1. Using Information Technology to Detect Ambulatory Adverse Events Related to Antidiabetic Drug Therapy Judy Wu, PharmD Duke University Hospital Co-Investigators: Heidi Cozart, RPh; Julie Whitehurst, PharmD; Philip Rodgers, PharmD; Jennifer Mando, PharmD

2. Adverse Drug Events (ADEs) Research primarily in the inpatient setting 3 – 6 ADEs per 100 admissions 1-3 27% – 50% of ADEs are preventable 1-3 Estimated cost: $ 3.5 billion (2006 dollars) 4 ADE detection methods Chart review, patient surveys, computer event monitoring, text scanning, voluntary reporting Multiple methods = more ADEs Not well understood in other care settings 1.Bates DW et al. JAMA 1995;274(1):29-34. 2.Classen DC et al. JAMA. 1997;277(4):301-6. 3.Jha AK et al. J Am Med Inform Assoc 1998;5(3):305-14. 4.Aspden P, IOM (U.S.). Preventing medication errors. Washington, DC: National Academies Press, 2007.

3. “Most data on medication error incidence rates come from the inpatient setting, but - Institute of Medicine the magnitude of the problem is likely to be greater outside the hospital.” Aspden P, IOM (U.S.). Preventing medication errors. Washington, DC: National Academies Press, 2007.

4. MedicationErrors Adverse Drug Events(ADEs) Definitions Gandhi TK et al. International Journal for Quality in Health Care. 2000; 12:69–76. Patient injury resulting from medical intervention related to a drug Bates DW et al. JAMA. 1995; 274:29–34. Patient injury resulting from medical intervention related to a drug Bates DW et al. JAMA. 1995; 274:29–34. Any error in any stage of the medication use process (ordering, transcribing, dispensing, administering, or monitoring) Bates DW et al. J Gen Intern Med 1995;10: 199-205. Any error in any stage of the medication use process (ordering, transcribing, dispensing, administering, or monitoring) Bates DW et al. J Gen Intern Med 1995;10: 199-205.

5. Scope of the Problem Limited research in ambulatory care 1,2 Baseline ADE incidence rate Identify strategies to decrease ADEs Barriers to ambulatory care ADE research Inefficient Lack of accessible data Large patient population Most common medications resulting in ED visits Insulin and warfarin 3,4 1.Thomsen LA et al. Ann Pharmacother 2007;41(9):1411-26. 2.Field T et al. Med Care 2005; 43: 1171-1176. 3.Hafner J et al. Annals of Emergency Medicine. 2002; 30: 258-267. 4.Budnitz DS et al. JAMA 2006;296(15):1858-66.

6. Research Objectives Quantify hypoglycemia ambulatory ADEs resulting in emergency department visits or hospitalization Quantify hypoglycemia ambulatory ADEs resulting in emergency department visits or hospitalization Characterize the population of subjects experiencing ADEs Characterize the population of subjects experiencing ADEs Evaluate the utility of three different electronic adverse event detection methods Evaluate the utility of three different electronic adverse event detection methods Design a catalog of trigger words to detect possible ADEs through free-text searching Design a catalog of trigger words to detect possible ADEs through free-text searching

7. Study Design Retrospective, electronic chart review Approved by Duke University Institutional Review Board Study site: Duke University Hospital Study period: January 1, 2007 to September 30, 2007 Inclusion criteria Subjects >18 years old experiencing possible antidiabetic drug-induced hypoglycemia resulting in an emergency department visit or hospitalization Exclusion criteria Subjects experiencing hypoglycemia not associated with medication use Lack of objective evidence

8. Hypoglycemic ADE Blood glucose < 50 mg/dL while on antidiabetic therapy ADE scoring ADE = causality score ≥ 5 and a severity score ≥ 3 Causality - Naranjo algorithm 1 Severity - Duke 7 point ADE severity score 2 ADE group Comprehensive list of ADEs detected from any of the 3 tools Measurements 1.Naranjo CA et al. Clin Pharmacol Ther. 1981;30:239-245. 2.Kilbridge PM et al. J Am Med Inform Assoc 2006; 13: 372-377.

