Novel data-mining methodologies for adverse drug event discovery and analysis

Clinical Pharmacology and Therapeutics

Volumn 91, Issue 6, 2012, Pages 1010-1021

  Harpaz, R ^a   Dumouchel, W ^b,c   Shah, N H ^d   Madigan, D ^c,e   Ryan, P ^c,f   Friedman, C ^a  

^a NewYork Presbyterian Hospital   (United States)

^b Oracle Health Sciences   (United States)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

^d STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e Och Spine at New York Presbyterian Hospitals   (United States)

^f JANSSEN RESEARCH AND DEVELOPMENT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASE CONTROL STUDY; COHORT ANALYSIS; DATA MINING; DRUG SURVEILLANCE PROGRAM; HUMAN; MACHINE LEARNING; PATIENT SAFETY; PRIORITY JOURNAL; REVIEW;

ARTIFICIAL INTELLIGENCE; CASE-CONTROL STUDIES; COHORT STUDIES; DATA MINING; DATABASES, BIBLIOGRAPHIC; DATABASES, FACTUAL; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; MULTIVARIATE ANALYSIS; PHARMACOVIGILANCE; RESEARCH DESIGN;

EID: 84861346585     PISSN: 00099236     EISSN: 15326535     Source Type: Journal    
DOI: 10.1038/clpt.2012.50     Document Type: Review

References (75)

