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OpenVigil - open tools for data-mining and analysis of pharmacovigilance data

Quick access:
Search drugs and adverse events with OpenVigil 2! Search drugs and adverse events with OpenVigil FDA! Calculate 2x2 contingency tables!
Documentation:
Welcome About Pharmacovigilance How to use OpenVigil
Technical docs and other resources Further Literature News & project roadmap
What is OpenVigil?

OpenVigil 1 and 2 are software packages to analyse pharmacovigilance (adverse drug event) data. There are several national and international databases of so called spontaneous adverse event reports, e.g., the U.S. american FDA Adverse Event Reporting System (AERS, mostly domestic data) or the WHO Uppsala Monitoring Centre (international). Currently, analyses of FDA AERS (LAERS & FAERS) pharmacovigilance data are available. In addition to U.S. american data, we have also imported German pharmcovigilance data. Data mining features include highly configurable search criteria filters and output filters. Analyses include disproportionality analyses for signal detection like Proportional Reporting Ratio (PRR) calculations. Results can be viewed, sorted and filtered in the webbrowser or saved for further analyses in statistical software packages. Both projects aim at integrating these and other pharmacolovigilance sources to pharmacoepidemiological data like prescription data. OpenVigil 2 is designed for complete case analyses.
OpenVigilFDA is a front-end to the openFDA-interface which is being developed by the FDA since 2014. It allows extraction of the latest reports. Due to technical limitations, the beta-version status and the ongoing changes to the API of openFDA, OpenVigil 2 is more stable and superior for analyses of disproportionality. OpenVigilFDA provides available case analysis, e.g., some records are not complete but still considered.

Where can I access OpenVigil?

US americal pharmacovigilance data analysis options There are live installations with U.S. american FDA pharmacovigilance data of both versions of OpenVigil with FDA AERS data and OpenVigilFDA freely available

IMPORTANT INFORMATION FOR ACCESSING OPENVIGIL 2! OpenVigil 2.1-MedDRA-v24 (data 2004Q1-2024Q4): http://ov2.h2876314.stratoserver.net/OV2


OpenVigil FDA: http://ovfda.h2876314.stratoserver.net

Where can I download OpenVigil?

You can download the PHP-sources/executables of OpenVigil 1, OpenVigilFDA and OpenVigil 2.1-MedDRA at sourceforge: http://sourceforge.net/projects/openvigil/

Who can be contacted about this project?

The project is maintained by Dr. Ruwen Böhm, specialist for clinical pharmacology, Department of Radiotherapy, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, and SocraTec R&D GmbH, Erfurt, and Prof. Dr. Hans-Joachim Klein, computer scientist, independend researcher, Kiel. We can be reached at <openvigil@pharmakologie.uni-kiel.de>

Legalese

The OpenVigil project follows the HONcode (Health On The Net) and was certified in november 2015. All software uses browser cookies. Cookies are used for the captchas and to store previous queries as convenience for the user (OpenVigilFDA only). Users are not tracked. Emails sent to the projects members are treated confidentially and are neither systematically saved nor used for statistics. Access of all webpages/programs is logged, including your IP address. You can contact us if you wish to see or delete this data.
Cf. the installation overview pages for date of last changes to programs or databases and version numbers and the cave-at documents for general pitfalls.
All software uses brand names which are not specifically identified (e.g., by using ®). Cf. the documentation to understand the difference between drug name and brand name and to learn which output does contain brand names. The authors declare no conflicts of interested as they have no financial or other relation to any of the producers.
Responsible for this website (Impressum / Betreiber der Website): Dr. med. Ruwen Böhm, Institut für Experimentelle und Klinisch Pharmakologie, UKSH Kiel, Hospitalstr. 4, 24105 Kiel, Germany. Tel. +49 431 500 30414, <ruwen.boehm@pharmakologie.uni-kiel.de>.
The project is funded by public funding via the Christian Albrechts University (CAU) of Kiel, Germany. There is no funding via advertisements.
The OpenVigil project does not produce or gather any of the pharmacovigilance data itself but is dependent on external data sources.
Our software is being developed for physicians, pharmacists and scientists. Due to the origin and nature of the data and the ongoing work on our programs, all results should be considered unvalidated. Especially, any findings must not be used uncritically for therapy changes or legal proceedings. However, these data are well usable for hypothesis generation.
This page was last changed on 2025-03-20.

Pharmacovigilance reporting flow What is pharmacovigilance?

Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem.

Why do we have pharmacovigilance?

