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Page 1: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

Kia Ora!

Page 2: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

TanzaniaTanzaniaNovember 2009November 2009

Cohort Event MonitoringCohort Event MonitoringPrescription event monitoring (PEM)Prescription event monitoring (PEM)

Dr. David CoulterDr. David Coulterformerly Research Associate Professorformerly Research Associate Professor

Intensive Medicines Monitoring ProgrammeIntensive Medicines Monitoring Programme& Head NZ Pharmacovigilance Centre& Head NZ Pharmacovigilance Centre

Dr. Geraldine HillDr. Geraldine HillTeaching Fellow, University of Otago Medical School, Teaching Fellow, University of Otago Medical School,

Dunedin, New ZealandDunedin, New Zealand(formerly Research Fellow(formerly Research Fellow

Intensive Medicines Monitoring Programme)Intensive Medicines Monitoring Programme)

Page 3: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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CEM worldwideCEM worldwide

NZ Intensive Medicines Monitoring NZ Intensive Medicines Monitoring Programme (IMMP), NZ, 1977Programme (IMMP), NZ, 1977

Drug Safety Research Unit (PEM), Drug Safety Research Unit (PEM), Southampton, UK, 1980Southampton, UK, 1980

Tanzania Tanzania CEM of anti-malarials CEM of anti-malarials

NigeriaNigeria

Page 4: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Plan of presentationPlan of presentation

ObjectivesObjectives

What results can you get?What results can you get?Examples and methods from the NZ Intensive Medicines Examples and methods from the NZ Intensive Medicines

Monitoring Programme (IMMP)Monitoring Programme (IMMP)

How do we get them?How do we get them?

Observations & commentsObservations & comments

Page 5: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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The objectives of CEMThe objectives of CEM

1.1. Characterise known reactionsCharacterise known reactions

– Mean ageMean age– GenderGender– Mean doseMean dose– Treatment durationTreatment duration– Time to onsetTime to onset– Seriousness profileSeriousness profile– IncidenceIncidence– OutcomesOutcomes– Effect on treatment (% withdrawals)Effect on treatment (% withdrawals)– Part of syndrome?Part of syndrome?

Page 6: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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The objectives of CEMThe objectives of CEM

2.2. Detect signals of unrecognised reactionsDetect signals of unrecognised reactions

3.3. Interactions withInteractions withOther medicinesOther medicinesComplementary and alternative medicinesComplementary and alternative medicinesFoodsFoods

4.4. Identify risk factors so that they can be avoidedIdentify risk factors so that they can be avoided

AgeAge Duration of therapyDuration of therapyGenderGender Concomitant diseaseConcomitant diseaseDoseDose Concomitant therapyConcomitant therapy

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The objectives of CEMThe objectives of CEM

5.5. Assess safety in pregnancy & lactationAssess safety in pregnancy & lactation

6.6. Estimate risk (including comparative)Estimate risk (including comparative)

7.7. Provide evidence for effective risk Provide evidence for effective risk managementmanagement

Safer prescribingSafer prescribing

Benefit / harm assessmentBenefit / harm assessment

Regulatory changesRegulatory changes

Page 8: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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The objectives of CEMThe objectives of CEM

8.8. Detect inefficacy, which might be due toDetect inefficacy, which might be due to

Faulty administrationFaulty administration

Poor storage conditionsPoor storage conditions

Out of dateOut of date

Poor quality productPoor quality product

Counterfeit Counterfeit

InteractionsInteractions

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The objectives of CEMThe objectives of CEM

9.9. Hypothesis generationHypothesis generation

10.10. Cohorts for studyCohorts for study

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The objectivesThe objectives

Achieve Achieve maximum maximum benefitbenefit, , least harmleast harm for for

patientspatients

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What results can you get?What results can you get?