1. World Health Organization. The Importance of Pharmacovigilance-Safety Monitoring of Medicinal Products. World Health Organization: Geneva, 2002.
2. Lazarou, J., Pomeranz, B.H. & Corey, P.N. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279, 1200-1205 (1998). (Pubitemid 28193431)
3. Classen, D.C., Pestotnik, S.L., Evans, R.S., Lloyd, J.F. & Burke, J.P. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. JAMA 277, 301-306 (1997). (Pubitemid 27045010)
4. Ahmad, S.R. Adverse drug event monitoring at the Food and Drug Administration. J. Gen. Intern. Med. 18, 57-60 (2003).
5. Bate, A. & Evans, S.J. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol. Drug Saf. 18, 427-436 (2009).
6. Hauben, M., Madigan, D., Gerrits, C.M., Walsh, L. & Van Puijenbroek, E.P. The role of data mining in pharmacovigilance. Expert Opin. Drug Saf. 4, 929-948 (2005). (Pubitemid 41246142)
7. Hauben, M. & Bate, A. Decision support methods for the detection of adverse events in post-marketing data. Drug Discov. Today 14, 343-357 (2009).
8. Almenoff, J.S., Pattishall, E.N., Gibbs, T.G., DuMouchel, W., Evans, S.J. & Yuen, N. Novel statistical tools for monitoring the safety of marketed drugs. Clin. Pharmacol. Ther. 82, 157-166 (2007). (Pubitemid 47078877)
9. Platt, R., Wilson, M., Chan, K.A., Benner, J.S., Marchibroda, J. & McClellan, M. The new sentinel network-improving the evidence of medical-product safety. N. Eng. J. Med. 361, 645-647 (2009).
10. Schneeweiss, S. A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol. Drug Saf. 19, 858-868 (2010).
11. Wysowski, D.K. & Swartz, L. Adverse drug event surveillance and drug withdrawals in the United States, 1969-2002: the importance of reporting suspected reactions. Arch. Intern. Med. 165, 1363-1369 (2005). (Pubitemid 40973418)
12. Stephenson, W.P. & Hauben, M. Data mining for signals in spontaneous reporting databases: proceed with caution. Pharmacoepidemiol. Drug Saf. 16, 359-365 (2007). (Pubitemid 46656857)
13. Graham, D.J. et al. Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-oxygenase 2 selective and non-selective non-steroidal anti-inflammatory drugs: nested case-control study. Lancet 365, 475-481 (2005). (Pubitemid 40215729)
14. Avorn, J. & Schneeweiss, S. Managing drug-risk information-what to do with all those new numbers. N. Engl. J. Med. 361, 647-649 (2009).
15. Stang, P.E. et al. Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann. Intern. Med. 153, 600-606 (2010).
16. McClellan, M. Drug safety reform at the FDA-pendulum swing or systematic improvement? N. Engl. J. Med. 356, 1700-1702 (2007). (Pubitemid 46658699)
17. US Food and Drug Administration Amendments Act (FDAAA) of 2007 .http://www.fda.gov/RegulatoryInformation/Legislation/ federalfooddrugandcosmeticactfdcact/significantamendmentstothefdcact/ foodanddrugadministrationamendmentsactof2007/default.htm Accessed February 2012.
18. The Sentinel Initiative: a national strategy for monitoring medical product safety http://www.fda.gov/downloads/Safety/FDAsSentinelInitiative/ UCM124701.pdf. Accessed February 2012.
19. Observational Medical Outcomes Partnership (OMOP) . Accessed February 2012.
20. Coloma, P.M. et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol. Drug Saf. 20, 1-11 (2011).
21. Simpson, S.E. Self-controlled methods for postmarketing drug safety surveillance in large-scale longitudinal data. Ph.D. dissertation, Columbia University, 2011.
22. LePendu, P., Iyer, S.V., Fairon, C. & Shah, N.H. Annotation analysis for testing drug safety signals using unstructured clinical notes. J. Biol. Sem. 3 (suppl. 1), S5 (2012).
23. Brownstein, J.S., Sordo, M., Kohane, I.S. & Mandl, K.D. The tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarction. PLoS ONE 2, e840 (2007).
24. Brown, J.S. et al. Early detection of adverse drug events within populationbased health networks: application of sequential testing methods. Pharmacoepidemiol. Drug Saf. 16, 1275-1284 (2007). (Pubitemid 350303439)
25. Chapman, W.W., Nadkarni, P.M., Hirschman, L., D'Avolio, L.W., Savova, G.K. & Uzuner, O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J. Am. Med. Inform. Assoc. 18, 540-543 (2011).
26. Brookhart, M.A., Stmer, T., Glynn, R.J., Rassen, J. & Schneeweiss, S. Confounding control in healthcare database research: challenges and potential approaches. Med. Care 48, S114-S120 (2010).
27. Schneeweiss, S. & Avorn, J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J. Clin. Epidemiol. 58, 323-337 (2005). (Pubitemid 40601980)
28. Wishart, D.S. et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901-D906 (2008). (Pubitemid 351149842)
29. Chiang, A.P. & Butte, A.J. Data-driven methods to discover molecular determinants of serious adverse drug events. Clin. Pharmacol. Ther. 85, 259-268 (2009).
30. Pouliot, Y., Chiang, A.P. & Butte, A.J. Predicting adverse drug reactions using publicly available PubChem BioAssay data. Clin. Pharmacol. Ther. 90, 90-99 (2011).
31. Bender, A. & Glen, R.C. Molecular similarity: a key technique in molecular informatics. Org. Biomol. Chem. 2, 3204-3218 (2004).
32. Matthews, E.J. et al. Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities. Regul. Toxicol. Pharmacol. 54, 23-42 (2009).
33. Frid, A.A. & Matthews, E.J. Prediction of drug-related cardiac adverse effects in humans-B: use of QSAR programs for early detection of drug-induced cardiac toxicities. Regul. Toxicol. Pharmacol. 56, 276-289 (2010).
34. Deftereos, S.N., Andronis, C., Friedla, E.J., Persidis, A. & Persidis, A. Drug repurposing and adverse event prediction using high-throughput literature analysis. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 323-334 (2011).
35. Shetty, K.D. & Dalal, S.R. Using information mining of the medical literature to improve drug safety. J. Am. Med. Inform. Assoc. 18, 668-674 (2011).
36. Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J. & Gonzalez, G. Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts in health-related social networks. Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, 117-125 (2010).