Triggered by the thalidomide (Contergan®) tragedy 1957-1961, various countries have introduced the systematic collection of spontaneous filed reports of adverse events occuring during or after pharmacotherapy. This ongoing monitoring of (newly approved) drugs ensures detection of rarely occuring adverse event and other types of issues with the pharmaceutical product or the patient adhearance to it. So, while clinical trials can contribute to drug safety, pharmacovigilance can improve drug therapy safety!

What type of data is gathered?

Reports can be filed by physicians, pharmacists, pharmaceutical companies and patients. Depending on the domestic laws, it is mandatory for most of these parties to report any observed adverse event. Recent EU directives recommend to gather reports from patients. The quality of the data is thus diverse: Some are unusable due to missing data. One the other hand, reports made by pharmaceutical contain a lot of information due to enforced laws concerning patient security. Most pharmacovigilance databases traditionally contain some basic data on the patient (e.g., gender and age), the adverse event(s) and a list of drugs. Depending on primary data sources (e.g., outpatient or hospital patient) and policy of the agency that is taking care of the database, other data, e.g., indications or laboratory values, can be added.
By the nature of this 'spontaneous collection' these data have to be treated with caution and are generally not suited for hypothesis confirmation but only for hypothesis generation.

How does analysis of pharmacovigilance data contribute to healt care?

Pharmacovigilance data-mining for signals of disproportionate reporting (SDR), i.e., disproportionally stronger associations between drugs and adverse events, is routinely done by the regulatory authorities. However, pharmacovigilance data is not only useful for monitoring new drugs but also for detecting more complex signals, e.g., drug-drug interactions or syndromes or to further analyse known signals and find a especially vulnerable population or mode of application (so called multi-item data mining). Data should be enriched with ontologies for these analyses (e.g., MedDRA, RxNorm, SNOMED, ATC, ICD-10/11).

Where can I extract or analyse pharmacovigilance data?

Open access to pharmacovigilance data is limited. The freedom of information act (US) and similar laws in other jurisdictions have led to the availability of raw data (e.g., FDA AERS datafiles) and new portals to access data (e.g., EMA http://www.adrreports.eu/). A list of possible access and analysis options is provied in our resource library page.
However, the open availability combined with the advanced and cleaning, filtering, extraction and analysis capabilities of OpenVigil 2 are unique: All pharmacovigilance research using OpenVigil software is completely transparent and reproducible, thus allowing other scientists to confirm any findings and expand the analyses.

How are statistical signals in pharmacovigilance data detected?

Statistical detection of signals whether a drug-event combination is a putative dverse drug reactions or just a random association can be done using either (i) frequency based methods comparing estimated counts to observed counts for a drug-event-combination like Relative Reporting Ratio (RRR), Proportional Reporting Ratio (PRR) or Reporting Odds Ratio (ROR) or Likelihood Ratio Test (LRT), (ii) Bayesian probabilities like Bayesian conidence propagation neural network (BCPNN) or Poisson-Dirichlet process (DP) or (iii) the (Multi-item) Gamma Poisson Shrinker (GPS/MGPS).
All OpenVigil software provides RRR, PRR and ROR which are similar in magnitude and explanatory power. These measurements of disproportionality are calculated as RRR=DE*N/(D*E), PRR=(DE/D)/(dE/d) and ROR=DE*de/(De*dE). A value of 1 is considered normal background noise. The confidence interval can be estimated using Chi-squared with Yates' correction chisq > 4 or lower bound of the 95% confidence interval (CI) of RRR, PRR or ROR, e.g. for s = sqr( 1/DE + 1/De + 1/dE + 1/de ) for ROR with CI = e ^ ( ln ROR ± 1,96s ) OpenVigil 2.2 will offer MGPS calculations. This signal detection algorithm is especially suited for small numbers of drug-event combinations (DE). Signal detection can be used to find a subgroup of vulnerable patients. By stratifying the reports by age, gender, mode of administration, dosage, indication or other categories, it is possible to identify any confounders and/or vulnerable patients.

Pharmacovigilance Open World Problem What are the usual pitfalls when analysing pharmacovigilance data?

By the very nature of this data collection, it represents only a certain part of the general population in health care (the so-called "open world" problem). Issues like under/over-reporting and counting issues due to multiplicates are summarized in the OpenVigil 1 & 2 cave-at document.
The quality of reports and the verbatim text items, e.g., DRUG.DRUGNAME in the FDA AERS data, require preprocessing of the records and a careful validation of any analysis results. OpenVigil 2 provides cleaning of imported data by using external databases like drugbank.ca and user input.
An analysis of pharmacovigilance data can usually not confirm a hypothesis. E.g., you cannot use it proof a certain association. In some situations it might support a hypothesis. Instead, pharmacovigilance data is routinely used to generate a new hypothesis that requires testing in more in silico, preclinical or clinical research, as well as to give therapy guidiance in direct health care.

