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McElroy SL, Coloma PM, Berger B, Guerdjikova AI, Joyce JM, Liebowitz MR, Pain S, Rabasa C. Efficacy, safety, and tolerability of nivasorexant in adults with binge-eating disorder: A randomized, Phase II proof of concept trial. Int J Eat Disord 2023; 56:2120-2130. [PMID: 37584285 DOI: 10.1002/eat.24039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/17/2023]
Abstract
OBJECTIVE This Phase II, placebo-controlled, double-blind study investigated the efficacy, safety, and tolerability of nivasorexant in the treatment of adults with moderate to severe binge-eating disorder (BED). METHODS Adults meeting the DSM-5 BED criteria were randomized 1:1 to placebo or nivasorexant (100 mg b.i.d.). The primary endpoint was the change from baseline to Week 12 in the number of binge eating (BE) days per week. Exploratory efficacy endpoints included cessation of BE in the last 4 weeks of treatment; and change from baseline to Week 12 in the number of BE episodes/week, the clinician global impression (CGI) of change, the Yale-Brown Obsessive-Compulsive Scale modified for BE, and the Hamilton rating scale for depression (HAMD-17). Key safety outcomes included treatment-emergent adverse events (TEAEs) and adverse events of special interest (i.e., somnolence and fatigue). RESULTS Sixty-eight participants were randomized to each treatment arm. The change from baseline to Week 12 in the number of BE days/week was the same for placebo (least squares mean [LSM]: -2.93) and nivasorexant (LSM: -2.93), with no difference between the treatment groups (LSM difference = .000 [95% confidence interval (CI): -.69, .69], p = .9992). Furthermore, no differences between treatment groups were observed in the exploratory efficacy endpoints. Nivasorexant was well tolerated; the overall incidence of TEAEs was balanced between treatment groups, and the frequency of somnolence and fatigue in the nivasorexant group were similar to placebo. DISCUSSION In this proof-of-concept study, 100 mg b.i.d. nivasorexant did not improve BE in adults with moderate to severe BED. PUBLIC SIGNIFICANCE The results of this Phase II study indicate that nivasorexant was well tolerated in adults with BED, but did not improve binge eating behavior over placebo. Further research is needed to improve our understanding of the role of the orexin-1 receptor in BED.
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Affiliation(s)
- Susan L McElroy
- Lindner Center of HOPE, Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | | | - Anna I Guerdjikova
- Lindner Center of HOPE, Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - J Mark Joyce
- Clinical Neuroscience Solutions, Inc., Jacksonville, Florida, USA
| | - Michael R Liebowitz
- Medical Research Network, New York, New York, USA
- Department of Psychiatry, Columbia University, New York, New York, USA
| | - Scott Pain
- Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland
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Beckenstrom AC, Coloma PM, Dawson GR, Finlayson AK, Malik A, Post A, Steiner MA, Potenza MN. Use of experimental medicine approaches for the development of novel psychiatric treatments based on orexin receptor modulation. Neurosci Biobehav Rev 2023; 147:105107. [PMID: 36828161 PMCID: PMC10165155 DOI: 10.1016/j.neubiorev.2023.105107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/08/2023] [Accepted: 02/18/2023] [Indexed: 02/25/2023]
Abstract
Despite progress in understanding the pathological mechanisms underlying psychiatric disorders, translation from animal models into clinical use remains a significant bottleneck. Preclinical studies have implicated the orexin neuropeptide system as a potential target for psychiatric disorders through its role in regulating emotional, cognitive, and behavioral processes. Clinical studies are investigating orexin modulation in addiction and mood disorders. Here we review performance-outcome measures (POMs) arising from experimental medicine research methods which may show promise as markers of efficacy of orexin receptor modulators in humans. POMs provide objective measures of brain function, complementing patient-reported or clinician-observed symptom evaluation, and aid the translation from preclinical to clinical research. Significant challenges include the development, validation, and operationalization of these measures. We suggest that collaborative networks comprising clinical practitioners, academics, individuals working in the pharmaceutical industry, drug regulators, patients, patient advocacy groups, and other relevant stakeholders may provide infrastructure to facilitate validation of experimental medicine approaches in translational research and in the implementation of these approaches in real-world clinical practice.
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Affiliation(s)
- Amy C Beckenstrom
- P1vital Ltd, Manor House, Howbery Business Park, Wallingford OX10 8BA, UK.
| | - Preciosa M Coloma
- Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, Allschwil 4123, Switzerland
| | - Gerard R Dawson
- P1vital Ltd, Manor House, Howbery Business Park, Wallingford OX10 8BA, UK
| | - Ailidh K Finlayson
- P1vital Ltd, Manor House, Howbery Business Park, Wallingford OX10 8BA, UK; Department of Psychology, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - Asad Malik
- P1vital Ltd, Manor House, Howbery Business Park, Wallingford OX10 8BA, UK
| | - Anke Post
- Corlieve Therapeutics, Swiss Innovation Park, Hegenheimermattweg 167A, 4123 Allschwil, Switzerland
| | | | - Marc N Potenza
- Departments of Psychiatry and Neuroscience and the Child Study Center, Yale School of Medicine, 1 Church Street, Room 726, New Haven, CT 06510, USA; Connecticut Mental Health Center, 34 Park Street, New Haven, CT 06519, USA; Connecticut Council on Problem Gambling, Wethersfield, CT, USA; The Wu Tsai Institute, Yale University, 100 College St, New Haven, CT 06510, USA
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Pavlik VN, Burnham SC, Kass JS, Helmer C, Palmqvist S, Vassilaki M, Dartigues JF, Hansson O, Masters CL, Pérès K, Petersen RC, Stomrud E, Butler L, Coloma PM, Teitsma XM, Doody R, Sano M. Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD): A Report of an International Research Collaboration Network. J Alzheimers Dis 2021; 85:31-45. [PMID: 34776434 PMCID: PMC8842789 DOI: 10.3233/jad-210525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Longitudinal observational cohort studies are being conducted worldwide to understand cognition, biomarkers, and the health of the aging population better. Cross-cohort comparisons and networks of registries in Alzheimer’s disease (AD) foster scientific exchange, generate insights, and contribute to the evolving clinical science in AD. A scientific working group was convened with invited investigators from established cohort studies in AD, in order to form a research collaboration network as a resource to address important research questions. The Connecting Cohorts to Diminish Alzheimer’s Disease (CONCORD-AD) collaboration network was created to bring together global resources and expertise, to generate insights and improve understanding of the natural history of AD, to inform design of clinical trials in all disease stages, and to plan for optimal patient access to disease-modifying therapies once they become available. The network brings together expertise and data insights from 7 cohorts across Australia, Europe, and North America. Notably, the network includes populations recruited through memory clinics as well as population-based cohorts, representing observations from individuals across the AD spectrum. This report aims to introduce the CONCORD-AD network, providing an overview of the cohorts involved, reporting the common assessments used, and describing the key characteristics of the cohort populations. Cohort study designs and baseline population characteristics are compared, and available cognitive, functional, and neuropsychiatric symptom data, as well as the frequency of biomarker assessments, are summarized. Finally, the challenges and opportunities of cross-cohort studies in AD are discussed.
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Affiliation(s)
- Valory N Pavlik
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Samantha C Burnham
- The Australian eHealth Research Centre, CSIRO Health and Biosecurity, Melbourne, VIC, Australia
| | - Joseph S Kass
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Catherine Helmer
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR, Bordeaux, France
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Jean-François Dartigues
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR, Bordeaux, France.,Department of Neurology, Memory Consultation, Bordeaux University Hospital, Bordeaux, France
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Colin L Masters
- The Florey Institute and The University of Melbourne, Parkville, VIC, Australia
| | - Karine Pérès
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR, Bordeaux, France
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA.,Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Lesley Butler
- Product Development Personalised Health Care - Data Science, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Preciosa M Coloma
- Product Development Personalised Health Care - Data Science, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Xavier M Teitsma
- Product Development Personalised Health Care - Data Science, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Rachelle Doody
- Product Development Neuroscience, F. Hoffmann-La Roche Ltd, Basel, Switzerland.,Product Development Neuroscience, Genentech, Inc., South San Francisco, CA, USA
| | - Mary Sano
- Department of Psychiatry, Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,James J. Peters VA Medical Center, Bronx, NY, USA
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Gavrielov-Yusim N, Barer Y, Martinec M, Siadimas A, Roumpanis S, Furby H, Goldshtein I, Jan A, Coloma PM. Huntington's Disease in Israel: A Population-Based Study Using 20 Years of Routinely-Collected Healthcare Data. J Huntingtons Dis 2021; 10:469-477. [PMID: 34602495 DOI: 10.3233/jhd-210500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Huntington's disease (HD) is a rare, genetic, neurodegenerative disease. Obtaining population-level data on epidemiology and disease management is challenging. OBJECTIVE To investigate the epidemiology, clinical manifestations, treatment, and healthcare utilization of patients with HD in Israel. METHODS Retrospective population-based cohort study, including 20 years of routinely collected data from Maccabi Healthcare Services, an insurer and healthcare provider for one-quarter of the Israeli population. RESULTS The study cohort included 109 adult patients (aged ≥18 years) diagnosed with HD, with mean age of 49.9 years and 56%females. The most common HD-related conditions were anxiety (40%), behavioral problems (34%), sleep disorders (21%), and falls (13%). Annual incidence rates for HD ranged from 0.17 to 1.34 per 100,000 from 2000 to 2018; the 2018 crude prevalence in adults was 4.36 per 100,000. Median survival from diagnosis was approximately 12 years (95%CI: 10.4-15.3). The most frequent symptomatic treatments were antidepressants (69%), antipsychotics (63%), and tetrabenazine (63%), the only drug approved for the treatment of HD chorea in Israel during the examined period. Patterns of healthcare utilization changed as disease duration increased, reflected by increased frequency of emergency department visits and home visits. CONCLUSION This retrospective population-based study provides insights into the prevalence, incidence, clinical profile, survival, and resource utilization of patients with HD in ethnically diverse Israel. The findings in this study are generally consistent with the international literature and demonstrate the value of routinely collected healthcare data as a complementary resource in HD research.
