Skip to main content
SpringerLink
Log in

Sequence Symmetry Analysis as a Signal Detection Tool for Potential Heart Failure Adverse Events in an Administrative Claims Database

  • Original Research Article
  • Published:
Drug Safety Aims and scope Submit manuscript

Abstract

Introduction

The potential for routine sequence symmetry analysis (SSA) signal detection in health claims databases to detect new safety signals of medicines is unknown.

Objective

Our objective was to assess the potential utility of SSA as a signal detection tool in health claims data for detecting medicines with potential heart failure (HF) adverse event signals.

Methods

We applied the SSA method to all subsidized single-ingredient medicines in Australia. The source of data was the Australian Government Department of Veterans’ Affairs (DVA) administrative claims database using data collected between 2002 and 2011. We used first ever HF hospitalization and frusemide initiation as indicators for HF. A signal was considered to be present if the lower limit of the 95 % confidence interval for the adjusted sequence ratio was greater than one. To identify potential new signals of HF, we excluded medicines where HF or edema was listed in the product information (PI) of that medicine or for any other medicine in the same class. We also excluded medicines that were used in HF treatment and medicines indicated for diseases that may contribute to the development of HF.

Results

We tested 691 medicines. HF signals were detected for 12 % (80/691) using the hospitalization event and 22 % (153/691) using frusemide initiation. Among medicines that did not have HF listed in the PI, SSA found 11 % (44/397) associated with HF hospitalization and 15 % (60/397) associated with frusemide initiation. Of the medicines tested in which no other medicine in the same class had HF or edema in the PI, and where the medicine was not indicated for a disease that is a risk factor for HF, potential new signals were generated for 2–3 % of these medicines tested (12 of 397 medicines using HF hospitalization and 9 of 397 medicines using frusemide initiation).

Conclusion

SSA generated potential new signals of HF for some anti-glaucoma and anti-dyspepsia medicines. For some of the potential signals, the event is biologically plausible and some have pre-marketing and post-marketing case reports to support the finding. Confirmation of these signals using cohort studies is required.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Han J, Kamber M. Data mining: concepts and techniques. 2nd ed. California: Morgan Kaufman; 2006.

    Google Scholar 

  2. Hauben M, Madigan D, Gerrits CM, Walsh L, Van Puijenbroek EP. The role of data mining in pharmacovigilance. Exp Opin Drug Saf. 2005;4(5):929–48.

    Article  CAS  Google Scholar 

  3. Evans SJW, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483–6.

    Article  CAS  PubMed  Google Scholar 

  4. Egberts ACG, Meyboom RHB, Van Puijenbroek EP. Use of measures of disproportionality in pharmacovigilance: three Dutch examples. Drug Saf. 2002;25(6):453–8.

    Article  PubMed  Google Scholar 

  5. Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54(4):315–21.

    Article  CAS  PubMed  Google Scholar 

  6. Gould AL. Practical pharmacovigilance analysis strategies. Pharmacoepidemiol Drug Saf. 2003;12(7):559–74.

    Article  PubMed  Google Scholar 

  7. Lindquist M, Stahl M, Bate A, Edwards IR, Meyboom RHB. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf. 2000;23(6):533–42.

    Article  CAS  PubMed  Google Scholar 

  8. Lehman HP, Chen J, Gould AL, Kassekert R, Beninger PR, Carney R. An evaluation of computer-aided disproportionality analysis for post-marketing signal detection. Clin Pharmacol Ther. 2007;82(2):173–80.

    Article  CAS  PubMed  Google Scholar 

  9. Virnig BA, McBean M. Administrative data for public health surveillance and planning. Ann Rev Pub Health. 2001;22:213–30.

    Article  CAS  Google Scholar 

  10. Curtis JR, Cheng H, Delzell E, Fram D, Kilgore M, Saag K. Adaptation of bayesian data mining algorithms to longitudinal claims data: coxib safety as an example. Med Care. 2008;46(9):969–75.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Schuemie MJ. Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD. Pharmacoepidemiol Drug Saf. 2011;20(3):292–9.

    Article  CAS  PubMed  Google Scholar 

  12. Brown JS, Kulldorff M, Chan KA, Davis RL, Graham D, Pettus PT, et al. Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidemiol Drug Saf. 2007;16(12):1275–84.

    Article  PubMed  Google Scholar 

  13. Brown J, Petronis K, Bate A, Zhang F, Dashevsky I, Kulldorff M, et al. Drug adverse event detection in health plan data using the Gamma Poisson Shrinker and comparison to the Tree-based Scan Statistic. Pharmaceutics. 2013;5(1):179–200.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jin H, Chen J, He H, Kelman C, McAullay D, O’Keefe CM. Signaling potential adverse drug reactions from administrative health databases. IEEE Trans Knowl Data Eng. 2010;22(6):839–53.

    Article  Google Scholar 

  15. Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Min Knowl Discov. 2010;20:361–87.