Which clinical or scientific questions can be addressed by analysing pharmacovigilance data?

Obviously, detecting new adverse drug reactions are the primary reason why pharmacovigilance has been implement and thus they are the most important analysis goal. Other usages include detection of especially vulnerable subpopulations, of harmful excipients/brands, of toxic chemical moieties, of syndromes, of drug-drug-interactions, comparing drugs within drug class and for drug repositioning/repurposing.

How about other usage (e.g., marketing or legal proceedings)?

Because of the limitations of pharmacovigilance data analysis due to the origin and nature of the data, any findings of disproportionality do generally not allow to proof an assumption or to suport a hypothesis. Occassionally, it might be usefull to show that a certain signal was present or not present at a certain date in the past for these purposes. Interpretation of queries requires sound knowledge of statistics, pharmacy, pharmacology and clinical significance of any findings. To fully understand the results, a team combining expertise in these areas is recommended.

Technical documents: Installation, Data cleaning, Caveat, Citing

Due to the nature of the method of collecting pharmacovigilance data and the nature of the data itself, several precautions need to be taken for high-quality analyses of drugs and their putative adverse drug reactions. This is especially important if you chose to install OpenVigil yourself.

Other general data analysis tools, pharmacovigilance database sources and similar analysis tools
OpenVigil-DPA-poster Tutorial and standard operating procedures

Common analysis scenarios are depicted here. Please note that our installations of OpenVigil do not use weekly updated data so that monitoring newly approved drugs is usually not readily possible.

