Colleen M Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Warren B Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles E Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Xu Han
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sean Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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20
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Leonard CE, Han X, Brensinger CM, Bilker WB, Cardillo S, Flory JH, Hennessy S. Comparative risk of serious hypoglycemia with oral antidiabetic monotherapy: A retrospective cohort study. Pharmacoepidemiol Drug Saf 2017; 27:9-18. [PMID: 29108130 DOI: 10.1002/pds.4337] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 07/24/2017] [Accepted: 09/23/2017] [Indexed: 11/11/2022]
Abstract
PURPOSE To examine and compare risks of serious hypoglycemia among antidiabetic monotherapy-treated adults receiving metformin, a sulfonylurea, a meglitinide, or a thiazolidinedione. METHODS We performed a retrospective cohort study of apparently new users of monotherapy with metformin, glimepiride, glipizide, glyburide, pioglitazone, rosiglitazone, nateglinide, or repaglinide within a dataset of Medicaid beneficiaries from California, Florida, New York, Ohio, and Pennsylvania. We did not include users of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonists, or sodium-glucose co-transporter 2 inhibitors. We identified serious hypoglycemia outcomes within 180 days following new use using a validated, diagnosis-based algorithm. We calculated age- and sex-standardized outcome occurrence rates for each drug and generated propensity score-adjusted hazard ratios vs metformin using Cox proportional hazards regression. RESULTS The ranking of standardized occurrence rates of serious hypoglycemia was glyburide > glimepiride > glipizide > repaglinide > nateglinide > rosiglitazone > pioglitazone > metformin. Rates were increased for all study drugs at higher average daily doses. Adjusted hazard ratios (95% confidence intervals) vs metformin were 3.95 (3.66-4.26) for glyburide, 3.28 (2.98-3.62) for glimepiride, 2.57 (2.38-2.78) for glipizide, 2.03 (1.64-2.52) for repaglinide, 1.21 (0.89-1.66) for nateglinide, 0.90 (0.75-1.07) for rosiglitazone, and 0.80 (0.68-0.93) for pioglitazone. CONCLUSIONS Sulfonylureas were associated with the highest rates of serious hypoglycemia. Among all study drugs, the highest rate was seen with glyburide. Pioglitazone was associated with a lower adjusted hazard for serious hypoglycemia vs metformin, while rosiglitazone and nateglinide had hazards similar to that of metformin.
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Affiliation(s)
- Charles E Leonard
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xu Han
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colleen M Brensinger
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Warren B Bilker
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Neuropsychiatry Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Serena Cardillo
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James H Flory
- Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Healthcare Policy and Research, Division of Comparative Effectiveness, Weill Cornell Medicine, Cornell University, New York, NY, USA.,Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sean Hennessy
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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21
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Hennessy S, Leonard CE, Gagne JJ, Flory JH, Han X, Brensinger CM, Bilker WB. Pharmacoepidemiologic Methods for Studying the Health Effects of Drug-Drug Interactions. Clin Pharmacol Ther 2015; 99:92-100. [PMID: 26479278 DOI: 10.1002/cpt.277] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 10/01/2015] [Accepted: 10/14/2015] [Indexed: 12/13/2022]
Abstract
A drug-drug interaction (DDI) occurs when one or more drugs affect the pharmacokinetics (the body's effect on the drug) and/or pharmacodynamics (the drug's effect on the body) of one or more other drugs. Pharmacoepidemiologic studies are the principal way of studying the health effects of potential DDIs. This article discusses aspects of pharmacoepidemiologic research designs that are particularly salient to the design and interpretation of pharmacoepidemiologic studies of DDIs.
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Affiliation(s)
- S Hennessy
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - C E Leonard
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - J J Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - J H Flory
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Division of Comparative Effectiveness and Outcomes Research, Department of Healthcare Research and Policy, Weill Cornell Medical College, New York, New York, USA
- Endocrinology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - X Han
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - C M Brensinger
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - W B Bilker
- Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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