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Affiliation(s)
- Natalie Gavrielov-Yusim
- Product Development Personalized Health Care - Data Science, Roche Pharmaceuticals, Hod HaSharon, Israel
| | - Yael Barer
- Maccabitech Institute for Research and Innovation, Maccabi Healthcare Services, Tel Aviv, Israel
| | - Michael Martinec
- Product Development Personalized Health Care - Data Science, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Athanasios Siadimas
- Product Development Personalized Health Care - Data Science, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Spyros Roumpanis
- Product Development Personalized Health Care - Data Science, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Hannah Furby
- Product Development Personalized Health Care - Data Science, Roche, Welwyn, UK
| | - Inbal Goldshtein
- Maccabitech Institute for Research and Innovation, Maccabi Healthcare Services, Tel Aviv, Israel.,Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Asif Jan
- Product Development Personalized Health Care - Data Science, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Preciosa M Coloma
- Product Development Personalized Health Care - Data Science, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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5
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Leuzy A, Klein G, Ossenkoppele R, Mattsson N, Janelidze S, Palmqvist S, Strandberg O, Coloma PM, Borroni E, Stomrud E, Smith R, Hansson O. Longitudinal [
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F]RO948 PET SUVR is associated with Aβ accumulation and baseline tau pathology. Alzheimers Dement 2020. [DOI: 10.1002/alz.046474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Gregory Klein
- Roche Pharma Research and Early Development Roche Innovation Center Basel Switzerland
| | | | | | | | | | | | - Preciosa M Coloma
- Real World Data Science (RWD‐S) Neuroscience and Established Products F. Hoffmann‐La Roche Ltd. Pharmaceuticals Division Basel Switzerland
| | | | - Erik Stomrud
- Clinical Memory Research Unit Department of Clinical Sciences Malmö Lund University Malmö Sweden
| | - Ruben Smith
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit Department of Clinical Sciences Malmö Lund University Malmö Sweden
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Burnham SC, Coloma PM, Li QX, Collins S, Savage G, Laws S, Doecke J, Maruff P, Martins RN, Ames D, Rowe CC, Masters CL, Villemagne VL. Application of the NIA-AA Research Framework: Towards a Biological Definition of Alzheimer's Disease Using Cerebrospinal Fluid Biomarkers in the AIBL Study. J Prev Alzheimers Dis 2020; 6:248-255. [PMID: 31686097 DOI: 10.14283/jpad.2019.25] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND The National Institute on Aging and Alzheimer's Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer's disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer's disease. OBJECTIVES To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)). DESIGN Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers. SETTING Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study. PARTICIPANTS One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays. INTERVENTION (if any): Not applicable. MEASUREMENTS Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test - Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores. RESULTS Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework's definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. CONCLUSIONS Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials.
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Affiliation(s)
- S C Burnham
- Samantha C. Burnham, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia, , Tel.: +61399627162
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Vassilaki M, Aakre JA, Kremers WK, Mielke MM, Geda YE, Alhurani RE, Dutt T, Machulda MM, Knopman DS, Vemuri P, Coloma PM, Schauble B, Lowe VJ, Jack CR, Petersen RC, Roberts RO. The Association of Multimorbidity With Preclinical AD Stages and SNAP in Cognitively Unimpaired Persons. J Gerontol A Biol Sci Med Sci 2019; 74:877-883. [PMID: 30124772 PMCID: PMC6521911 DOI: 10.1093/gerona/gly149] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Multimorbidity (defined as ≥2 chronic conditions) has been associated with increased risk of mild cognitive impairment and cross-sectionally with imaging biomarkers of neurodegeneration in cognitively unimpaired persons aged ≥70 years. Its association with preclinical Alzheimer's disease stages has not been studied in detail yet. The objective of the study was to assess the cross-sectional association of multimorbidity with preclinical Alzheimer's disease stages and suspected non-amyloid pathophysiology in cognitively unimpaired participants of the Mayo Clinic Study of Aging (≥50 years of age). METHODS The study included 1,535 cognitively unimpaired participants with multimorbidity, 11C-PiB positron emission topography and magnetic resonance imaging data available. Abnormal (elevated) 11C-PiB-positron emission topography retention ratio (A+; standardized uptake value ratio >1.42) and abnormal (reduced) Alzheimer's disease signature cortical thickness (N+; <2.67 mm) were used to define biomarker combinations (A-N-, A+N-, A-N+, A+N+). Chronic medical conditions were ascertained by using the Rochester Epidemiology Project medical records linkage system and International Classification of Diseases criteria. Cross-sectional associations were examined using multinomial logistic regression models adjusting for age, sex, education, and apolipoprotein E ɛ4 allele status. RESULTS Frequency of A+, N+, A+N+, and A-N+ biomarker groups increased significantly with increasing number of chronic conditions. Multimorbidity was significantly associated with A+N+ (vs A-N-; odds ratio, 1.76, 95% confidence interval 1.02, 2.90) and A-N+ (vs A-N-; odds ratio, 2.16, 95% confidence interval 1.47, 3.18). There was a dose-response relationship between increasing number of chronic conditions (eg, 0-1, 2-3, and 4+) and the odds of A+N+ and A-N+ (vs A-N-). CONCLUSIONS Multimorbidity was associated with biomarker combinations that included neurodegeneration with or without elevated amyloid deposition (ie, A-N+, A+N+). The associations should be validated in longitudinal studies.
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Affiliation(s)
- Maria Vassilaki
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Jeremiah A Aakre
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Walter K Kremers
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Michelle M Mielke
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Yonas E Geda
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, Arizona
- Department of Neurology, Mayo Clinic, Scottsdale, Arizona
| | | | - Taru Dutt
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Preciosa M Coloma
- Real World Data Science, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Ronald C Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Rosebud O Roberts
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
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Vassilaki M, Aakre JA, Kremers WK, Mielke MM, Geda YE, Machulda MM, Knopman DS, Coloma PM, Schauble B, Vemuri P, Lowe VJ, Jack CR, Petersen RC, Roberts RO. Association Between Functional Performance and Alzheimer's Disease Biomarkers in Individuals Without Dementia. J Am Geriatr Soc 2018; 66:2274-2281. [PMID: 30462843 DOI: 10.1111/jgs.15577] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/24/2018] [Accepted: 07/24/2018] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To examine the cross-sectional association between functional performance and Alzheimer's disease (AD) neuroimaging biomarkers in individuals without dementia (cognitively unimpaired (CU), and those with mild cognitive impairment (MCI)). DESIGN Cross-sectional. SETTING Olmsted County, Minnesota. PARTICIPANTS Population-based Mayo Clinic Study of Aging (MCSA) participants (aged ≥ 50, mean age 71.3 ± 10.2; 53.4% male; 28.3% apolipoprotein (APO)E ε4 allele carriers, 1,578 CU, 204 MCI) who underwent 11 C-Pittsburgh compound B (11 C-PiB) positron emission tomography (PET) (N=1,782). MEASUREMENTS We defined an abnormal (high) 11 C-PiB-PET retention ratio as a standardized uptake value ratio greater than 1.42 (high amyloid; A+), abnormal (reduced) AD signature cortical thickness (neurodegeneration; N+) as less than 2.67 mm (MRI measurement), and biomarker groups according to the combination of abnormality (or not) for amyloid accumulation (A+/A-) and neurodegeneration (N+/N-). Functional performance was assessed using the Clinical Dementia Rating (CDR) Sum of Boxes (SOB) for functional domains and the Functional Activities Questionnaire (FAQ). RESULTS Participants with a CDR-SOB (functional) score greater than 0 were almost 4 times as likely to have N + (odds ratio (OR)=3.92, 95% confidence interval (CI)=1.77-8.67, adjusting for age, sex, education, global cognitive z-score, and APOE ε4 allele status; p<.001) and those with a FAQ score greater than 0 were 1.5 times as likely to have A + (OR=1.48, 95% CI=1.04-2.11, p=.03). Higher FAQ scores were associated with greater odds of A+N + and A-N + in CU participants. CONCLUSION The findings of this cross-sectional study supplement limited available information that supports an association between functional performance and AD neuroimaging biomarkers very early in the dementia pathophysiology. The associations should be validated in longitudinal studies. J Am Geriatr Soc 66:2274-2281, 2018.