  • Tutorials: How to perform certain tasks with OpenVigil
  • Selected analysis scenarios
    • OpenVigil-DrugRepurposing-Talk
  • Our OpenVigil publications
    • Herdegen T, Böhm R: Dtsch Apoth Ztg 2009; 149(4): 315. [Neuro-psychiatric ADR of montelukast] Neuropsychiatrische UAW von Montelukast.
    • Böhm R, Herdegen Th. Dtsch Apoth Ztg 2009, 149(32), S. 3623. [Risk of infection and liver damage by orlistat] Infektionsrisiko und Leberschädigung unter Orlistat
    • Böhm R, Cascorbi I. & Herdegen T. [Hypoglycemic risk of insulinotropic drugs] Hypoglykämie bei insulinotropen Substanzen. Med. Monatsschr. Pharm. 32, 453–458 (2009). PMID 20088347
    • Schulz-Du Bois C & Böhm R. Haloperidol intravenous – a preliminary risk assessment. Pharmacopsychiatry 44, A104 (2011). https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0031-1292545
    • Böhm R, Höcker J, Cascorbi I, Herdegen T. OpenVigil--free eyeballs on AERS pharmacovigilance data. Nat Biotechnol. 2012 Feb 8;30(2):137-8. doi: 10.1038/nbt.2113. http://www.nature.com/nbt/journal/v30/n2/abs/nbt.2113.html
    • Böhm R, Reinecke K, Haen E, Cascorbi I, Herdegen Th. [Clinical pharmacy – Understand, teach and avoid Drug-Drug-Interactions ] KLINISCHE PHARMAZIE - Arzneimittelinteraktionen verstehen, vermitteln und vermeiden. Deutsche Apotheker-Zeitung Vol. 152, No. 36 (2012), p. 64-75
    • Böhm R, Meybohm P. [Pediatric emergencies - part 1: fever] Kindernotfälle - Teil 1: Fieber. Notfallmedizin up2date 2012(7):2-4
    • Eggeling Ch. and Zieger S. [Project report pharmacovigilance analysis] Projektbericht Pharmakovigilanzanalyse. Project report 2013
    • Eggeling Ch. [Data quality in pharmacovigilance data] Datenqualität in Pharmakovigilanzdaten. Master Thesis 2013
    • Zieger S. [Statistical methods of pharmacovigilance data mining] Statistische Methoden des Data Mining in der Pharmakovigilanz. Master Thesis 2013
    • Böhm R, Meybohm, P. [Intoxications and antidotes - part 1] Intoxikationen und Antidote - Teil 1. Notfallmedizin up2date, 2013(2):82-85.
    • Böhm R, Meybohm, P, Kunz T. [Ketamine - established anaethetic with new indications] Ketamin–bewährtes Narkotikum mit neuen Indikationen. Notfallmedizin up2date, 2014(9):292-293.
    • von Hehn L, Zieger S, Freitag-Wolf S, Böhm R, Klein H.-J., Herdegen T. Clinical applications of the OpenVigil 2 pharmacovigilance analysis tool: Reverse disproportionality analyses and detection of drug-drug-interactions.  Naunyn-Schmiedeberg´s Arch Pharmacol (2015) 388  (Suppl 1):S57. DGPT Congress 2015, Poster #229
    • Böhm R, Liebetrau A, Weiler N, Hedderich J, Tag H, Goeder R, Höcker J, Herdegen T, Hohagen F, Adlenhoff J, Schulz-Du Bois C, Schulz-Du Bois A. Cardiotoxicity of intravenous haloperidol - an update. Naunyn-Schmiedeberg´s Arch Pharmacol (2015) 388  (Suppl 1):S32. DGPT Congress 2015,Poster #129
    • Böhm R, Eggeling Ch, Polomski T, Heidebrecht D, von Hehn L, Herdegen T, Klein HJ. Data Quality and Methodological Transparency in Pharmacovigilance.Naunyn-Schmiedeberg´s Arch Pharmacol (2015) 388  (Suppl 1):S58. DGPT Congress 2015. Short talk
    • Böhm R, Herdegen T. Pharmacovigilance applied to clinical neurology and psychiatry. Poster at 2nd Kiel Neuroscience Day 2015
    • Böhm R, Herdegen T. Using the OpenVigil 2 pharmacovigilance tool for guidance for clinical decisions involving newly occurring adverse events. GPTS Congress 2016 Poster #408
    • Böhm R, von Hehn L, Herdegen T, Klein HJ, Bruhn O, Petri H, Höcker J. OpenVigil FDA - Inspection of U.S. American Adverse Drug Events Pharmacovigilance Data and Novel Clinical Applications. PLoS One. 2016 Jun 21;11(6):e0157753. PMID: 27326858 http://dx.plos.org/10.1371/journal.pone.0157753
