Journal List > J Korean Soc Med Inform > v.15(2) > 1035522

Park, Lee, Kim, Kim, Kam, Choi, Han, Kang, and Park: A Data Warehouse Based Retrospective Post-marketing Surveillance Method: A Feasibility Test with Fluoxetine

Abstract

Objective

Post-marketing surveillance (PMS) is an adverse events monitoring practice of pharmaceutical drugs on the market. Traditional PMS methods are labor intensive and expensive to perform, because they are largely based on manual work including phone-calling, mailing, or direct visits to relevant subjects. The objective of this study was to develop and validate a PMS methodology based on the clinical data warehouse (CDW).

Methods

We constructed a archival DB using a hospital information system and a refined CDW from three different hospitals. Fluoxetine hydrochloride, an antidepressant, was selected as the target monitoring drug. Corrected QT prolongation on ECG was selected as the target adverse outcome. The Wilcoxon signed rank test was performed to analyze the difference in the corrected QT interval before and after the target drug administration.

Results

A refined CDW was successfully constructed from three different hospitals. Table specifications and an entity-relation diagram were developed and are presented. A total of 13 subjects were selected for monitoring. There was no statistically significant difference in the QT interval before and after target drug administration (p=0.727).

Conclusion

The PMS method based on CDW was successfully performed on the target drug. This IT-based alternative surveillance method might be beneficial in the PMS environment of the future.

Figures and Tables

Figure 1
Process of making a specialized CDM for PMS
jksmi-15-191-g001
Figure 2
ER-Diagram for the integrated CDW
jksmi-15-191-g002
Table 1
Name of drugs with Fluoxetine and order code, examples based on A hospital
jksmi-15-191-i001
Table 2
The extraction of target ECG examination code, examples based on A hospital
jksmi-15-191-i002
Table 3
The important tables and fields for archival DB for PMS
jksmi-15-191-i003
Table 4
Distribution of major variables (N=145)
jksmi-15-191-i004
Table 5
Comparison of QTc between pre-post Fluoxetine administration (N=13)
jksmi-15-191-i005

*Wilcoxon signed rank test

Notes

This research was supported by a grant(07152KFDA625) from Korea Food & Drug Administration in 2007