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Affiliation(s)
- Maria Vassilaki
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota
| | - Jeremiah A Aakre
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota
| | - Walter K Kremers
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota
| | - Michelle M Mielke
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.,Departments of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Yonas E Geda
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.,Department of Psychiatry and Psychology and Department of Neurology, Mayo Clinic, Scottsdale, Arizona
| | - Mary M Machulda
- Departments of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Ronald C Petersen
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.,Departments of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Rosebud O Roberts
- Departments of Health Sciences Research, Mayo Clinic, Mayo Clinic, Rochester, Minnesota.,Departments of Neurology, Mayo Clinic, Rochester, Minnesota
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Patadia VK, Schuemie MJ, Coloma PM, Herings R, van der Lei J, Sturkenboom M, Trifirò G. Can Electronic Health Records Databases Complement Spontaneous Reporting System Databases? A Historical-Reconstruction of the Association of Rofecoxib and Acute Myocardial Infarction. Front Pharmacol 2018; 9:594. [PMID: 29928230 PMCID: PMC5997784 DOI: 10.3389/fphar.2018.00594] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/17/2018] [Indexed: 11/30/2022] Open
Abstract
Background: Several initiatives have assessed if mining electronic health records (EHRs) may accelerate the process of drug safety signal detection. In Europe, Exploring and Understanding Adverse Drug Reactions (EU-ADR) Project Focused on utilizing clinical data from EHRs of over 30 million patients from several European countries. Rofecoxib is a prescription COX-2 selective Non-Steroidal Anti-Inflammatory Drugs (NSAID) approved in 1999. In September 2004, the manufacturer withdrew rofecoxib from the market because of safety concerns. In this study, we investigated if the signal concerning rofecoxib and acute myocardial infarction (AMI) could have been identified in EHR database (EU-ADR project) earlier than spontaneous reporting system (SRS), and in advance of rofecoxib withdrawal. Methods: Data from the EU-ADR project and WHO-VigiBase (for SRS) were used for the analysis. Signals were identified when respective statistics exceeded defined thresholds. The SRS analyses was conducted two ways- based on the date the AMI events with rofecoxib as a suspect medication were entered into the database and also the date that the AMI event occurred with exposure to rofecoxib. Results: Within the databases participating in EU-ADR it was possible to identify a strong signal concerning rofecoxib and AMI since Q3 2000 [RR LGPS = 4.5 (95% CI: 2.84–6.72)] and peaked to 4.8 in Q4 2000. In WHO-VigiBase, for AMI term grouping, the EB05 threshold of 2 was crossed in the Q4 2004 (EB05 = 2.94). Since then, the EB05 value increased consistently and peaked in Q3 2006 (EB05 = 48.3) and then again in Q2 2008 (EB05 = 48.5). About 93% (2260 out of 2422) of AMIs reported in WHO-VigiBase database actually occurred prior to the product withdrawal, however, they were reported after the risk minimization/risk communication efforts. Conclusion: In this study, EU-EHR databases were able to detect the AMI signal 4 years prior to the SRS database. We believe that for events that are consistently documented in EHR databases, such as serious events or events requiring in-patient medical intervention or hospitalization, the signal detection exercise in EHR would be beneficial for newly introduced medicinal products on the market, in addition to the SRS data.
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Affiliation(s)
- Vaishali K Patadia
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.,Sanofi, Bridgewater, NJ, United States
| | - Martijn J Schuemie
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | | | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Miriam Sturkenboom
- Julius Global Health, University Medical Center Utrecht, Utrecht, Netherlands
| | - Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
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10
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Bos I, Vos SJ, Frölich L, Kornhuber J, Wiltfang J, Maier W, Peters O, Rüther E, Engelborghs S, Niemantsverdriet E, De Roeck EE, Tsolaki M, Freund-Levi Y, Johannsen P, Vandenberghe R, Lleó A, Alcolea D, Frisoni GB, Galluzzi S, Nobili F, Morbelli S, Drzezga A, Didic M, van Berckel BN, Salmon E, Bastin C, Dauby S, Santana I, Baldeiras I, de Mendonça A, Silva D, Wallin A, Nordlund A, Coloma PM, Wientzek A, Alexander M, Novak GP, Gordon MF, Wallin ÅK, Hampel H, Soininen H, Herukka SK, Scheltens P, Verhey FR, Visser PJ. The frequency and influence of dementia risk factors in prodromal Alzheimer's disease. Neurobiol Aging 2017; 56:33-40. [PMID: 28482212 DOI: 10.1016/j.neurobiolaging.2017.03.034] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 03/29/2017] [Accepted: 03/31/2017] [Indexed: 11/19/2022]
Abstract
We investigated whether dementia risk factors were associated with prodromal Alzheimer's disease (AD) according to the International Working Group-2 and National Institute of Aging-Alzheimer's Association criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox-regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hypercholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with mild cognitive impairment. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e., preclinical) of AD.
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Affiliation(s)
- Isabelle Bos
- Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, Netherlands.
| | - Stephanie J Vos
- Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, Netherlands
| | - Lutz Frölich
- On behalf of German Dementia Competence Network; Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Johannes Kornhuber
- On behalf of German Dementia Competence Network; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
| | - Jens Wiltfang
- On behalf of German Dementia Competence Network; Department of Psychiatry and Psychotherapy, University Medical Center (UMC), Georg-August-University, Göttingen, Germany
| | - Wolfgang Maier
- On behalf of German Dementia Competence Network; Department of Psychiatry and Psychotherapy, University of Bonn, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Oliver Peters
- On behalf of German Dementia Competence Network; Department of Psychiatry and Psychotherapy, Charité Berlin, Berlin, Germany
| | - Eckhart Rüther
- On behalf of German Dementia Competence Network; Department of Psychiatry and Psychotherapy, University of Göttingen, Göttingen, Germany
| | - Sebastiaan Engelborghs
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
| | - Ellen Elisa De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium; Department of Clinical and Lifespan Psychology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Memory and Dementia Center, "G Papanicolau" General Hospital, Thessaloniki, Greece
| | - Yvonne Freund-Levi
- Division of Clinical Geriatrics, Department of Neurobiology, Caring Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden; Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rik Vandenberghe
- Department of Neurology, University of Hospital Leuven, Leuven, Belgium; Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | - Alberto Lleó
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | - Giovanni B Frisoni
- On behalf of the EADC-PET consortium; Geneva Neuroscience Center, University Hospital and University of Geneva, Geneva, Switzerland; IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Flavio Nobili
- On behalf of the EADC-PET consortium; Clinical Neurology, Department of Neurosciences (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Silvia Morbelli
- On behalf of the EADC-PET consortium; Nuclear Medicine, Department of Health Science (DISSAL), University of Genoa IRCCS AOU San Martino-IST, Genoa, Italy
| | - Alexander Drzezga
- On behalf of the EADC-PET consortium; Department of Nuclear Medicine, University of Cologne, Cologne, Germany
| | - Mira Didic
- On behalf of the EADC-PET consortium; AP-HM Hôpitaux de la Timone, Service de Neurologie et Neuropsychologie, Marseille, France; Aix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Bart N van Berckel
- On behalf of the EADC-PET consortium; Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Eric Salmon
- Department of Neurology and Memory Clinic, CHU Liège, Liège, Belgium; GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
| | | | - Solene Dauby
- Department of Neurology and Memory Clinic, CHU Liège, Liège, Belgium
| | - Isabel Santana
- Department of Neurology and Memory Clinic, CHU Liège, Liège, Belgium
| | - Inês Baldeiras
- Center for Neuroscience and Cell Biology, Faculty of Medicine, Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Alexandre de Mendonça
- Institute of Molecular Medicine and Faculty of Medicine, University of Lisbon, Portugal
| | - Dina Silva
- Institute of Molecular Medicine and Faculty of Medicine, University of Lisbon, Portugal
| | - Anders Wallin
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Arto Nordlund
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Preciosa M Coloma
- Real World Data Science (RWD-S) Neuroscience and Established Products, F. Hoffmann-La Roche Ltd. Pharmaceuticals Division, Basel, Switzerland
| | - Angelika Wientzek
- PDB RWD (Real World Data) Team, Roche Products Limited, Welwyn Garden City, UK; Epidemiologische Beratung und Literatur-Recherche "conepi", Herrsching, Germany
| | - Myriam Alexander
- PDB RWD (Real World Data) Team, Roche Products Limited, Welwyn Garden City, UK
| | - Gerald P Novak
- Janssen Pharmaceutical Research and Development, Titusville, NJ, USA
| | | | - Åsa K Wallin
- Department of Clinical Sciences Malmö, Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - Harald Hampel
- Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, AXA Research Fund & UPMC Chair, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, 47 Boulevard de l'Hôpital, Paris, CEDEX 13, France
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland and Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine, Neurology, University of Eastern Finland and Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Frans R Verhey
- Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, Netherlands
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, Netherlands; Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands
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11
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Pacurariu AC, Coloma PM, Gross-Martirosyan L, Sturkenboom MC, Straus SM. Decision making in drug safety-a literature review of criteria used to prioritize newly detected safety issues. Pharmacoepidemiol Drug Saf 2016; 26:327-334. [PMID: 27878889 DOI: 10.1002/pds.4128] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 09/08/2016] [Accepted: 10/15/2016] [Indexed: 11/06/2022]