    • Böhm R, Herdegen T. Using the OpenVigil FDA pharmacovigilance tool to screen for new drug-drug-interactions among neuro- and psychotropic drugs. Poster at Kiel Neuroscience Day 2016
    • Böhm R, Cascorbi I. Pharmacogenetics and Predictive Testing of Drug Hypersensitivity Reactions. Frontiers in Pharmacology 2016; 7: 396
    • Thingholm LB, Rühlemann MC, Koch M, Laucke G, Böhm R, Bang C, Heinsen FA, Frost F, Lerch MM, Homuth G, Kacprowski T, Lieb W, Laudes M, Huttenhower C, Franke A. Gut microbiome associations with diet and medication usage in type 2 diabetes. Poster presentation Cambridge 2017
    • Schulz M, Gradl G, Laufs U, Herdegen T, Werning J, Kieble M, Bruckmüller H, Klein HJ, Böhm R. Temporal synchrony between drug dispensings and adverse drug events? The example of statins & rhabdomyolysis and metamizole or clozapine & agranulocytosis. Poster at ESCP 2017 Presentation at ESCP 2017
    • Böhm R, Petri H, Herdegen T, Klein HJ. Pharmakon 4/2018. [Management of adverse reactions using pharmacovigilance data] Management von Nebenwirkungen mittels Pharmakovigilanz-Daten
    • Böhm R, Proksch E, Schwarz T, Cascorbi I. Dtsch Arztebl Int 2018; 115(29-30): 501-12. [Drug hypersensitivity—diagnosis, genetics, and prevention] Arzneimittelüberempfindlichkeit - Diagnostik, Genetik und Vermeidung
    • Böhm R, Bulin C, Petri H, Herdegen T. Krankenhauspharmazie 2019 (40)2:100-101 [Reporting Ratios for estimation of risks of side effects] Reporting Ratios zur Abschätzung des Risikos von Nebenwirkungen. Poster at 6. Kongress für Arzneimittelinformation der AKDA
    • Böhm R, Bulin C, Tropmann-Frick M, Klein HJ, Herdegen T. Large-scale drug repositioning by safety signals using the OpenVigil 2 pharmacovigilance data analysis toolNaunyn-Schmiedeberg's Archives of Pharmacology 2019; 392(1) Poster at GPTS 2019
    • Thingholm LB, Rühlemann MC, Koch M, Fuqua B, Laucke G, Boehm R, Bang C, Franzosa EA, Hübenthal M, Rahnavard A, Frost F, Lloyd-Price J, Schirmer M, Lusis AJ, Vulpe CD, Lerch MM, Homuth G, Kacprowski T, Schmidt CO, Nöthlings U, Karlsen TH, Lieb W, Laudes M, Franke A, Huttenhower C. Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition. Cell Host Microbe. 2019 Aug 14;26(2):252-264.e10 (announcement in Kieler Nachrichten)
    • Bronsch T, Böhm R, Bulin C, Bergh B, Schreiweis B. Mapping Medication Metadata from the ABDA Data Model to an OpenEHR Medication Archetype: A Qualitative Analysis. Stud Health Technol Inform. 2019 Aug 21;264:1435-1436. doi: 10.3233/SHTI190471.
    • Seoudy AK, Schulte DM, Hollstein T, Böhm R Cascorbi I, Laudes M (2021). Gliflozins for the Treatment of Congestive Heart Failure and Renal Failure in Type 2 Diabetes. Deutsches Ärzteblatt International, 118(8), 122.Fulltext
    • Böhm R, Bulin C, Waetzig V, Cascorbi I, Klein HJ, Herdegen Th. (2021). Pharmacovigilance‐based drug repurposing: The search for inverse signals via OpenVigil identifies putative drugs against viral respiratory infections. British Journal of Clinical Pharmacology https://doi.org/10.1111/bcp.14868
    • Steinbrecht S, Herdegen T, Ankermann T, Böhm R (2022). A pharmacovigilance study of the adverse event "photosensitivity reaction" in children versus adolescents. Poster at GPTS 2022 Posterpresentation with audio
    • Rottmann F, Herdegen T, Cascorbi I, Klein HJ, Böhm R. (2022). Public opinion and information may correlate with harmful adverse events: an OpenVigil pharmacovigilance study. Poster at GPTS 2022 Posterpresentation with audio
  • Peer-reviewed publications referring to OpenVigil or data extracted by OpenVigil