    Article  Google Scholar 

  16. Coloma PM, Schuemie MJ, Trifirò G, Furlong L, van Mulligen E, Bauer-Mehren A. Drug induced acute myocardial infarction: Identifying ‘prime suspects’ from electronic healthcare records-based surveillances system. PLoS One. 2013;8(8):e72148.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vegter S, De Jong-Van Den Berg LTW. Misdiagnosis and mistreatment of a common side effect—angiotensin-converting enzyme inhibitor-induced cough. Br J Clin Pharmacol. 2010;69(2):200–3.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Garrison SR, Dormuth CR, Morrow RL, Carney GA, Khan KM. Nocturnal leg cramps and prescription use that precedes them: a sequence symmetry analysis. Arch Intern Med. 2012;172(2):120–6.

    Article  PubMed  Google Scholar 

  19. Corrao G, Botteri E, Bagnardi V, Zambon A, Carobbio A, Falcone C, et al. Generating signals of drug-adverse effects from prescription databases and application to the risk of arrhythmia associated with antibacterials. Pharmacoepidemiol Drug Saf. 2005;14(1):31–40.

    Article  PubMed  Google Scholar 

  20. Wahab IA, Pratt NL, Wiese MD, Kalisch LM, Roughead EE. The validity of sequence symmetry analysis (SSA) for adverse drug reaction signal detection. Pharmacoepidemiol Drug Saf. 2013;22(5):496–502.

    Article  PubMed  Google Scholar 

  21. Pratt N, Andersen M, Bergman U, Choi N-K, Gerhard T, Huang C, et al. Multi-country rapid adverse drug event assessment: the Asian Pharmacoepidemiology Network (AsPEN) antipsychotic and acute hyperglycaemia study. Pharmacoepidemiol Drug Saf. 2013;22(9):915–24.

    CAS  PubMed  Google Scholar 

  22. Australian Government Department of Veterans’ Affairs. Treatment population statistics. Quarterly Report-March 2011; 2011 [cited 2015 May 30]. Available from: http://www.dva.gov.au/aboutDVA/Statistics/Documents/TpopMar2011.pdf.

  23. World Health Organization Collaborating Centre for Drug Statistics Methodology. Anatomical Therapeutic Chemical Code Classification index with Defined Daily Doses 2015; 2015 [cited 2015 May 30]. Available from: http://www.whocc.no/atcddd/.

  24. Australian Government, Department of Health and Ageing. Schedule of Pharmaceutical Benefits. PBS for health professional 2015; 2015 [cited 2015 May 30]. Available from: http://www.pbs.gov.au/info/healthpro/explanatorynotes/section1/Section_1_2_Explanatory_Notes.

  25. National Centre for Classification in Health. International statistical classification of diseases and related health problems, Tenth Revision, Australian Modification (ICD-10-AM). National Centre for Classification in Health, Faculty of Health Sciences, University of Sydney; 2004.

  26. Australian Government. Department of Health and Ageing. Therapeutic Goods Administration. Information about prescription medicines in Australia; 2015 [cited 2015 May 30]. Available from: https://www.ebs.tga.gov.au/.

  27. Watson RDS, Gibbs CR, Lip GYH. ABC of heart failure: clinical features and complications. BMJ. 2000;320(7229):236–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dargie H. Heart failure post-myocardial infarction: a review of the issues. Heart. 2005;91(Suppl 2):ii3–ii6.

  29. Tsiropoulos I, Andersen M, Hallas J. Adverse events with use of antiepileptic drugs: a prescription and event symmetry analysis. Pharmacoepidemiol Drug Saf. 2009;18(6):483–91.

    Article  CAS  PubMed  Google Scholar 

  30. Hallas J. Evidence of depression provoked by cardiovascular medication: a prescription sequence symmetry analysis. Epidemiol. 1996;7(5):478–84.

    Article  CAS  Google Scholar 

  31. Uppsala Monitoring Centre. Latanoprost Safety Report 2010.

  32. United States Department of Health and Services. U.S Food and Drug Administration. Drugs@FDA: FDA Approved Drug Products. Travatan; 2015 [cited 2015 May 30]. Available from: http://www.accessdata.fda.gov/drugsatfda_docs/nda/2001/21257_Travatan_medr_P1.pdf.

  33. Parker RE, Strandhoy JW. A vasoconstrictor cardiogenic chemoreflex induced by prostaglandin F2 alpha. Am J Physiol Heart Circ Physiol. 1981;240(4):H528–32.

    CAS  Google Scholar 

  34. Schror K. Prostaglandin-mediated actions of the renin-angiotensin system. Arzneimittelforschung. 1993;43(2A):236–41.

    CAS  PubMed  Google Scholar 

  35. Castellani S, Paladini B, Paniccia R, Di Serio C, Vallotti B, Ungar A, et al. Increased renal formation of thromboxane A 2 and prostaglandin F 2α in heart failure. Am Heart J. 1997;133(1):94–100.