Abstract
PURPOSE In drug safety, there is a lack of guidance on how prioritization of safety issues should be performed. The aim of this literature review is to provide an overview of criteria used for signal prioritization and of the associated decision support frameworks. METHODS A search strategy was constructed to identify relevant articles in Medline/Embase databases from the period from 1 January 1995 to 31 August 2015. The prioritization criteria were extracted and classified in relevant categories. RESULTS From an initial set of 63 articles, 11 were retained for full review. The articles mentioned 48 criteria used in the prioritization process, with a median of six criteria per study [range: 1-16]. More than half of the criteria (63%), referred to strength of evidence while 19% related to public health impact, 14% to general public and media attention and 4% to novelty of the drug event association. Fifteen criteria were tested for predictive value with 11 showing positive results, most of them from the strength of evidence category. Six decision-making frameworks are presented, which incorporate criteria from various categories. Five of these frameworks were tested against expert decisions or by other means, but only in one database each and for a limited set of products. CONCLUSIONS There is a wide range of prioritization criteria described in the literature; however, few of them demonstrated predictive value. Many criteria with predictive value were related to strength of evidence category and to novelty. There were few attempts at integrating different criteria in decision support frameworks. Five of the frameworks were tested for validity and showed usefulness, while at least three are already in use for prioritization. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Alexandra C Pacurariu
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Dutch Medicines Evaluation Board, Utrecht, The Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Miriam C Sturkenboom
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sabine M Straus
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Dutch Medicines Evaluation Board, Utrecht, The Netherlands
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12
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Schuemie MJ, Trifirò G, Coloma PM, Ryan PB, Madigan D. Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure-adjusted self-controlled case series. Stat Methods Med Res 2016; 25:2577-2592. [DOI: 10.1177/0962280214527531] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Most approaches used in postmarketing drug safety monitoring, including spontaneous reporting and statistical risk identification using electronic health care records, are primarily suited to pick up only acute adverse drug effects. With the availability of increasingly larger electronic health record and administrative claims databases comes the opportunity to monitor for potential adverse effects that occur only after prolonged exposure to a drug, but analysis methods are lacking. We propose an adaptation of the self-controlled case series design that uses the notion of accumulated exposure to capture long-term effects of drugs and evaluate extensions to correct for age and recurrent events. Several variations of the approach are tested on simulated data and two large insurance claims databases. To evaluate performance a set of positive and negative control drug–event pairs was created by medical experts based on drug product labels and review of the literature. Performance on the real data was measured using the area under the receiver operator characteristics curve. The best performing method achieved an area under the receiver operator characteristics curve of 0.86 in the largest database using a spline model, adjustment for age, and ignoring recurrent events, but it appears this performance can only be achieved with very large data sets.
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Affiliation(s)
- Martijn J Schuemie
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA
| | - Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Patrick B Ryan
- Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA
- Janssen Research and Development LLC, Titusville, USA
| | - David Madigan
- Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, USA
- Department of Statistics, Columbia University, New York, USA
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13
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Coloma PM, de Ridder M, Bezemer I, Herings RMC, Gini R, Pecchioli S, Scotti L, Rijnbeek P, Mosseveld M, van der Lei J, Trifirò G, Sturkenboom M. Risk of cardiac valvulopathy with use of bisphosphonates: a population-based, multi-country case-control study. Osteoporos Int 2016; 27:1857-67. [PMID: 26694594 PMCID: PMC4839043 DOI: 10.1007/s00198-015-3441-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 11/23/2015] [Indexed: 01/25/2023]
Abstract
UNLABELLED Analyses of healthcare data from 30 million individuals in three countries showed that current use of bisphosphonates may be associated with a small increased risk of cardiac valvulopathy (vs. those not exposed within the previous year), although confounding cannot be entirely ruled out. The observed tendency for decreased valvulopathy risk with cumulative duration of bisphosphonate use >6 months may even indicate a protective effect with prolonged use. Further studies are still needed to evaluate whether bisphosphonates increase or decrease the risk of valvulopathy. INTRODUCTION A signal of cardiac valve disorders with use of bisphosphonates was identified in the literature and EudraVigilance database, which contains reports of suspected adverse drug reactions from worldwide sources. The aim of this study was to evaluate the association using population-based healthcare data. METHODS This was a case-control study among users of bisphosphonates and other drugs for osteoporosis in six healthcare databases covering over 30 million individuals in Italy, Netherlands and the UK from 1996 to 2012. Prescriptions/dispensations were used to assess drug exposure. Newly diagnosed cases of cardiac valvulopathy were identified via disease codes/free-text search. Controls were matched to each case by age, sex, database and index date. Adjusted odds ratios (ORs) were estimated using conditional logistic regression for the pooled data and meta-analysis of individual database risk estimates. RESULTS A small but statistically significant association was found between exposure to bisphosphonates as a class and risk of valvulopathy. Overall risk was 18 % higher (95 % CI 12-23 %) in those currently exposed to any bisphosphonate (mainly alendronate and risedronate) vs. those not exposed within the previous year. Risk of valve regurgitation was 14 % higher (95 % CI 7-22 %). Decreased valvulopathy risk was observed with longer cumulative duration of bisphosphonate use, compared to use of less than 6 months. Meta-analyses of database-specific estimates confirmed results from pooled analyses. CONCLUSIONS The observed increased risks of cardiac valvulopathy with bisphosphonate use, although statistically significant, were quite small and unlikely to be clinically significant. Further studies are still needed to evaluate whether bisphosphonates increase or decrease the risk of valvulopathy and to investigate possible mechanisms for the association.
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Affiliation(s)
- P M Coloma
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands.
| | - M de Ridder
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
| | - I Bezemer
- PHARMO Institute NV, 3528 AE, Utrecht, The Netherlands
| | - R M C Herings
- PHARMO Institute NV, 3528 AE, Utrecht, The Netherlands
| | - R Gini
- Agenzia Regionale di Sanità della Toscana, 50141, Florence, Italy
| | - S Pecchioli
- Società Italiana di Medicina Generale, 50141, Florence, Italy
| | - L Scotti
- Università di Milano-Bicocca, 20126, Milan, Italy
| | - P Rijnbeek
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
| | - M Mosseveld
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
| | - J van der Lei
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
| | - G Trifirò
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
- Department of Clinical and Experimental Medicine, University of Messina, 98122, Messina, Italy
| | - M Sturkenboom
- Department of Medical Informatics, Erasmus MC University Medical Center Rotterdam, 3000 CA, Rotterdam, The Netherlands
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14
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Pacurariu AC, Coloma PM, van Haren A, Genov G, Sturkenboom MCJM, Straus SMJM. A description of signals during the first 18 months of the EMA pharmacovigilance risk assessment committee. Drug Saf 2015; 37:1059-66. [PMID: 25398646 DOI: 10.1007/s40264-014-0240-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND OBJECTIVE New pharmacovigilance legislation in the European Union has underlined the importance of signal management, giving the European Medicines Agency's newly established Pharmacovigilance Risk Assessment Committee (PRAC) the mandate to oversee all aspects of the use of medicinal products including detection, assessment, minimization, and communication relating to the risk of adverse reactions. In this study, we describe the signals as brought to the PRAC during the first 18 months of its operation and the ensuing regulatory actions. METHODS Data were collected from publicly available sources, for the period July 2012-December 2013, classified according to predefined rules, and described using the appropriate descriptive statistics. Suspected adverse drug reactions were categorized into the Medical Dictionary for Regulatory Affairs and drug names were mapped to the Anatomical Therapeutic Chemical codes. RESULTS During the study period, 125 signals concerning 96 medicinal products were discussed by the PRAC. The majority of signals were triggered by spontaneous reports (62%) and the median drug age (since marketing authorization) for drugs that prompted a signal was 12 years, significantly less compared with drugs that had no signal within the same period (20 years). The mean time until a decision was reached by the PRAC was 75 days (median 30 days, range 0-273) with 43% of all decisions taken during the first meeting. The decisions to start a referral and to send a direct healthcare professional communication took the least amount of time [54 days (median 27 days, range 0-186) and 51 days (median 0 days, range 0-153)]. CONCLUSIONS The importance of spontaneous reporting in signal detection and monitoring of safety issues throughout the entire life cycle of a medicinal product is confirmed in this study. The amount of time a drug has been on the market is correlated with the number of signals detected. The PRAC decision-making process seems efficient particularly with respect to serious concerns; its role in improving signal prioritization and real-time signal management will be further clarified in its subsequent years of operation.