    • Dear scientific community, I can no longer manage to manually list the numerous publications that have used OpenVigil 2 or OpenVigilFDA. Thank you all for your kind support! Visit https://scholar.google.com/scholar?start=90&q=openvigilfda+OR+OpenVigil for an exhaustive list of the more than 500 publications!
    • Diethelm Tschöpe, Peter Bramlage, Christiane Binz, Michael Krekler, Tanja Plate,Evelin Deeg and Anselm K Gitt. Antidiabetic pharmacotherapy and anamnestic hypoglycemia in a large cohort of type 2 diabetic patients - an analysis of the DiaRegis registry. Cardiovasc Diabetol. 2011 Jul 14;10:66. doi: 10.1186/1475-2840-10-66. http://www.cardiab.com/content/10/1/66
    • Li N, Deng Y, Wang D, Qiao Y, Li F. Determination of glibenclamide and puerarin in rat plasma by UPLC–MS/MS: Application to their pharmacokinetic interaction study. Talanta 2013; 104, 109-115. PMID: 23597896
    • Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA Adverse Event Reporting System. Int J Med Sci. 2013 Apr 25;10(7):796-803. doi: 10.7150/ijms.6048. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689877/
    • Yum, S. K., Kim, T., & Hwang, M. Y. (2014). Polycystic ovaries is a disproportionate signal in pharmacovigilance data mining of second generation antipsychotics. Schizophrenia research, 158(1), 275-276. PMID: 25048421
    • Grigoriev I, zu Castell W, Tsvetkov P, Antonov AV. AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System. Pharmacoepidemiol Drug Saf. 2014 Aug;23(8):795-801. doi: 10.1002/pds.3561. Epub 2014 Feb 12. http://onlinelibrary.wiley.com/doi/10.1002/pds.3561/pdf
    • Sarangdhar M, Tabar S, Schmidt C, Kushwaha A, Shah K, Dahlquist JE, Jegga AG, Aronow BJ. Data mining differential clinical outcomes associated with drug regimens using adverse event reporting data. Nature Biotechnology volume 34, pages 697–700 (2016)
    • Etminan M. Risk of intracranial hypertension with intrauterine levonorgestrel: reply. Ther Adv Drug Saf. 2016 Feb; 7(1): 25–26. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4716392/
    • Guo M, Luo H, Samii A, Etminan M. Risk Of Glioblastoma With Tnf Inhibitors. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy (2016). http://onlinelibrary.wiley.com/doi/10.1002/phar.1731/abstract
    • Mammo Z, Guo M, Maberley D, Matsubara J, Etminan M. Oral Bisphosphonates and Risk of Wet Age-Related Macular Degeneration. American Journal of Ophthalmology (2016).
    • Cherepanov, V., Fortmann, S. D., Kim, M. H., Marciniak, T. A., Litvinov, O., Mihalev, K., & Serebruany, V. L. (2017). Annual adverse event profiles after clopidogrel, prasugrel, and ticagrelor in the Food and Drug Administration Adverse Event Reporting System. European Heart Journal-Cardiovascular Pharmacotherapy.
    • Serebruany, V., Kim, M. H., Thevathasan, C., & Marciniak, T. (2018). Assessing Cancer Signal during Oral Antiplatelet Therapy in the Food and Drug Administration Adverse Event Reporting System: Mission Impossible. TH Open, 2(01), e28-e32.
    • Mukthinuthalapati, P. K., Fontana, R. J., Vuppalanchi, R., Chalasani, N., & Ghabril, M. (2018). Celecoxib-induced Liver Injury: Analysis of Published Case Reports and Cases Reported to the Food and Drug Administration. Journal of clinical gastroenterology, 52(2), 114-122.
    • Patel, N., Hatley, O., Berg, A., Romero, K., Wisniowska, B., Hanna, D., ... & Polak, S. (2018). Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin. The AAPS journal, 20(3), 47.
    • Sriramakrishnan GV, Sasi Kumar A. (2018). Finding Adverse Reaction of Lorcaserin Drug Using Effective Data Mining Algorithm. Journal of Advanced Research in Dynamical and Control Systems, 10. Fulltext
    • Siafis S, Papazisis G (2018). Detecting a potential safety signal of antidepressants and type 2 diabetes: a pharmacovigilance‐pharmacodynamic study. British journal of clinical pharmacology, 84(10), 2405-2414. Fulltext
    • Lehrer S, Rheinstein PH, Rosenzweig KE. No Relationship of Anti-Androgens to Alzheimer’s Disease or Cognitive Disorder in the MedWatch Database. Journal of Alzheimer's Disease Reports, 2018, Volume 2, Number 1, Page 123
    • Etminan, M., Sodhi, M., Ganjizadeh-Zavareh, S., Carleton, B., Kezouh, A., & Brophy, J. M. (2019). Oral fluoroquinolones and risk of mitral and aortic regurgitation. Journal of the American College of Cardiology, 74(11), 1444-1450. Fulltext
    • Hübner F, Langan EA, Recke A. (2019). Lichen planus pemphigoides: from lichenoid inflammation to autoantibody-mediated blistering. Frontiers in Immunology, 10. Fulltext
    • Ether N, Leishman D, Bailie M, Lauver A. (2019). Relationship of clinical adverse event reports to models of arrhythmia risk. Journal of pharmacological and toxicological methods, 100, 106622. Fulltext
    • Fang T, Maberley DA, Etminan M. (2019). Ocular adverse events with immune checkpoint inhibitors. Journal of current ophthalmology, 31(3), 319-322. Fulltext
