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Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 1 Data Mining Analysis of Multiple Antibiotics in AERS Jonathan G. Levine, PhD Mathematical.

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Presentation on theme: "Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 1 Data Mining Analysis of Multiple Antibiotics in AERS Jonathan G. Levine, PhD Mathematical."— Presentation transcript:

1 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 1 Data Mining Analysis of Multiple Antibiotics in AERS Jonathan G. Levine, PhD Mathematical Statistician Office of Critical Path Programs Office of the Commissioner FDA and Ana Szarfman, MD, PhD Medical Officer Division of Cardiovascular and Renal Products Office of New Drugs and Division of Biometrics VI, Office of Biostatistics Office of Translational Sciences CDER, FDA Jonathan G. Levine, PhD Mathematical Statistician Office of Critical Path Programs Office of the Commissioner FDA and Ana Szarfman, MD, PhD Medical Officer Division of Cardiovascular and Renal Products Office of New Drugs and Division of Biometrics VI, Office of Biostatistics Office of Translational Sciences CDER, FDA

2 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 2 What is data mining? In general: Statistical analysis applied to large databases without any a priori hypotheses. In this case: Using the MGPS algorithm to analyze all suspect drug and adverse event pairs in the AERS database. I will briefly discuss AERS and MGPS; details are in the review contained in the briefing package. In general: Statistical analysis applied to large databases without any a priori hypotheses. In this case: Using the MGPS algorithm to analyze all suspect drug and adverse event pairs in the AERS database. I will briefly discuss AERS and MGPS; details are in the review contained in the briefing package.

3 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 3 What is the AERS Database Computerized adverse case reporting system –Voluntary reporting by health care workers and the general public. –Mandatory reporting by manufacturers for serious, unexpected events Adverse event reports –Coded according to the standardized terminology of the Medical Dictionary for Regulatory Activities (MedDRA) –Over 3 million reports from 1968 to the present. –Small number of data elements (drugs, events, age, sex, etc.) –Lots of missing data Computerized adverse case reporting system –Voluntary reporting by health care workers and the general public. –Mandatory reporting by manufacturers for serious, unexpected events Adverse event reports –Coded according to the standardized terminology of the Medical Dictionary for Regulatory Activities (MedDRA) –Over 3 million reports from 1968 to the present. –Small number of data elements (drugs, events, age, sex, etc.) –Lots of missing data

4 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 4 Disproportionality Analysis Using DuMouchel’s MGPS Method Calculate observed and expected number of reports for a particular drug-event combination. Observed rate = Number of reports for event X with drug Y Number of reports for drug Y Expected rate = Number of reports for event X in AERS Number of reports in AERS Reporting Ratio (RR)= Observed rate Expected rate “Shrink” the RR towards 1. The shrunk RR is referred to as the EBGM score. The amount of shrinkage is a function of the amount of information in AERS about the drug-event combination. Calculate observed and expected number of reports for a particular drug-event combination. Observed rate = Number of reports for event X with drug Y Number of reports for drug Y Expected rate = Number of reports for event X in AERS Number of reports in AERS Reporting Ratio (RR)= Observed rate Expected rate “Shrink” the RR towards 1. The shrunk RR is referred to as the EBGM score. The amount of shrinkage is a function of the amount of information in AERS about the drug-event combination.

5 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 5 Why Shrink RRs? Expected counts are often so small that a single report will yield a huge RR –Example: Acetaminophen has one report for “Alice in wonderland syndrome” –The expected number of cases is approximately 0.011 –RR= 89.4 –EBGM = 1.37 –Shrinking dramatically reduces the false positive rate Expected counts are often so small that a single report will yield a huge RR –Example: Acetaminophen has one report for “Alice in wonderland syndrome” –The expected number of cases is approximately 0.011 –RR= 89.4 –EBGM = 1.37 –Shrinking dramatically reduces the false positive rate

6 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 6 Drug-Event Combinations Analyzed Drugs Selected by Division of Antiinfective and Ophthalmic Products We considered the results for all adverse events in the AERs database. Only adverse events with at least one of the selected drugs having EBGM >=2 and an N>=2 for at least one cumulative time period were analyzed in detail. Removed adverse events most likely related to the indications being treated (e.g., pneumonia, meningitis, otitis, pain). Selected the event codes that reflected a more severe problem (e.g., we selected “Hepatic failure” instead of “Aspartate Aminotransferase Increased”, “Toxic epidermal necrolysis” instead of “Rash”). Drugs Selected by Division of Antiinfective and Ophthalmic Products We considered the results for all adverse events in the AERs database. Only adverse events with at least one of the selected drugs having EBGM >=2 and an N>=2 for at least one cumulative time period were analyzed in detail. Removed adverse events most likely related to the indications being treated (e.g., pneumonia, meningitis, otitis, pain). Selected the event codes that reflected a more severe problem (e.g., we selected “Hepatic failure” instead of “Aspartate Aminotransferase Increased”, “Toxic epidermal necrolysis” instead of “Rash”).