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Affiliation(s)
- Alexandra C Pacurariu
- Department of Medical Informatics, Erasmus University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
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15
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de Bie S, Coloma PM, Ferrajolo C, Verhamme KMC, Trifirò G, Schuemie MJ, Straus SMJM, Gini R, Herings R, Mazzaglia G, Picelli G, Ghirardi A, Pedersen L, Stricker BHC, van der Lei J, Sturkenboom MCJM. The role of electronic healthcare record databases in paediatric drug safety surveillance: a retrospective cohort study. Br J Clin Pharmacol 2015; 80:304-14. [PMID: 25683723 DOI: 10.1111/bcp.12610] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Revised: 02/16/2015] [Accepted: 02/10/2015] [Indexed: 11/25/2022] Open
Abstract
AIM Electronic healthcare record (EHR)-based surveillance systems are increasingly being developed to support early detection of safety signals. It is unknown what the power of such a system is for surveillance among children and adolescents. In this paper we provide estimates of the number and classes of drugs, and incidence rates (IRs) of events, that can be monitored in children and adolescents (0-18 years). METHODS Data were obtained from seven population-based EHR databases in Denmark, Italy, and the Netherlands during the period 1996-2010. We estimated the number of drugs for which specific adverse events can be monitored as a function of actual drug use, minimally detectable relative risk (RR) and IRs for 10 events. RESULTS The population comprised 4 838 146 individuals (25 575 132 person years (PYs)), who were prescribed 2170 drugs (1 610 631 PYs drug-exposure). Half of the total drug-exposure in PYs was covered by only 18 drugs (0.8%). For a relatively frequent event like upper gastrointestinal bleeding there were 39 drugs for which an association with a RR ≥4, if present, could be investigated. The corresponding number of drugs was eight for a rare event like anaphylactic shock. CONCLUSION Drug use in children is rare and shows little variation. The number of drugs with enough exposure to detect rare adverse events in children and adolescents within an EHR-based surveillance system such as EU-ADR is limited. Use of additional sources of paediatric drug exposure information and global collaboration are imperative in order to optimize EHR data for paediatric safety surveillance.
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Affiliation(s)
- Sandra de Bie
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,Dutch Medicines Evaluation Board, Utrecht, the Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Carmen Ferrajolo
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,Campania Regional Center of Pharmacovigilance and Pharmacoepidemiology, Department of Experimental Medicine, Pharmacology Section, Second University of Naples, Naples, Italy
| | - Katia M C Verhamme
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Clinical and Experimental Medicine and Pharmacology, Section of Pharmacology, University of Messina, Messina, Italy
| | - Martijn J Schuemie
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sabine M J M Straus
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,Dutch Medicines Evaluation Board, Utrecht, the Netherlands
| | - Rosa Gini
- Agenzia Regionale di Sanità della Toscana, Florence, Italy
| | - Ron Herings
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,PHARMO Institute, Utrecht, the Netherlands
| | | | - Gino Picelli
- Pedianet-Società Servizi Telematici SRL, Padova, Italy
| | - Arianna Ghirardi
- Department of Statistics, Universita di Milano-Bicocca, Milan, Italy
| | - Lars Pedersen
- Department of Clinical Epidemiology, Aarhus University Hospital Aarhus, Denmark
| | - Bruno H C Stricker
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Inspectorate of Health Care, The Hague, the Netherlands
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Miriam C J M Sturkenboom
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
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Masclee GMC, Coloma PM, Kuipers EJ, Sturkenboom MCJM. Increased risk of microscopic colitis with use of proton pump inhibitors and non-steroidal anti-inflammatory drugs. Am J Gastroenterol 2015; 110:749-59. [PMID: 25916221 DOI: 10.1038/ajg.2015.119] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 03/01/2015] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Microscopic colitis (MC) is characterized by chronic watery diarrhea. Recently, several drugs were reported to increase the risk of MC. However, studies lacked a clear exposure definition, did not address duration relationships, and did not take important biases into account. We estimated the risk of MC during drug use. METHODS This is a population-based nested case-control study using a Dutch primary care database (1999-2013). Incident MC cases (aged ≥18 years) were matched to community-based and colonoscopy-negative controls on age, sex, and primary care practice. Drug use was assessed within 1 and 2 years before the index date. Adjusted odds ratios (OR) were calculated by conditional logistic regression. RESULTS From the source population of 1,458,410 subjects, 218 cases were matched to 15,045 community controls and 475 colonoscopy-negative controls. Current use (≤3 months) of proton pump inhibitors (PPIs), nonsteroidal anti-inflammatory drugs (NSAIDs), selective serotonin reuptake inhibitors, low-dose aspirin, angiotensin-converting enzyme (ACE) inhibitors and beta-blockers significantly increased the risk of MC compared with never use in community controls. Adjusted ORs ranged from 2.5 (95% confidence interval (CI): 1.5-4.2) for ACE inhibitors to 7.3 (95% CI: 4.5-12.1) for PPIs in the year prior to the index date. After accounting for diagnostic delay, only use of NSAIDs, PPIs, low-dose aspirin, and ACE inhibitors increased the risk of MC. Compared with colonoscopy controls, only use of PPIs (OR-adjusted 10.6; 1.8-64.2) and NSAIDs (OR-adjusted 5.6; 1.2-27.0) increased the risk of MC. CONCLUSIONS NSAIDs and PPIs are associated with an increased risk of MC. The association of MC with use of the other drugs is probably explained by worsening of diarrhea/symptoms rather than increasing the risk of MC itself.
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Affiliation(s)
- Gwen M C Masclee
- 1] Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands [2] Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ernst J Kuipers
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Miriam C J M Sturkenboom
- 1] Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Masclee GMC, Coloma PM, Spaander MCW, Kuipers EJ, Sturkenboom MCJM. NSAIDs, statins, low-dose aspirin and PPIs, and the risk of oesophageal adenocarcinoma among patients with Barrett's oesophagus: a population-based case-control study. BMJ Open 2015; 5:e006640. [PMID: 25633286 PMCID: PMC4316430 DOI: 10.1136/bmjopen-2014-006640] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Non-steroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPIs), low-dose aspirin and statins may decrease the risk of oesophageal adenocarcinoma (OAC) among patients with Barrett's oesophagus (BO). However, previous studies did not adequately address bias and confounding. Our objective was to estimate the risk of OAC among patients with BO exposed to NSAIDs, statins and PPIs. DESIGN Case-control study nested within a BO cohort. SETTING Two primary care databases (the UK and the Netherlands (NL)). PARTICIPANTS Cases were adults ≥18 years of age with OAC or high-grade dysplasia (HGD) diagnosis ≥1 year after BO diagnosis. Controls were matched on age, sex, year of BO diagnosis and database. EXPOSURE Drug use was assessed from BO diagnosis until matching date. OUTCOME MEASURE Adjusted ORs with 95% CI were calculated by conditional logistic regression. RESULTS Within the BO cohort (n=15 134), 45 OAC (UK: 40, NL: 5) and 12 HGD cases (NL: 12) were identified. ORa for OAC during NSAID use was 1.2 (95% CI 0.6 to 2.5) and during statin use for >3 years 0.5 (95% CI 0.1 to 1.7). When including HGD cases (n=57), ORa for NSAID use was 0.9 (95% CI 0.5 to 1.8) and for statin use >3 years 0.5 (95% CI 0.1 to 1.7). Higher doses of statins showed lower estimates for OAC and HGD, though not statistically significant. Low-dose aspirin and PPIs did not significantly decrease the risk of OAC and HGD. CONCLUSIONS In this population-based nested case-control study, use of NSAIDs, PPIs, low-dose aspirin or statins did not reduce the risk of HGD and OAC among patients with BO. These findings indicate that for an unselected group of patients with BO chemoprevention by use of drugs to reduce progression to HGD and OAC should not be directly considered as routine care.