    • Ji HH, Tang XW, Dong Z, Song L, Jia YT. (2019). Adverse event profiles of anti-CTLA-4 and anti-PD-1 monoclonal antibodies alone or in combination: analysis of spontaneous reports submitted to FAERS. Clinical drug investigation, 39(3), 319-330.
    • Lehrer S, Rheinstein PH. (2019). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce suicidal ideation and depression. Discovery Medicine, 28(154), 205-212. Fulltext
    • Nikolopoulou, V., Siafis, S., Milonas, A., Kouvelas, D., & Papazisis, G. (2019). Safety of the newer disease-modifying agents for multiple sclerosis: disproportionality analysis in the FDA Adverse Events Reporting System database. Dialogues in Clinical Neuroscience & Mental Health, 2(2), 81-93. Fulltext
    • Lapeyre-Mestre M., Montastruc F. (2019). Interest of pharmacoepidemiology for pharmacodynamics and analysis of the mechanism of action of drugs. Therapies, 74(2), 209-214. Fulltext
    • Neha R, Beulah E, Anusha B, Vasista S, Stephy C, Subeesh V. (2020). Aromatase inhibitors associated osteonecrosis of jaw: signal refining to identify pseudo safety signals. International Journal of Clinical Pharmacy, 1-7. Fulltext
    • Spachos D, Siafis S, Bamidis P, Kouvelas D, Papazisis G. (2020). Combining big data search analytics and the FDA Adverse Event Reporting System database to detect a potential safety signal of mirtazapine abuse. Health Informatics Journal, 1460458219901232. Fulltext
    • Ran C, Zhou H, Tan C, Tan J, Zhang Z, Zhao W. (2020). Detection and Evaluation of Adverse Drug Reaction Signals of Antidepressants Based on FDA Adverse Event Reporting System Database. Open Journal of Depression, 9(02), 17. Fulltext
    • Jiao XF, Li HL, Jiao XY, Guo YC, Zhang C, Yang CS, Zeng LN Bo ZY, Chen Z, Song HB, Zhang LL (2020). Ovary and uterus related adverse events associated with statin use: an analysis of the FDA Adverse Event Reporting System. Scientific Reports, 10(1), 1-10. Fulltext
    • Huang, L., Liu, Y., Li, H., Huang, W., Geng, R., Tang, Z., & Jiang, Y. (2021). Bullous Pemphigoid and Diabetes medications: A disproportionality analysis based on the FDA Adverse Event Reporting System. International journal of medical sciences, 18(9), 1946. Fulltext
    • Zhou, Y., Ren, Q., Hu, R., Zheng, K., Qin, Y., & Li, X. (2021). Does HIF-PHI increased risk of gastrointestinal hemorrhage in patients with renal anemia: a review of cases reported to the US Food and drug administration adverse event reporting system. Renal Failure, 43(1), 1170-1171. Fulltext
    • Kim, Y. S., Brar, S., D’Albo, N., Dey, A., Shah, S., Ganatra, S., & Dani, S. S. (2021). Five Years of Sacubitril/Valsartan—a Safety Analysis of Randomized Clinical Trials and Real-World Pharmacovigilance. Cardiovascular Drugs and Therapy, 1-10. Fulltext
    • Kumar, V., Singh, A. P., Wheeler, N., Galindo, C. L., & Kim, J. J. (2021). Safety profile of D-penicillamine: a comprehensive pharmacovigilance analysis by FDA adverse event reporting system. Expert Opinion on Drug Safety, 1-8. Fulltext
    • Meng, L., Yang, B., Qiu, F., Jia, Y., Sun, S., Yang, J., & Huang, J. (2021). Lung Cancer Adverse Events Reports for Angiotensin-Converting Enzyme Inhibitors: Data Mining of the FDA Adverse Event Reporting System Database. Frontiers in medicine, 8, 36. Fulltext
    • Papazisis, G., Spachos, D., Siafis, S., Pandria, N., Deligianni, E., Tsakiridis, I., & Goulas, A. (2021). Assessment of the safety signal for the abuse potential of pregabalin and gabapentin using the FAERS database and big data search analytics. Frontiers in Psychiatry, 12. Fulltext
    • Stamatellos, V. P., Siafis, S., & Papazisis, G. (2021). Disease-modifying agents for multiple sclerosis and the risk for reporting cancer: a disproportionality analysis using US Food and Drug Administration Adverse Event Reporting System (FAERS) database. British Journal of Clinical Pharmacology. Fulltext
    • Nguyen, D. D., Marchese, M., Cone, E. B., Paciotti, M., Basaria, S., Bhojani, N., & Trinh, Q. D. (2021). Investigation of suicidality and psychological adverse events in patients treated with finasteride. JAMA dermatology, 157(1), 35-42. Fulltext
    • Tian, X., Yao, Y., He, G., Jia, Y., Wang, K., & Chen, L. (2021). Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database. Scientific Reports, 11(1), 1-9. Fulltext
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    • Trumbo, H., Kaluza, K., Numan, S., & Goodnough, L. T. (2021). Frequency and associated costs of anaphylaxis-and hypersensitivity-related adverse events for intravenous iron products in the USA: an analysis using the US Food and Drug Administration Adverse Event Reporting System. Drug safety, 44(1), 107-119.Fulltext
    • WU, Z., HE, N., CHENG, Y., ZHAI, S., & LIU, W. (2022). Data mining of adverse drug reaction signals for ado-trastuzumab emtansine and brentuximab vedotin based on FAERS database. China Pharmacy, 740-744.
