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A Novel Approach to Visualize Risk Minimization Effectiveness: Peeping at the 2012 UK Proton Pump Inhibitor Label Change Using a Rapid Cycle Analysis Tool

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Abstract

Introduction

Evaluation of risk minimization (RM) actions is an emerging area of regulatory science, often without tools to rapidly and systematically assess their effectiveness.

Purpose

The aim of this study was to evaluate whether chronographs, typically used for rapid signal detection in observational longitudinal databases, could be used to visualize RM effectiveness. We evaluated the UK Medicines and Healthcare products Regulatory Agency (MHRA) 2012 proton-pump inhibitors (PPIs) class-wide label change that warned of increased risk of bone fracture, advocated to limit duration of use, and recommended to treat those at risk for osteoporosis according to clinical guidelines.

Methods

The cohort consisted of adults aged 18 years and above prescribed one of the five PPIs available in the UK The Health Improvement Network (THIN) database through September 2015. Four chronographs were compared using drug episodes that started before (PRE) and after (POST) the 20 April 2012 MHRA warning; fracture and osteoporosis were evaluated separately. Chronographs show a measure of observed/expected events, the Information Component (IC) and 95% credibility interval (CI), calculated at monthly time intervals relative to the start date of a prescription, and summed to estimate IC over a 3-year period; IC > 0 indicates observed > expected events. We hypothesized that chronographs may assess RM effectiveness if stratified by PRE/POST an RM intervention such as a label change.

Results

There were 1,588,973 and 664,601 PPI users in the PRE and POST periods, respectively. We observed a 4.6% reduction in the proportion of long-term PPI episodes and a 4.1% reduction in the overall proportion of the THIN population using PPIs. Compared with the PRE chronographs, when both visually comparing and when examining the summed ICs for fracture in the POST period, a significant reduction was observed overall (IC = 0.024 [95% CI 0.015 to 0.33] PRE vs − 0.141 [95% CI − 0.162 to − 0.120] POST), suggesting less observed events than expected, and prior to PPI start, suggestive of strong channeling (IC = − 0.027 [95% CI − 0.037 to − 0.017] PRE vs − 0.291 [95% CI − 0.308 to − 0.274] POST). Results were qualitatively similar for osteoporosis.

Conclusions

This pilot demonstrated a novel application of a visual, rapid analysis technique to assess RM effectiveness, and supported a hypothesis that prescribers altered some behaviors after the MHRA label change, such as channeling patients at risk of fracture or osteoporosis away from PPI use and potentially reducing fracture outcomes. Limitations include lack of confounding control and outcomes defined only by diagnosis code. Results demonstrate the potential to use large healthcare databases with chronographs to rapidly assess RM effectiveness, similar to signal detection in pharmacovigilance, and may help design more comprehensive RM evaluation studies.

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References

  1. Strom BL, editor. Pharmacoepidemiology. 4th ed. West Sussex: Wiley; 2006.

    Google Scholar 

  2. US Food and Drug Administration Draft Guidance. REMS assessment: planning and reporting guidance for industry. 2019. https://www.fda.gov/media/119790/download. Accessed 8 May 2019.

  3. US Food and Drug Administration Draft Guidance. Survey methodologies to assess REMS goals that relate to knowledge guidance for industry. 2019. https://www.fda.gov/media/119789/download. Accessed 8 May 2019.

  4. EU GVP Module V. http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guideline/2016/02/WC500202424.pdf. Accessed 25 Mar 2019.

  5. EU GVP Module XVI, Revision 2. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/02/WC500162051.pdf. Accessed 25 Mar 2019.

  6. Banerjee AK, Zomerdijk IM, Wooder S, et al. Post-approval evaluation of effectiveness of risk minimisation: methods, challenges and interpretation. Drug Saf. 2014;37:33. https://doi.org/10.1007/s40264-013-0126-7.

    Article  PubMed  Google Scholar 

  7. Levinson DR. FDA lacks comprehensive data to determine whether REMS improve drug safety. Institute of Medicine, Office of the US Inspector General. https://oig.hhs.gov/oei/reports/oei-04-11-00510.pdf. Accessed 25 Mar 2019.

  8. de Luise C, Schade R, Trifiro G, Pederson L, Herings R, Sturkenboom M. Trends in prescribing patterns for cabergoline in 4 European countries. Pharmacoepidemiol Drug Saf. 2015;24-S1:219.