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Affiliation(s)
- Gwen M C Masclee
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ernst J Kuipers
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Miriam C J M Sturkenboom
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Masclee GMC, Valkhoff VE, Coloma PM, de Ridder M, Romio S, Schuemie MJ, Herings R, Gini R, Mazzaglia G, Picelli G, Scotti L, Pedersen L, Kuipers EJ, van der Lei J, Sturkenboom MCJM. Risk of upper gastrointestinal bleeding from different drug combinations. Gastroenterology 2014; 147:784-792.e9; quiz e13-4. [PMID: 24937265 DOI: 10.1053/j.gastro.2014.06.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 05/22/2014] [Accepted: 06/11/2014] [Indexed: 01/20/2023]
Abstract
BACKGROUND & AIMS Concomitant use of nonsteroidal anti-inflammatory drugs (NSAIDs) and low-dose aspirin increases the risk of upper gastrointestinal bleeding (UGIB). Guidelines suggest avoiding certain drug combinations, yet little is known about the magnitude of their interactions. We estimated the risk of UGIB during concomitant use of nonselective (ns)NSAIDs, cyclooxygenase -2 selective inhibitors (COX-2 inhibitors), and low-dose aspirin with other drugs. METHODS We performed a case series analysis of data from 114,835 patients with UGIB (930,888 person-years of follow-up) identified from 7 population-based health care databases (approximately 20 million subjects). Each patient served as his or her own control. Drug exposure was determined based on prescriptions of nsNSAIDs, COX-2 inhibitors, or low-dose aspirin, alone and in combination with other drugs that affect the risk of UGIB. We measured relative risk (incidence rate ratio [IRR] during drug exposure vs nonexposure) and excess risk due to concomitant drug exposure (relative excess risk due to interaction [RERI]). RESULTS Monotherapy with nsNSAIDs increased the risk of diagnosis of UGIB (IRR, 4.3) to a greater extent than monotherapy with COX-2 inhibitors (IRR, 2.9) or low-dose aspirin (IRR, 3.1). Combination therapy generally increased the risk of UGIB; concomitant nsNSAID and corticosteroid therapies increased the IRR to the greatest extent (12.8) and also produced the greatest excess risk (RERI, 5.5). Concomitant use of nsNSAIDs and aldosterone antagonists produced an IRR for UGIB of 11.0 (RERI, 4.5). Excess risk from concomitant use of nsNSAIDs with selective serotonin reuptake inhibitors (SSRIs) was 1.6, whereas that from use of COX-2 inhibitors with SSRIs was 1.9 and that for use of low-dose aspirin with SSRIs was 0.5. Excess risk of concomitant use of nsNSAIDs with anticoagulants was 2.4, of COX-2 inhibitors with anticoagulants was 0.1, and of low-dose aspirin with anticoagulants was 1.9. CONCLUSIONS Based on a case series analysis, concomitant use of nsNSAIDs, COX-2 inhibitors, or low-dose aspirin with SSRIs significantly increases the risk of UGIB. Concomitant use of nsNSAIDs or low-dose aspirin, but not COX-2 inhibitors, with corticosteroids, aldosterone antagonists, or anticoagulants produces significant excess risk of UGIB.
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Affiliation(s)
- Gwen M C Masclee
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Vera E Valkhoff
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maria de Ridder
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Silvana Romio
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Martijn J Schuemie
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ron Herings
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; PHARMO Institute, Utrecht, The Netherlands
| | - Rosa Gini
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; Agenzi Regionali di Sanità della Toscana, Florence, Italy
| | | | - Gino Picelli
- Pedianet, Societá Servizi Telematici SRL, Padova, Italy
| | | | - Lars Pedersen
- Clinical Epidemiology, Aarhus University Hospital, Århus Sygehus, Aarhus, Denmark
| | - Ernst J Kuipers
- Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Miriam C J M Sturkenboom
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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Masclee GMC, Coloma PM, de Wilde M, Kuipers EJ, Sturkenboom MCJM. Letter: incidence rates of Barrett's oesophagus and oesophageal adenocarcinoma in UK and the Netherlands - authors' reply. Aliment Pharmacol Ther 2014; 40:404. [PMID: 25040926 DOI: 10.1111/apt.12856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 06/10/2014] [Indexed: 01/10/2023]
Affiliation(s)
- G M C Masclee
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Trifirò G, Coloma PM, Rijnbeek PR, Romio S, Mosseveld B, Weibel D, Bonhoeffer J, Schuemie M, van der Lei J, Sturkenboom M. Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: why and how? J Intern Med 2014; 275:551-61. [PMID: 24635221 DOI: 10.1111/joim.12159] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A growing number of international initiatives (e.g. EU-ADR, Sentinel, OMOP, PROTECT and VAESCO) are based on the combined use of multiple healthcare databases for the conduct of active surveillance studies in the area of drug and vaccine safety. The motivation behind combining multiple healthcare databases is the earlier detection and validation, and hence earlier management, of potential safety issues. Overall, the combination of multiple healthcare databases increases statistical sample size and heterogeneity of exposure for postmarketing drug and vaccine safety surveillance, despite posing several technical challenges. Healthcare databases generally differ by underlying healthcare systems, type of information collected, drug/vaccine and medical event coding systems and language. Therefore, harmonization of medical data extraction through homogeneous coding algorithms across highly different databases is necessary. Although no standard procedure is currently available to achieve this, several approaches have been developed in recent projects. Another main challenge involves choosing the work models for data management and analyses whilst respecting country-specific regulations in terms of data privacy and anonymization. Dedicated software (e.g. Jerboa) has been produced to deal with privacy issues by sharing only anonymized and aggregated data using a common data model. Finally, storage and safe access to the data from different databases requires the development of a proper remote research environment. The aim of this review is to provide a summary of the potential, disadvantages, methodological issues and possible solutions concerning the conduct of postmarketing multidatabase drug and vaccine safety studies, as demonstrated by several international initiatives.
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Affiliation(s)
- G Trifirò
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
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Masclee GMC, Coloma PM, de Wilde M, Kuipers EJ, Sturkenboom MCJM. The incidence of Barrett's oesophagus and oesophageal adenocarcinoma in the United Kingdom and The Netherlands is levelling off. Aliment Pharmacol Ther 2014; 39:1321-30. [PMID: 24738722 DOI: 10.1111/apt.12759] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Revised: 03/20/2014] [Accepted: 03/27/2014] [Indexed: 01/02/2023]
Abstract
BACKGROUND Barrett's oesophagus (BO) is a risk factor for oesophageal adenocarcinoma (OAC). Several studies report increasing incidences of BO with substantial variation. AIM To determine age- and sex-stratified incidence rates (IR) of BO and OAC. METHODS Cohort study using two primary care databases in the United Kingdom (UK) and the Netherlands (NL) (2000-2012). BO and OAC cases were identified using disease-specific READ codes (UK) and free-text search with manual validation (NL). Age- and sex-specific incidence rates (IRs) were calculated for both BO and OAC. RESULTS From the study population of 6,885,420 subjects in the UK, we identified 12,312 incident BO and 40 (0.3%) subsequent incident OAC cases. There were 1383 incident BO, and subsequent 5 (0.4%) incident OAC cases among the 1,487,191 subjects in the NL. The IR of BO increased linearly with age: 15.6/100,000 PYs (UK) and 23.7/100,000 PYs (NL) for patients aged 40-44 years, increasing to 85.6/100,000 PYs (UK) and 87.0/100,000 PYs (NL) for 70-74 years. In both the UK and the NL, IR of BO was 2-4 times higher in males than females across all age groups. With respect to calendar time, the IR of BO increased by 35% (UK) and 41% (NL) from 2000 to 2003, after which IRs remained stable until 2012. CONCLUSIONS The incidence rates of BO in the UK and the NL increased until 2003, but levelled off thereafter. Around 0.3% of patients with BO developed OAC at least 1 year after BO diagnosis. These findings may help tailor endoscopic surveillance strategies among patients with BO.
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Affiliation(s)
- G M C Masclee
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Valkhoff VE, Coloma PM, Masclee GMC, Gini R, Innocenti F, Lapi F, Molokhia M, Mosseveld M, Nielsson MS, Schuemie M, Thiessard F, van der Lei J, Sturkenboom MCJM, Trifirò G. Validation study in four health-care databases: upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk. J Clin Epidemiol 2014; 67:921-31. [PMID: 24794575 DOI: 10.1016/j.jclinepi.2014.02.020] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 02/11/2014] [Accepted: 02/21/2014] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To evaluate the accuracy of disease codes and free text in identifying upper gastrointestinal bleeding (UGIB) from electronic health-care records (EHRs). STUDY DESIGN AND SETTING We conducted a validation study in four European electronic health-care record (EHR) databases such as Integrated Primary Care Information (IPCI), Health Search/CSD Patient Database (HSD), ARS, and Aarhus, in which we identified UGIB cases using free text or disease codes: (1) International Classification of Disease (ICD)-9 (HSD, ARS); (2) ICD-10 (Aarhus); and (3) International Classification of Primary Care (ICPC) (IPCI). From each database, we randomly selected and manually reviewed 200 cases to calculate positive predictive values (PPVs). We employed different case definitions to assess the effect of outcome misclassification on estimation of risk of drug-related UGIB. RESULTS PPV was 22% [95% confidence interval (CI): 16, 28] and 21% (95% CI: 16, 28) in IPCI for free text and ICPC codes, respectively. PPV was 91% (95% CI: 86, 95) for ICD-9 codes and 47% (95% CI: 35, 59) for free text in HSD. PPV for ICD-9 codes in ARS was 72% (95% CI: 65, 78) and 77% (95% CI: 69, 83) for ICD-10 codes (Aarhus). More specific definitions did not have significant impact on risk estimation of drug-related UGIB, except for wider CIs. CONCLUSIONS ICD-9-CM and ICD-10 disease codes have good PPV in identifying UGIB from EHR; less granular terminology (ICPC) may require additional strategies. Use of more specific UGIB definitions affects precision, but not magnitude, of risk estimates.