References

1. Brewer T, Colditz GA. Postmarketing surveillance and adverse drug reactions: current perspectives and future needs. JAMA. 1999. 281(9):824–829.
crossref
2. Yang Q, Khoury MJ, James LM, Olney RS, Paulozzi LJ, Erickson JD. The return of thalidomide: are birth defects surveillance systems ready? Am J Med Genet. 1997. 73(3):251–258.
crossref
3. Swanson G, Ward A. Recruiting clinical trials: toward a participant-friendly system. J Natl Cancer Inst. 1995. 87(23):1747–1759.
4. Ahmad S. Adverse drug event monitoring at the food and drug administration. J Gen Intern Med. 2003. 18(1):57–60.
crossref
5. Waller PC, Coulson RA, Wood SM. Regulatory pharmacovigilance in the United Kingdom: current principles and practice. Pharmacoepidemiol Drug Saf. 1996. 5(6):363–375.
crossref
6. Weaver J, Bonnel RA, Karwoski CB, Brinker AD, Beitz J. GI events leading to death in association with celecoxib and rofecoxib. Am J Gastroenterol. 2001. 96(12):3449–3450.
crossref
7. Wysowski DK, Bacsanyi J. Cisapride and fatal arrhythmia. N Engl J Med. 1996. 335(4):290–291.
crossref
8. Wysowski DK, Corken A, Gallo-Torres H, Talarico L, Rodriguez EM. Postmarketing reports of QT prolongation and ventricular arrhythmia in association with cisapride and Food and Drug Administration regulatory actions. Am J Gastroenterol. 2001. 96(6):1698–1703.
crossref
9. Brown AM. Drugs, hERG and sudden death. Cell Calcium. 2004. 35(6):543–547.
crossref
10. Fermini B, Fossa AA. The impact of drug-induced QT interval prolongation on drug discovery and development. Nat Rev Drug Discov. 2003. 2(6):439–447.
crossref
11. Lasser KE, Allen PD, Woolhandler SJ, Himmelstein DU, Wolfe SM, Bor DH. Timing of new black box warnings and withdrawals for prescription medications. JAMA. 2002. 287(17):2215–2220.
crossref
12. Arfken CL, Cicero TJ. Postmarketing surveillance for drug abuse. Drug Alcohol Depend. 2003. 70:3 Suppl. S97–S105.
crossref
13. Guth BD, Germeyer S, Kolb W, Markert M. Developing a strategy for the nonclinical assessment of proarrhythmic risk of pharmaceuticals due to prolonged ventricular repolarization. Journal of Pharmacological and Toxicological Methods. 2004. 49(3):159–169.
crossref
14. Viskin S. Long QT syndromes and torsade de pointes. Lancet. 1999. 354(9190):1625–1633.
crossref
15. Simon SR, Kaushal R, Cleary PD, Jenter CA, Volk LA, Orav EJ, et al. Physicians and electronic health records: a statewide survey. Arch Intern Med. 2007. 167(5):507–512.
16. Menachemi N, Perkins RM, van Durme DJ, Brooks RG. Examining the adoption of electronic health records and personal digital assistants by family physicians in Florida. Inform Prim Care. 2006. 14(1):1–9.
crossref
17. Sittig F, Guappone K, Campbell E, Dykstra R, Ash J. A survey of USA acute care hospitals' computer-based provider order entry system infusion levels. Stud Health Technol Inform. 2007. 129(1):252.
18. Park RW, Shin SS, Choi YI, Ahn JO, Hwang SC. Computerized physician order entry and electronic medical record systems in Korean teaching and general hospitals: results of a 2004 survey. J Am Med Inform Assoc. 2005. 12(6):642–647.
crossref
19. DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al. Electronic health records in ambulatory care--a national survey of physicians. N Engl J Med. 2008. 359(1):50–60.
crossref
20. Dewitt JG, Hampton PM. Development of a data warehouse at an academic health system: knowing a place for the first time. Acad Med. 2005. 80(11):1019–1025.
crossref
21. Schubart JR, Einbinder JS. Evaluation of a data warehouse in an academic health sciences center. Int J Med Inform. 2000. 60(3):319–333.
crossref
22. Silver M, Sakata T, Su HC, Herman C, Dolins SB, O'Shea MJ. Case study: how to apply data mining techniques in a healthcare data warehouse. J Healthc Inf Manag. 2001. 15(2):155–164.
23. Zhang Q, Matsumura Y, Teratani T, Yoshimoto S, Mineno T, Nakagawa K, et al. The application of an institutional clinical data warehouse to the assessment of adverse drug reactions (ADRs). Evaluation of aminoglycoside and cephalosporin associated nephrotoxicity. Methods Inf Med. 2007. 46(5):516–522.
crossref
24. Hauben M, Patadia V, Gerrits C, Walsh L, Reich L. Data mining in pharmacovigilance: the need for a balanced perspective. Drug Saf. 2005. 28(10):835–842.
25. Waller PC, Evans SJ. A model for the future conduct of pharmacovigilance. Pharmacoepidemiol Drug Saf. 2003. 12(1):17–29.
crossref
26. Szirbik NB, Pelletier C, Chaussalet T. Six methodological steps to build medical data warehouses for research. Int J Med Inform. 2006. 75(9):683–691.
crossref
27. Hinrichsen VL, Kruskal B, O'Brien MA, Lieu TA, Platt R. Using electronic medical records to enhance detection and reporting of vaccine adverse events. J Am Med Inform Assoc. 2007. 14(6):731–735.
crossref
28. Sheen SS, Choi JE, Park RW, Kim EY, Lee YH, Kang UG. Overdose rate of drugs requiring renal dose adjustment: data analysis of 4 years prescriptions at a tertiary teaching hospital. J Gen Intern Med. 2008. 23(4):423–428.
crossref
TOOLS
Similar articles