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  • Other Articles and Books referring to OpenVigil
    • News & History
    • 2025-03-20: Update to FAERS data Q4/2024.
    • 2024-12-22: New URLs for OpenVigil 2 and OpenVigilFDA, latest FAERS data (2024Q3) in OpenVigil 2, major speed-ups for OpenVigil 2, Google Scholar link to the more than 500 publications based on data extracted using OpenVigil 2 or OpenVigilFDA
    • 2024-06-13: OpenVigil 2 bugfixes and update to latest Drugbank and FAERS data (2024Q1)
    • 2023-11-23: Minor bugfixes to OpenVigil 2 and update to the most current FDA AERS data (2023Q3)
    • 2020-09-25: New installation of OpenVigil 2 on a new server with improved drug/brand mapping logic (67.5% of all case reports are now successfully imported)
    • 2019-01-08: Bug fixes of counting issues in OpenVigil 1, OpenVigil 1.2.6c released
    • 2018-07-09: OpenVigil 2 contains now AERS data up to 2018Q1. Reverse disproportionality analyses were published in the journals Pharmakon and Deutsches Ärzteblatt.
    • 2018-01-08: OpenVigil 2.1-MedDRA WAR file and sources are available
    • 2017-11-15: The latest FAERS data up to Q2/2017 is now available in OpenVigil 2
    • 2017-10-18: MedDRA has been incorporated into OpenVigil 2.1 and is now available
    • 2016-10-10: The contingency table calculator has been expanded and announced in Frontiers in Pharmacology
    • 2016-06-21: OpenVigilFDA 1.0.2 is released and announced in PLOS ONE
    • 2015-11-24: openvigil.sf.net is certified to comply with the Health On the Net code (HONConduct959695)
    • 2015-10-19: OpenVigilFDA 1.0 released.
    • 2015-10-18: OpenVigil 2.0 and 2.1 are now available for public usage without prior registration.
    • 2015-09-29: Successful validation of data extraction (OpenVigil FDA 1.0rc4) and calculations of measurements of disproportionality analyses (all software)
      New online-tool to calculate arbitrary 2x2 contingency tables and measurements of disproprotionality, e.g., relative odds ratio
    • 2015-08-27: OpenVigil 1 is being tested with German pharmacovigilance data
    • 2015-08-26: OpenVigilFDA is available for beta-testing
    • 2015-07-31: An interface to the openFDA API (OpenVigilFDA) is being developed
    • Logo Deutsches Arzneiprüfungsinstitut e. V. (DAPI)2015-07-23 The German Institute for Drug Use Evaluation (Deutsches Arzneiprüfungsinstitut e. V. (DAPI), http://www.dapi.de), Berlin, and the creators of OpenVigil (Institute of Experimental and Clinical Pharmacology and Department of Computer Science, University of Kiel) have joined their expertise and commenced to assess how pharmacoepidemiologic and pharmacovigilance data can improve signal detection for certain drug/adverse event-combinations. The correlation of data on drug usage and adverse events might also improve estimations of the incidence of a certain drug/adverse event-association.
    • 2015-07-03: New webpage with more general information on pharmacovigilance
    • Logo WWKLogo UKSH2015-06-30 (Updated 2015-10-03) The ability of OpenVigil to help with clinical decisions (project title "From the Adverse Event to the most likely Culprit") is now being evaluated at wards at the UKSH Kiel and at the hospital pharmacy at Werner Wicker Klinik, Bad Wildungen. The goal is to find the most likely pharmacological cause of a newly occurring adverse event during pharmacotherapy. Medical doctors and pharmacists are being trained how to systematically estimate the most likely cause of a new adverse event by using both "eminence-based approaches" (i.e., internal SOPs which specialist or guideline to consult) and evidence-based medicine (EBM) like SIDER ( http://sideeffects.embl.de/ ), OpenVigilFDA ("List the most likely drugs in a list of medications causing a specified adverse event and compare likelihoods") and OpenVigil 2 (reverse DPA "Frequentist methods"). During this project, the utility of this analysis method shall be evaluated (e.g., decrease of costs, adverse events or length of stay in hospital) and a new user interface to address this clinical problem and offering easily readable output will be created (in parts done in OpenVigilFDA, pending for OpenVigil 2).
    • 2015-03-11: Oral and poster presentations of OpenVigil 2 and potential uses at the annual congress of the DGPT
    • 2014-09-12: OpenVigil 1.2.6b is released
      Changes: Major bugfixes to importer, SQL clean-ups with speed improvements
    • 2013-06-21: First public release of OpenVigil 2.0-testing
    • 2012-05-08: Development of OpenVigil 2 is started
    • 2012-01-13: OpenVigil 1.2.3 released and announced in Nature Biotechnology
    • 2011-05-03: Project registered at SourceForge
    • 2009: First pharmacovigilance-based analyses (cf. publication list) creating a need for a tool to easily access and analyse pharmacovigilance data