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Affiliation(s)
- Vera E Valkhoff
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam The Netherlands
| | - Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Gwen M C Masclee
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam The Netherlands
| | - Rosa Gini
- Agenzi Regionali di Sanità della Toscana, Via Pietro Dazzi 1 - 50141, Firenze, Italy
| | - Francesco Innocenti
- Health Search, Italian College of General Practitioners, Via del Pignoncino, 9-11,50142, Florence, Italy
| | - Francesco Lapi
- Centre for Clinical Epidemiology, Jewish General Hospital, 3755 Côte-Sainte-Catherine Road, Montréal, QC H3T 1E2, Canada; Department of Preclinical and Clinical Pharmacology, University of Florence, Piazza di San Marco, 4, 50121 Firenze, Italy
| | - Mariam Molokhia
- Department of Primary Care and Public Health Sciences, Kings College London, Division of Health and Social Care Research, 7th Floor, Capital House, 42 Weston Street, London SE1 3QD, United Kingdom
| | - Mees Mosseveld
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Malene Schou Nielsson
- Department of Clinical Epidemiology, Aarhus University Hospital, Olof Palmes Allé 43-45 DK-8200 Aarhus N, Denmark
| | - Martijn Schuemie
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Frantz Thiessard
- LESIM, ISPED, Universite Bordeaux 2', 146 Rue Léo Saignat, 33076 Bordeaux, France
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Miriam C J M Sturkenboom
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands; Department of Clinical and Experimental Medicine and Pharmacology, Via Consolare Pompea, 1, Messina, University of Messina, Italy.
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Coloma PM, Trifirò G, Patadia V, Sturkenboom M. Postmarketing safety surveillance : where does signal detection using electronic healthcare records fit into the big picture? Drug Saf 2013; 36:183-97. [PMID: 23377696 DOI: 10.1007/s40264-013-0018-x] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The safety profile of a drug evolves over its lifetime on the market; there are bound to be changes in the circumstances of a drug's clinical use which may give rise to previously unobserved adverse effects, hence necessitating surveillance postmarketing. Postmarketing surveillance has traditionally been carried out by systematic manual review of spontaneous reports of adverse drug reactions. Vast improvements in computing capabilities have provided opportunities to automate signal detection, and several worldwide initiatives are exploring new approaches to facilitate earlier detection, primarily through mining of routinely-collected data from electronic healthcare records (EHR). This paper provides an overview of ongoing initiatives exploring data from EHR for signal detection vis-à-vis established spontaneous reporting systems (SRS). We describe the role SRS has played in regulatory decision making with respect to safety issues, and evaluate the potential added value of EHR-based signal detection systems to the current practice of drug surveillance. Safety signal detection is both an iterative and dynamic process. It is in the best interest of public health to integrate and understand evidence from all possibly relevant information sources on drug safety. Proper evaluation and communication of potential signals identified remains an imperative and should accompany any signal detection activity.
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Affiliation(s)
- Preciosa M Coloma
- Ee-2116, Department of Medical Informatics, Erasmus Medical Centre, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
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Coloma PM, Valkhoff VE, Mazzaglia G, Nielsson MS, Pedersen L, Molokhia M, Mosseveld M, Morabito P, Schuemie MJ, van der Lei J, Sturkenboom M, Trifirò G. Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries. BMJ Open 2013; 3:bmjopen-2013-002862. [PMID: 23794587 PMCID: PMC3686251 DOI: 10.1136/bmjopen-2013-002862] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE To evaluate positive predictive value (PPV) of different disease codes and free text in identifying acute myocardial infarction (AMI) from electronic healthcare records (EHRs). DESIGN Validation study of cases of AMI identified from general practitioner records and hospital discharge diagnoses using free text and codes from the International Classification of Primary Care (ICPC), International Classification of Diseases 9th revision-clinical modification (ICD9-CM) and ICD-10th revision (ICD-10). SETTING Population-based databases comprising routinely collected data from primary care in Italy and the Netherlands and from secondary care in Denmark from 1996 to 2009. PARTICIPANTS A total of 4 034 232 individuals with 22 428 883 person-years of follow-up contributed to the data, from which 42 774 potential AMI cases were identified. A random sample of 800 cases was subsequently obtained for validation. MAIN OUTCOME MEASURES PPVs were calculated overall and for each code/free text. 'Best-case scenario' and 'worst-case scenario' PPVs were calculated, the latter taking into account non-retrievable/non-assessable cases. We further assessed the effects of AMI misclassification on estimates of risk during drug exposure. RESULTS Records of 748 cases (93.5% of sample) were retrieved. ICD-10 codes had a 'best-case scenario' PPV of 100% while ICD9-CM codes had a PPV of 96.6% (95% CI 93.2% to 99.9%). ICPC codes had a 'best-case scenario' PPV of 75% (95% CI 67.4% to 82.6%) and free text had PPV ranging from 20% to 60%. Corresponding PPVs in the 'worst-case scenario' all decreased. Use of codes with lower PPV generally resulted in small changes in AMI risk during drug exposure, but codes with higher PPV resulted in attenuation of risk for positive associations. CONCLUSIONS ICD9-CM and ICD-10 codes have good PPV in identifying AMI from EHRs; strategies are necessary to further optimise utility of ICPC codes and free-text search. Use of specific AMI disease codes in estimation of risk during drug exposure may lead to small but significant changes and at the expense of decreased precision.
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Affiliation(s)
- Preciosa M Coloma
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Vera E Valkhoff
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Giampiero Mazzaglia
- Department of Research, Health Search, Italian College of General Practitioners, Florence, Italy
| | | | - Lars Pedersen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Mariam Molokhia
- Primary Care and Population Sciences, Kings College, London, UK
| | - Mees Mosseveld
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Paolo Morabito
- Department of Clinical and Experimental Medicine and Pharmacology, University of Messina, Messina, Italy
| | - Martijn J Schuemie
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Miriam Sturkenboom
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Gianluca Trifirò
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Clinical and Experimental Medicine and Pharmacology, University of Messina, Messina, Italy
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Coloma PM, Avillach P, Salvo F, Schuemie MJ, Ferrajolo C, Pariente A, Fourrier-Réglat A, Molokhia M, Patadia V, van der Lei J, Sturkenboom M, Trifirò G. A reference standard for evaluation of methods for drug safety signal detection using electronic healthcare record databases. Drug Saf 2013; 36:13-23. [PMID: 23315292 DOI: 10.1007/s40264-012-0002-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND The growing interest in using electronic healthcare record (EHR) databases for drug safety surveillance has spurred development of new methodologies for signal detection. Although several drugs have been withdrawn postmarketing by regulatory authorities after scientific evaluation of harms and benefits, there is no definitive list of confirmed signals (i.e. list of all known adverse reactions and which drugs can cause them). As there is no true gold standard, prospective evaluation of signal detection methods remains a challenge. OBJECTIVE Within the context of methods development and evaluation in the EU-ADR Project (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge), we propose a surrogate reference standard of drug-adverse event associations based on existing scientific literature and expert opinion. METHODS The reference standard was constructed for ten top-ranked events judged as important in pharmacovigilance. A stepwise approach was employed to identify which, among a list of drug-event associations, are well recognized (known positive associations) or highly unlikely ('negative controls') based on MEDLINE-indexed publications, drug product labels, spontaneous reports made to the WHO's pharmacovigilance database, and expert opinion. Only drugs with adequate exposure in the EU-ADR database network (comprising ≈60 million person-years of healthcare data) to allow detection of an association were considered. Manual verification of positive associations and negative controls was independently performed by two experts proficient in clinical medicine, pharmacoepidemiology and pharmacovigilance. A third expert adjudicated equivocal cases and arbitrated any disagreement between evaluators. RESULTS Overall, 94 drug-event associations comprised the reference standard, which included 44 positive associations and 50 negative controls for the ten events of interest: bullous eruptions; acute renal failure; anaphylactic shock; acute myocardial infarction; rhabdomyolysis; aplastic anaemia/pancytopenia; neutropenia/agranulocytosis; cardiac valve fibrosis; acute liver injury; and upper gastrointestinal bleeding. For cardiac valve fibrosis, there was no drug with adequate exposure in the database network that satisfied the criteria for a positive association. CONCLUSION A strategy for the construction of a reference standard to evaluate signal detection methods that use EHR has been proposed. The resulting reference standard is by no means definitive, however, and should be seen as dynamic. As knowledge on drug safety evolves over time and new issues in drug safety arise, this reference standard can be re-evaluated.