    Google Scholar 

  9. Ball R, Robb M, Anderson S, Dal Pan G. The FDA’s sentinel initiative—a comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016;99:265–8. https://doi.org/10.1002/cpt.320.

    Article  CAS  PubMed  Google Scholar 

  10. Butler, et al. Changing patterns of asthma medication use related to US Food and Drug Administration long-acting β2-agonist regulation from 2005–2011. J Allergy Clin Immunol. 2016;137(3):710–7.

    Article  CAS  PubMed  Google Scholar 

  11. Sobel RE, Bate A, Marshall J, et al. Do FDA label changes work? Assessment of the 2010 class label change for proton pump inhibitors using the Sentinel System’s analytic tools. Pharmacoepidemiol Drug Saf. 2018;27:332–9. https://doi.org/10.1002/pds.4392.

    Article  CAS  PubMed  Google Scholar 

  12. Vora P, Artime E, Soriano-Gabarró M, Qizilbash N, Singh V, Asiimwe A. A review of studies evaluating the effectiveness of risk minimisation measures in Europe using the European Union electronic Register of Post-Authorization Studies. Pharmacoepidemiol Drug Saf. 2018;27:695–706. https://doi.org/10.1002/pds.4434.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Norén 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 

  14. Cederholm S, Hill G, Asiimwe A, Bate A, Bhayat F, Brobert GP, Bergvall T, Ansell D, Star K, Norén GN. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network. Drug Saf. 2015;38(1):87–100.

    Article  CAS  PubMed  Google Scholar 

  15. Donegan K, Owen R, Bird H, Burch B, Smith A, Tregunno P. Exploring the potential routine use of electronic healthcare record data to strengthen early signal assessment in UK medicines regulation: proof-of-concept study. Drug Saf. 2018;41:899–910.

    Article  PubMed  Google Scholar 

  16. Bate A, Evans SJW. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol Drug Saf. 2009;18(6):427–36.

    Article  CAS  PubMed  Google Scholar 

  17. Bate A, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54:315–21.

    Article  CAS  PubMed  Google Scholar 

  18. Wisniewski AFZ, Bate A, Bousquet C, et al. Good signal detection practices: evidence from IMI PROTECT. Drug Sa. 2016;39(6):469–90.

    Article  Google Scholar 

  19. Norén GN, Bergvall T, Ryan PB, et al. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(S1):S107–21.

    Article  PubMed  Google Scholar 

  20. UK MHRA Safety Communication on Potential Increased Risk of Fractures with PPIs. https://www.gov.uk/drug-safety-update/proton-pump-inhibitors-in-long-term-use-increased-risk-of-fracture#further-information. Accessed 25 Mar 2019.

  21. Blak B, Thompson M, Dattani H, Bourke A. Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates. J Innov Health Inform. 2011;19(4):251–5. https://doi.org/10.14236/jhi.v19i4.820.

    Article  Google Scholar 

  22. Yu-Xiao Yang Y-X, Lewis JD, Epstein S, Metz DC. Long-term proton pump inhibitor therapy and risk of hip fracture. JAMA. 2006;296:2947–53.

    Article  PubMed  Google Scholar 

  23. Kaye JA, Jick H. Proton pump inhibitor use and risk of hip fractures in patients without major risk factors. Pharmacotherapy. 2008;28(8):951–9.

    Article  CAS  PubMed  Google Scholar 

  24. Yu EW, Bauer SC, Bain PA, Bauer DC. Proton pump inhibitors and risk of fractures: a meta-analysis of 11 international studies. Am J Med. 2011;124:519–26. https://doi.org/10.1016/j.amjmed.2011.01.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. CVW Analytics. https://www.commoninf.com/products/commonwealth-vigilance-workbench-cvw/cvw-analytics/. Accessed 25 Mar 2019.

  26. Juhlin K, Ye X, Star K, Norén GN. Outlier removal to uncover patterns in adverse drug reaction surveillance—a simple unmasking strategy. Pharmacoepidemiol Drug Saf. 2013;22:1119–29. https://doi.org/10.1002/pds.3474.

    Article  PubMed  Google Scholar 

  27. Othman F, Card TR, Crooks CJ. Proton pump inhibitor prescribing patterns in the UK: a primary care database study. Pharmacoepidemiol Drug Saf. 2016;25:1079–87. https://doi.org/10.1002/pds.4043.