    Article  CAS  PubMed  Google Scholar 

  36. Hill AB. The environment and disease: association or causation? Proc R Soc Med London. 1965;58:295–300.

    CAS  Google Scholar 

  37. Mira E. Betahistine in the treatment of vertigo. History and clinical implications of recent pharmacological researches. Acta Otorhinolaryngol Ital. 2001;21(3 Suppl 66):1–7.

  38. Borow KM, Ehler D, Berlin R, Neumann A. Influence of histamine receptors on basal left ventricular contractile tone in humans: assessment using the H2 receptor antagonist famotidine and the beta-adrenoceptor antagonist esmolol as pharmacologic probes. J Am Coll Cardiol. 1992;19(6):1229–36.

    Article  CAS  PubMed  Google Scholar 

  39. Hilleman DE, Mohiuddin SM, Williams MA, Gannon JM, Mathias RJ, Thalken LJ. Impact of chronic oral H2-antagonist therapy on left ventricular systolic function and exercise capacity. J Clin Pharmacol. 1992;32(11):1033–7.

    Article  CAS  PubMed  Google Scholar 

  40. Malinowska B, Godlewski G, Schlicker E. Histamine H3 receptors: general characterization and their function in the cardiovascular system. J Physiol Pharmacol. 1998;49(2):191–211.

    CAS  PubMed  Google Scholar 

  41. Solomon SD, Wolff S, Jarboe LA, Wolfe MM, Lee RT. Effects of histamine type 2-receptor antagonists cimetidine and famotidine on left ventricular systolic function in chronic congestive heart failure. Am J Cardiol. 1993;72(15):1163–6.

    Article  CAS  PubMed  Google Scholar 

  42. Dobnig H. A review of teriparatide and its clinical efficacy in the treatment of osteoporosis. Expert Opin Pharmacother. 2004;5(5):1153–62.

    Article  CAS  PubMed  Google Scholar 

  43. Hagström E, Ingelsson E, Sundström J, Hellman P, Larsson TE, Berglund L, Melhus H, Held C, Michaëlsson K, Lind L, Arnlöv J. Plasma parathyroid hormone and risk of congestive heart failure in the community. Eur J Heart Fail. 2010;12(11):1186–92.

    Article  PubMed  Google Scholar 

  44. Patrik A, Erik R, Ronnie W. Primary hyperparathyroidism and heart disease: a review. Eur Heart J. 2004;25(20):1776–87.

    Article  Google Scholar 

  45. Kaul P, Ezekowitz JA, Armstrong PW, Leung BK, Savu A, Welsh RC, et al. Incidence of heart failure and mortality after acute coronary syndromes. Am Heart J. 2013;165(3):379–85.e2.

  46. Walker AM. Confounding by indication. Epidemiol. 1996;7(4):335–6.

    CAS  Google Scholar 

  47. Nor GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Min Knowl Discov. 2010;20(3):361–87.

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank DVA for providing the data used in this study. We would also like to thank the Uppsala Monitoring Centre for providing prostaglandin eye drop ADR reports. The supplied data come from a variety of sources, including both regulated and voluntary sources. The likelihood of a causal relationship is not the same in all reports. The study results, discussion, and conclusion are those of the authors and do not represent the opinion of the World Health Organization.

Author information

Authors and Affiliations

  1. Faculty of Pharmacy, Cyberjaya University College of Medical Sciences, 3410, Jalan Teknokrat 3, Cyber 4, 63000, Cyberjaya, Selangor, Malaysia

    Izyan A. Wahab

  2. School of Pharmacy and Medical Sciences, Quality Use of Medicines and Pharmacy Research Centre, Sansom Institute, University of South Australia, Adelaide, SA, Australia

    Nicole L. Pratt, Lisa Kalisch Ellett & Elizabeth E. Roughead

Authors
  1. Izyan A. Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicole L. Pratt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lisa Kalisch Ellett
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth E. Roughead
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Izyan A. Wahab.

Ethics declarations

Funding

This work is funded by a National Health and Medical Research Centre (NHMRC) Grant, Centre of Research Excellence in post-marketing surveillance of medicines and medical devices GNT 1040938. Nicole Pratt is supported by an NHMRC Early Career Research Fellowship GNT 1035889. Dr. Izyan A. Wahab was supported by a Malaysian Government PhD scholarship.

Conflict of interest

Izyan A. Wahab, Nicole L. Pratt, Lisa Kalisch Ellett, and Elizabeth E. Roughead have no conflicts of interest that are directly relevant to the content of the manuscript.

Statement about prior postings and presentations

The results of the study have not been previously presented at any proceedings or conferences.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 202 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wahab, I.A., Pratt, N.L., Ellett, L.K. et al. Sequence Symmetry Analysis as a Signal Detection Tool for Potential Heart Failure Adverse Events in an Administrative Claims Database. Drug Saf 39, 347–354 (2016). https://doi.org/10.1007/s40264-015-0391-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40264-015-0391-8

Keywords

Search

Navigation