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Affiliation(s)
- Preciosa M Coloma
- Department of Medical Informatics, Erasmus Medical Center, Postbus 2040, 3000, CA, Rotterdam, The Netherlands.
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Avillach P, Coloma PM, Gini R, Schuemie M, Mougin F, Dufour JC, Mazzaglia G, Giaquinto C, Fornari C, Herings R, Molokhia M, Pedersen L, Fourrier-Réglat A, Fieschi M, Sturkenboom M, van der Lei J, Pariente A, Trifirò G. Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project. J Am Med Inform Assoc 2012; 20:184-92. [PMID: 22955495 DOI: 10.1136/amiajnl-2012-000933] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE Data from electronic healthcare records (EHR) can be used to monitor drug safety, but in order to compare and pool data from different EHR databases, the extraction of potential adverse events must be harmonized. In this paper, we describe the procedure used for harmonizing the extraction from eight European EHR databases of five events of interest deemed to be important in pharmacovigilance: acute myocardial infarction (AMI); acute renal failure (ARF); anaphylactic shock (AS); bullous eruption (BE); and rhabdomyolysis (RHABD). DESIGN The participating databases comprise general practitioners' medical records and claims for hospitalization and other healthcare services. Clinical information is collected using four different disease terminologies and free text in two different languages. The Unified Medical Language System was used to identify concepts and corresponding codes in each terminology. A common database model was used to share and pool data and verify the semantic basis of the event extraction queries. Feedback from the database holders was obtained at various stages to refine the extraction queries. MEASUREMENTS Standardized and age specific incidence rates (IRs) were calculated to facilitate benchmarking and harmonization of event data extraction across the databases. This was an iterative process. RESULTS The study population comprised overall 19 647 445 individuals with a follow-up of 59 929 690 person-years (PYs). Age adjusted IRs for the five events of interest across the databases were as follows: (1) AMI: 60-148/100 000 PYs; (2) ARF: 3-49/100 000 PYs; (3) AS: 2-12/100 000 PYs; (4) BE: 2-17/100 000 PYs; and (5) RHABD: 0.1-8/100 000 PYs. CONCLUSIONS The iterative harmonization process enabled a more homogeneous identification of events across differently structured databases using different coding based algorithms. This workflow can facilitate transparent and reproducible event extractions and understanding of differences between databases.
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Affiliation(s)
- Paul Avillach
- LESIM, ISPED, University Bordeaux Segalen, Bordeaux, France.
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Coloma PM, Trifirò G, Schuemie MJ, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Picelli G, Corrao G, Pedersen L, van der Lei J, Sturkenboom M. Electronic healthcare databases for active drug safety surveillance: is there enough leverage? Pharmacoepidemiol Drug Saf 2012; 21:611-21. [PMID: 22315152 DOI: 10.1002/pds.3197] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2011] [Revised: 11/22/2011] [Accepted: 11/28/2011] [Indexed: 11/07/2022]
Abstract
PURPOSE To provide estimates of the number and types of drugs that can be monitored for safety surveillance using electronic healthcare databases. METHODS Using data from eight European databases (administrative claims, medical records) and in the context of a cohort study, we determined the amount of drug exposure required for signal detection across varying magnitudes of relative risk (RR). We provide estimates of the number and types of drugs that can be monitored as a function of actual use, minimal detectable RR, and empirically derived incidence rates for the following adverse events: (i) acute myocardial infarction; (ii) acute renal failure; (iii) anaphylactic shock; (iv) bullous eruptions; (v) rhabdomyolysis; and (vi) upper gastrointestinal bleeding. We performed data simulation to see how expansion of database size would influence the capabilities of such system. RESULTS Data from 1,947,452 individuals (59,594,132 person-years follow-up) who used 2,289 drugs in the EU-ADR network show that for a frequent event such as acute myocardial infarction, there are 531 drugs (23% of total) for which an association with RR = 2, if present, can be investigated. For a rare event such as rhabdomyolysis, there are 19 drugs (1%) for which an association of same magnitude can be investigated. CONCLUSION Active surveillance using healthcare data-based networks for signal detection is feasible, although the leverage to do so may be low for infrequently used drugs and for rare outcomes. Extending database network size to include data from heterogeneous populations and increasing follow-up time are warranted to maximize leverage of these surveillance systems.
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Affiliation(s)
- Preciosa M Coloma
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands.
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Trifirò G, Patadia V, Schuemie MJ, Coloma PM, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Giaquinto C, Scotti L, Pedersen L, Avillach P, Sturkenboom MCJM, van der Lei J. EU-ADR healthcare database network vs. spontaneous reporting system database: preliminary comparison of signal detection. Stud Health Technol Inform 2011; 166:25-30. [PMID: 21685607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug-induced. The methodology for signal detection in EU-ADR is still under development and testing phase.
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Affiliation(s)
- Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Coloma PM, Schuemie MJ, Trifirò G, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Giaquinto C, Corrao G, Pedersen L, van der Lei J, Sturkenboom M. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf 2010; 20:1-11. [PMID: 21182150 DOI: 10.1002/pds.2053] [Citation(s) in RCA: 176] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2010] [Revised: 07/03/2010] [Accepted: 08/18/2010] [Indexed: 01/06/2023]
Abstract
PURPOSE In this proof-of-concept paper we describe the framework, process, and preliminary results of combining data from European electronic healthcare record (EHR) databases for large-scale monitoring of drug safety. METHODS Aggregated demographic, clinical, and prescription data from eight databases in four countries (Denmark, Italy, Netherlands, the UK) were pooled using a distributed network approach by generation of common input data followed by local aggregation through custom-built software, Jerboa(©). Comparison of incidence rates of upper gastrointestinal bleeding (UGIB) and nonsteroidal anti-inflammatory drug (NSAID) utilization patterns were used to evaluate data harmonization and quality across databases. The known association of NSAIDs and UGIB was employed to demonstrate sensitivity of the system by comparing incidence rate ratios (IRRs) of UGIB during NSAID use to UGIB during all other person-time. RESULTS The study population for this analysis comprised 19,647,445 individuals corresponding to 59,929,690 person-years of follow-up. 39,967 incident cases of UGIB were identified during the study period. Crude incidence rates varied between 38.8 and 109.5/100,000 person-years, depending on country and type of database, while age-standardized rates ranged from 25.1 to 65.4/100,000 person-years. NSAID use patterns were similar for databases within the same country but heterogeneous among different countries. A statistically significant age- and gender-adjusted association between use of any NSAID and increased risk for UGIB was confirmed in all databases, IRR from 2.0 (95%CI:1.7-2.2) to 4.3 (95%CI: 4.1-4.5). CONCLUSIONS Combining data from EHR databases of different countries to identify drug-adverse event associations is feasible and can set the stage for changing and enlarging the scale for drug safety monitoring.
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Affiliation(s)
- Preciosa M Coloma
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, Catania MA, Salvo F, David A, Moore N, Caputi AP, Sturkenboom M, Molokhia M, Hippisley-Cox J, Acedo CD, van der Lei J, Fourrier-Reglat A. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf 2010; 18:1176-84. [PMID: 19757412 DOI: 10.1002/pds.1836] [Citation(s) in RCA: 136] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE Data mining on electronic health records (EHRs) has emerged as a promising complementary method for post-marketing drug safety surveillance. The EU-ADR project, funded by the European Commission, is developing techniques that allow mining of EHRs for adverse drug events across different countries in Europe. Since mining on all possible events was considered to unduly increase the number of spurious signals, we wanted to create a ranked list of high-priority events. METHODS Scientific literature, medical textbooks, and websites of regulatory agencies were reviewed to create a preliminary list of events that are deemed important in pharmacovigilance. Two teams of pharmacovigilance experts independently rated each event on five criteria: 'trigger for drug withdrawal', 'trigger for black box warning', 'leading to emergency department visit or hospital admission', 'probability of event to be drug-related', and 'likelihood of death'. In case of disagreement, a consensus score was obtained. Ordinal scales between 0 and 3 were used for rating the criteria, and an overall score was computed to rank the events. RESULTS An initial list comprising 23 adverse events was identified. After rating all the events and calculation of overall scores, a ranked list was established. The top-ranking events were: cutaneous bullous eruptions, acute renal failure, anaphylactic shock, acute myocardial infarction, and rhabdomyolysis. CONCLUSIONS A ranked list of 23 adverse drug events judged as important in pharmacovigilance was created to permit focused data mining. The list will need to be updated periodically as knowledge on drug safety evolves and new issues in drug safety arise.
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Affiliation(s)
- Gianluca Trifirò
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
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