    Article  CAS  PubMed  Google Scholar 

  28. Wallerstedt SM, Fastbom J, Linke J, Vitols S. Long-term use of proton pump inhibitors and prevalence of disease- and drug-related reasons for gastroprotection—a cross-sectional population-based study. Pharmacoepidemiol Drug Saf. 2017;26:9–16. https://doi.org/10.1002/pds.4135.

    Article  CAS  PubMed  Google Scholar 

  29. Bustillos H, Leer K, Kitten A, Reveles KR. A cross-sectional study of national outpatient gastric acid suppressant prescribing in the United States between 2009 and 2015. PLoS One. 2018;13(11):e0208461. https://doi.org/10.1371/journal.pone.0208461.

    Article  PubMed  PubMed Central  Google Scholar 

  30. UK MHRA Public assessment report: pharmacy to general sales list, reclassification nexium control 20mg gastro-resistant tablets (esomeprazole). 2015. http://www.mhra.gov.uk/home/groups/s-par/documents/websiteresources/con504924.pdf. Accessed 25 Mar 2019.

  31. Nyeland ME, Laursen MV, Callréus T. Evaluating the effectiveness of risk minimisation measures: the application of a conceptual framework to Danish real-world dabigatran data. Pharmacoepidemiol Drug Saf. 2017;26:607–14. https://doi.org/10.1002/pds.4203.

    Article  PubMed  Google Scholar 

  32. Kesselheim AS, Campbell EG, Schneeweiss S, Rausch P, Lappin BM, Zhou EH, Seeger JD, Brownstein JS, Woloshin S, Schwartz LM, Toomey T, Dal Pan GJ, Avorn J. Methodological approaches to evaluate the impact of FDA drug safety communications. Drug Saf. 2015;38(6):565–75. https://doi.org/10.1007/s40264-015-0291-y.

    Article  CAS  PubMed  Google Scholar 

  33. Ogdie A, Alehashemi S, Love TJ, et al. Validity of psoriatic arthritis and capture of disease modifying antirheumatic drugs in the health improvement network. Pharmacoepidemiol Drug Saf. 2014;23(9):918–22. https://doi.org/10.1002/pds.3677.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cea Soriano L, Soriano-Gabarró M, García Rodríguez LA. Validation of low-dose aspirin prescription data in The Health Improvement Network: how much misclassification due to over-the-counter use? Pharmacoepidemiol Drug Saf. 2016;25:392–8. https://doi.org/10.1002/pds.3926.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Dr. Robert F. Reynolds for his critical review of this manuscript. We would also like to thank Mr. Geoff Gordon and Mr. William Lebow of Commonwealth Informatics, Ms. Harshvinder Bhullar, Mr. Mustafa Dungarwalla, and the THIN/IQVIA staff for their support of this project.

Author information

Authors and Affiliations

  1. Epidemiology/Worldwide Research and Development, Pfizer Inc, 235 East 42nd St. MS# 219-9-1, New York, NY, 10017, USA

    Rachel E. Sobel & Andrew Bate

  2. Commonwealth Informatics Inc, a Genpact Company, Waltham, MA, USA

    William Blackwell & David M. Fram

Authors
  1. Rachel E. Sobel
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  2. William Blackwell
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  3. David M. Fram
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  4. Andrew Bate
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Corresponding author

Correspondence to Rachel E. Sobel.

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Funding

No specific funding was provided to conduct this study.

Conflict of interest

Rachel E. Sobel was an employee and is a shareholder of Pfizer Inc; Andrew Bate is an employee and shareholder of Pfizer Inc., the manufacturer of one or more PPIs described in this study; the views expressed in this manuscript are their own and do not necessarily reflect those of Pfizer. They contributed to the study design, analysis, and interpretation of data, the writing of the report, and the decision to submit the report for publication. William Blackwell and David M. Fram are employees of Commonwealth Informatics Inc, a Genpact company, which developed the Commonwealth Vigilance Workbench software that was used to generate the chronographs and associated analyses for this study.

Ethical approval

The protocol was reviewed and approved by the UK Scientific Review Committee (SRC Reference #17THIN012).

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Sobel, R.E., Blackwell, W., Fram, D.M. et al. A Novel Approach to Visualize Risk Minimization Effectiveness: Peeping at the 2012 UK Proton Pump Inhibitor Label Change Using a Rapid Cycle Analysis Tool. Drug Saf 42, 1365–1376 (2019). https://doi.org/10.1007/s40264-019-00853-y

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