7 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 7 Ranking by Max EBGM for Recoded Events This data reduction left us with 168 adverse events terms and 6 serious outcomes for the 16 drugs, a total of 2,784 possible EBGM values. How to present this many estimates? We chose to reduce the dimensionality of the problem by: –Grouping similar Adverse Events –Looking at maximum EBGM value over both adverse event group and cumulative year subset. This data reduction left us with 168 adverse events terms and 6 serious outcomes for the 16 drugs, a total of 2,784 possible EBGM values. How to present this many estimates? We chose to reduce the dimensionality of the problem by: –Grouping similar Adverse Events –Looking at maximum EBGM value over both adverse event group and cumulative year subset.

8 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 8 ResultsResults Insufficient time to discuss all results Only summary conclusions for 11 selected adverse event groups will be provided in this presentation. Details are presented in the review provided in the briefing package. Insufficient time to discuss all results Only summary conclusions for 11 selected adverse event groups will be provided in this presentation. Details are presented in the review provided in the briefing package.

9 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 9 EBGMs for Selected Drug-Event Combinations

10 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 10 16 Drugs Selected by DAIOP

11 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 11 Selected Serious Event Groups

12 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 12 Color Coding of EBGM Scores

13 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 13 Eye Events and Myasthenia

14 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 14 SyncopeSyncope

15 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 15 Hepatic Failure and Hepatitis

16 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 16 CholestasisCholestasis

17 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 17 Drug Interaction and Drug Ineffective

18 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 18 Clostridial Infection

19 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 19 Toxic Skin Reactions

20 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 20 Hypersensitivity Reactions

21 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 21 ConclusionsConclusions There is an unusually large signal for eye events with telithromyicin There is an unusually large signal for myasthenia with telithromyicin The large signal for telithromyicin and syncope is second only to the signal for moxifloxacin There is an unusually large signal for eye events with telithromyicin There is an unusually large signal for myasthenia with telithromyicin The large signal for telithromyicin and syncope is second only to the signal for moxifloxacin

22 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 22 ConclusionsConclusions “Hepatic Failure” and “Hepatitis” both have signals with telithromycin –Hepatic Failure has a signal less than trovafloxacin and nitrofurantoin, but comparable to amoxicillin and clavulanate –Hepatitis has a signal comparable to trovafloxacin, nitrofurantoin, and amoxicillin and clavulanate “Cholestasis” has a weak signal with telithromycin. –The majority of antibiotics have a stronger signal for cholestasis “Hepatic Failure” and “Hepatitis” both have signals with telithromycin –Hepatic Failure has a signal less than trovafloxacin and nitrofurantoin, but comparable to amoxicillin and clavulanate –Hepatitis has a signal comparable to trovafloxacin, nitrofurantoin, and amoxicillin and clavulanate “Cholestasis” has a weak signal with telithromycin. –The majority of antibiotics have a stronger signal for cholestasis

23 Anti-Infective Drugs Advisory Committee Meeting December 15, 2006 23 ConclusionsConclusions “Drug Interaction” has a high signal score with telithromycin, –Azithromycin, clarithromycin, and erythromycin all have higher signal scores than telithromycin. “Toxic Skin” and “Hypersensitivity Reaction” have weak signals for telithromycin compared to the majority of other antibiotics. “Drug Ineffective” and “Clostridial Infection” do not have signals for telithromycin “Drug Interaction” has a high signal score with telithromycin, –Azithromycin, clarithromycin, and erythromycin all have higher signal scores than telithromycin. “Toxic Skin” and “Hypersensitivity Reaction” have weak signals for telithromycin compared to the majority of other antibiotics. “Drug Ineffective” and “Clostridial Infection” do not have signals for telithromycin

24 24 Return: Anti-Infective Drugs Advisory Committee in Joint Session with the Drug Safety and Risk Management Advisory Committee. December 14 & 15, 2006 Return to meeting agenda.

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