37. Szarfman, A., Machado, S.G. & O'Neill, R.T. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database. Drug Saf. 25, 381-392 (2002). (Pubitemid 34729136)
38. Bate, A. et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clin. Pharmacol. 54, 315-321 (1998). (Pubitemid 28328016)
39. DuMouchel, W. Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System. Am. Stat. 53, 177-190 (1999).
40. Szarfman, A. Safety Data Mining. FDA Advisory Committee Meeting Briefing Document (2006) http://www.fda.gov/ohrms/dockets/ac/06/briefing/2006-4266b1-02- 06-FDA-appendic-f.pdf Accessed December 2011.
41. Deshpande, G., Gogolak, V. & Sheila, W.S. Data mining in drug safety: review of published threshold criteria for defining signals of disproportionate reporting. Pharmaceut. Med. 24, 37-43 (2010).
42. Ramirez, E. et al. A pharmacovigilance program from laboratory signals for the detection and reporting of serious adverse drug reactions in hospitalized patients. Clin. Pharmacol. Ther. 87, 74-86 (2010).
43. Almenoff, J.S., DuMouchel, W., Kindman, L.A., Yang, X. & Fram, D. Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post-marketing setting. Pharmacoepidemiol. Drug Saf. 12, 517-521 (2003). (Pubitemid 37069389)
44. J ewell, N.P. Statistics for Epidemiology (Chapman and Hall, Boca Raton, FL, 2003).
45. Genkin, A., Lewis, D.D. & Madigan, D. Large-scale Bayesian logistic regression for text categorization. Technometrics 49, 29-304 (2007).
46. Caster, O., Noren, G.N., Madigan, D. & Bate, A. Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database. Stat. Analy. Data Mining 3, 197-208 (2010).
47. Solomon, R. & DuMouchel, W. Contrast media and nephropathy: findings from systematic analysis and Food and Drug Administration reports of adverse effects. Invest. Radiol. 41, 651-660 (2006). (Pubitemid 44284174)
48. Agrawal, R., Imielinski, T. & Swami, A. Mining association rules between sets of items in large databases. SIGMOD, 207-216 (1993).
49. Rouane, H., Toussaint, Y. & Valtchev, P. Mining Signals in Spontaneous Reports Database Using Concept Analysis. AIME 2009 (Springer, Berlin, Germany, 2009).
50. Harpaz, R., Chase, H.S. & Friedman, C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics 11 (suppl. 9), S7 (2010).
51. Zorych, I., Madigan, D. Ryan, P. & Bate, A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res. 30 August 2011 (doi:10.1177/0962280211403602).
52. Fan, K. et al. High-performance signal detection for adverse drug events using MapReduce paradigm. AMIA Annu. Symp. Proc. 2010, 902-906 (2010).
53. Harpaz, R., Perez, H., Chase, H.S., Rabadan, R., Hripcsak, G. & Friedman, C. Biclustering of adverse drug events in the FDA's spontaneous reporting system. Clin. Pharmacol. Ther. 89, 243-250 (2011).
54. Ball, R. & Botsis, T. Can network analysis improve pattern recognition among adverse events following immunization reported to VAERS? Clin. Pharmacol. Ther. 90, 271-278 (2011).
55. Stang, P.E. et al. Health outcomes of interest in observational data: issues in identifying definitions in the literature. Health Outcomes Res. Med. 3, e37-e44 (2012).
56. Zorych, I., Madigan, D., Ryan, P. & Bate, A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res (in press).
57. Schuemie, M.J. Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD. Pharmacoepidemiol. Drug Saf. 20, 292-299 (2011).
58. Nor, G.N., Hopstadius, J., Bate, A. & Edwards, I.R. Safety surveillance of longitudinal databases: methodological considerations. Pharmacoepidemiol. Drug Saf. 20, 714-717 (2011).
59. D'Agostino, R.B. Jr & D'Agostino, R.B.Sr. Estimating treatment effects using observational data. JAMA 297, 314-316 (2007). (Pubitemid 46143312)
60. Patrick, A.R. et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiol. Drug Saf. 20, 551-559 (2011).
61. Schneeweiss, S., Rassen, J.A., Glynn, R.J., Avorn, J., Mogun, H. & Brookhart, M.A. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20, 512-522 (2009).
62. Bloom, M.S., Schisterman, E.F. & Hediger, M.L. The use and misuse of matching in case-control studies: the example of polycystic ovary syndrome. Fertil. Steril. 88, 707-710 (2007). (Pubitemid 47376698)
63. Madigan, D., Ryan, P., Simpson, S.E. & Zorych, I. Bayesian methods in pharmacovigilance (with discussion). In Bayesian Statistics 9 (eds. Bernardo, J.M. et al.) 421-438 (Oxford University Press, Oxford, UK, 2010).
64. Madigan, D. & Ryan, P. What can we really learn from observational studies?: the need for empirical assessment of methodology for active drug safety surveillance and comparative effectiveness research. Epidemiology 22, 629-631 (2011).
65. Gagne, J.J. et al. Design considerations in an active medical product safety monitoring system. Pharmacoepidemiol. Drug Saf. 21 (suppl. 1), 32-40 (2012).
66. Vilar, S., Harpaz, R., Chase, H.S., Costanzi, S., Rabadan, R. & Friedman, C. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis. J. Am. Med. Inform. Assoc. 18 (suppl. 1), i73-i80 (2011).
67. Wicks, P., Vaughan, T.E., Massagli, M.P. & Heywood, J. Accelerated clinical discovery using self-reported patient data collected online and a patientmatching algorithm. Nat. Biotechnol. 29, 411-414 (2011).
68. Cami, A., Arnold, A., Manzi, S. & Reis, B. Predicting adverse drug events using pharmacological network models. Sci. Transl. Med. 3, 114ra127 (2011).
69. Tatonetti, N.P. et al. Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels. Clin. Pharmacol. Ther. 90, 133-142 (2011).
70. Gibbons, R.D. et al. Post-approval drug safety surveillance. Annu. Rev. Public Health 31, 419-437 (2010).
71. Becquemont, L. Pharmacogenomics of adverse drug reactions: practical applications and perspectives. Pharmacogenomics 10, 961-969 (2009).
72. Pharmacogenomics Knowledge Base (PharmGKB) http://www.pharmgkb.org Accessed February 2012.
73. Hochberg, A.M. et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug Saf. 32, 509-525 (2009).
74. Musen, M.A. et al. The National Center for Biomedical Ontology. J. Am. Med. Inform. Assoc. 19, 190-195 (2012).
75. Berry, S.M. & Berry, D.A. Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biometrics 60, 418-426 (2004). (Pubitemid 38780201)