9. Detection Methods Computerized ADE Surveillance (ADE-S) Computerized ADE Surveillance (ADE-S) Diagnosis (ICD-9) codes Diagnosis (ICD-9) codes Free-text searching Free-text searching

10. Detection Methods: Detection Methods: Computerized ADE Surveillance Logic based rules Screens demographic and laboratory data, medications, and other clinical results Alerts pharmacist about possible ADEs Review and scoring process Acute care setting vs emergency department Hypoglycemia rule Dextrose 50% when BG < 50 mg/dL

11. Detection Methods: Detection Methods: Diagnosis (ICD-9) codes Administrative data Administrative data International Classification of Diseases, 9 th edition International Classification of Diseases, 9 th edition Codes for diagnoses and procedures Codes for diagnoses and procedures E900 codes specific to adverse events due to drugs E900 codes specific to adverse events due to drugs E932.3 Adverse effect insulin/antidiabetics E932.3 Adverse effect insulin/antidiabetics

12. Detection Methods: Detection Methods: Free-text searching Electronic medical records Emergency department visits Refinement of searching tool Identification of trigger words Elimination of negative and ambiguous terms Final search strategy Include {DM or diabetes} AND {hypoglycemia or hypoglycemic or low blood glucose or low BG or low glucose} AND exclude {(-)DM}

13. Results: Hypoglycemia Alerts Detected ComputerSurveillance Free-text search ICD-9 ICD-9 n = 138 n = 72 n = 212 # of unique alerts = 364 8 6 26 12 112 168 32

14. Results: Hypoglycemia ADEs Detected Free-text search ICD-9 ICD-9 ComputerSurveillance ADEs = 154 (42%) 91 55 57 6

15. ADE Population Characteristics Number of events154 Age in years (mean ± SD) 59 ± 16.6 Gender Male (%)45 Number of comorbidities (mean ± SD) 6.8 ± 3.7 Number of medications (mean ± SD) Antidiabetic Total 1.7 ± 0.6 9.8 ± 5.0 Hospitalization (%)49

16. ADE Distribution By Race n = 154

17. ADE Distribution By Age n = 154 Age (Years) Number of Events

18. ADEs With Insulin Involvement n = 154 Mean blood glucose value at time of hypoglycemic event: 32 mg/dL Mean blood glucose value at time of hypoglycemic event: 32 mg/dL Insulin + Sulfonylurea4.5%

19. Positive Predictive Value (PPV) of ADE Detection Tools Free-text search ICD-9 ICD-9 ComputerSurveillance 43% 40% 79% Overestimation 100% 91 ADEs 55 ADEs 57 ADEs

20. Sensitivity of ADE Detection Tools n = 154

21. Limitations Retrospective, chart review Retrospective, chart review Not generalizable to other ambulatory ADEs Not generalizable to other ambulatory ADEs Subjectivity in scoring ADEs Subjectivity in scoring ADEs Underestimation of hypoglycemic incidence rate Underestimation of hypoglycemic incidence rate Specific population Specific population Exclusion of symptomatic hypoglycemia with BG > 50 Exclusion of symptomatic hypoglycemia with BG > 50 Undetected hypoglycemic ADEs? Undetected hypoglycemic ADEs? Detection tool limitations Detection tool limitations ADE-S, ICD-9, free-text search ADE-S, ICD-9, free-text search

22. Conclusion 17 hypoglycemia ADEs per month were detected 17 hypoglycemia ADEs per month were detected 49% require hospitalization 49% require hospitalization 71% of ADEs involved insulin use 71% of ADEs involved insulin use African American and older age present more frequently with hypoglycemia ADEs African American and older age present more frequently with hypoglycemia ADEs Highest yield & sensitivity  free text search tool Highest yield & sensitivity  free text search tool Greatest PPV  ICD-9 coding Greatest PPV  ICD-9 coding Minimal overlap among tools Minimal overlap among tools Combining methods increases ADE yield Combining methods increases ADE yield

23. “The primary focus of research on medication errors in the next decade should be prevention strategies, recognizing that to plan an error prevention study, it is essential to be able to measure the baseline rate of errors.” - Institute of Medicine Future research: Expand into other populations and other ambulatory ADE areas Expand into other populations and other ambulatory ADE areas Tool refinement Tool refinement Use of detection methods in outpatient clinics Use of detection methods in outpatient clinics Prevention strategies Prevention strategies

24. Acknowledgements Heidi Cozart Heidi Cozart Julie Whitehurst Julie Whitehurst DHTS DHTS Department of Pharmacy Department of Pharmacy Residency Research Committee Residency Research Committee

25. Questions

26. Race Distribution http://quickfacts.census.gov/qfd/states/37/37063.html North Carolina Durham County Duke

27. Race Distribution http://quickfacts.census.gov/qfd/states/37/37063.html Predicted Actual