Page 12: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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COX-2 inhibitorsCOX-2 inhibitorscelecoxib, rofecoxibcelecoxib, rofecoxib

Preliminary monitoring Preliminary monitoring datadata

Page 13: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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The following will be The following will be summarisedsummarised

Cohort description & drug utilisationCohort description & drug utilisation

Preliminary events dataPreliminary events data

Preliminary review of deathsPreliminary review of deaths

Page 14: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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IMMP ProcessIMMP Process

Prescription

Patient and Prescription details

Follow-up questionnairesEvent information

Cohort data Relationship

assessment

NZHIS

Page 15: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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CohortCohort

Prescriptions Patients

Celecoxib 98,975 32,630

Rofecoxib 52,874 26,666

Page 16: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

1616

Profile of Ages at First Prescription

213543

1477

3325

5827 5969

6552

3601

503468

945

1939

3426

4704

4254 4297

2289

280

0

5

10

15

20

25

< 20 20-30 30-39 40-49 50-59 60-69 70-79 80-89 90 plus

% o

f tot

al k

now

n ag

es

Celecoxib Rofecoxib

Page 17: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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IMMP example –COX-2IMMP example –COX-2

AgeAge CelecoxibCelecoxib RofecoxibRofecoxib

MeanMean 6363 5858

ModeMode 5959 5353

<40 years<40 years 6.9%6.9% 12.6%12.6%Highly significantHighly significant

70+ years70+ years 32.7%32.7% 25.7%25.7%Highly significantHighly significant

Page 18: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Gender and termGender and term

CelecoxibCelecoxib RofecoxibRofecoxib

WomenWomen 61.6%61.6% 60.5%60.5%

Short termShort term 6879 (21%)6879 (21%) 9843 (37%)9843 (37%)

Page 19: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Rofecoxib doseRofecoxib dose

12.5 11,695 28.3

25 26,027 63.0

37.5 36 0.1

50 3,546 8.6

mg/day No. %

Page 20: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

2020

Celecoxib dose mg/no./%Celecoxib dose mg/no./%

100 6,622 8.1 200 65,591 80.5 300 274 0.3 400 8,927 11.0 600 46 800 30

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IMMP ProcessIMMP Process

Prescription

Patient and Prescription details

Follow-up questionnairesEvent information

Cohort data Relationship

assessment

NZHIS

Page 22: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Indications for useIndications for use((0r type/seriousness of malaria0r type/seriousness of malaria))

CelecoxibCelecoxib

No. %No. %RofecoxibRofecoxib

No. %No. %DifferenceDifference

Chi-squareChi-square

PatientsPatients 6,2006,200 4,5364,536

InflammatoryInflammatory 211 (3.4)211 (3.4) 129 (2.8)129 (2.8) P>0.05P>0.05

OsteoarthritisOsteoarthritis 1805 (29)1805 (29) 775 (17)775 (17) P<0.0001P<0.0001

MusculoskelMusculoskel 1668 (27)1668 (27) 1105 (24)1105 (24) P<0.01P<0.01

Other painOther pain 2479 (40)2479 (40) 2495 (55)2495 (55) P<0.0001P<0.0001

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Baseline information 1Baseline information 1

QuestionsQuestions1.1. Current Acid Related DisorderCurrent Acid Related Disorder

2.2. Past ARDPast ARD

3.3. NSAID exposure NSAID exposure • Past GI problems Past GI problems • Direct switch to COX-2Direct switch to COX-2• Concurrent aspirinConcurrent aspirin

4.4. Past cardiovascular diseasePast cardiovascular disease• Hypertension / Heart failureHypertension / Heart failure• MI / AnginaMI / Angina• Dysrhythmia / Stroke - TIADysrhythmia / Stroke - TIA

Page 24: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Baseline information 2Baseline information 2

Questionnaire response rateQuestionnaire response rate CelecoxibCelecoxib: number sent 4635 : number sent 4635

No. returned with information 3985 No. returned with information 3985 (91%)(91%)

RofecoxibRofecoxib: number sent 3050 : number sent 3050 No. returned with information 2725 No. returned with information 2725

(89%)(89%)

Page 25: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Baseline information 3Baseline information 3No. & % of positive responses to questionNo. & % of positive responses to question

CELCEL ROFROF Rate ratioRate ratio

(95% CI)(95% CI)

ARDARD 2281 2281 (68%)(68%)

1341 1341 (60%)(60%)

1.4 1.4

(1.27-1.58)(1.27-1.58)

NSAID/ARDNSAID/ARD 2136 2136 (62%)(62%)

1199 1199 (54%)(54%)

1.41.4

(1.28-1.59)(1.28-1.59)

SwitchSwitch 1345 1345 (36%)(36%)

824 824

(34%)(34%)

1.91.9

(0.98-1.21)(0.98-1.21)

AspirinAspirin 352 352 (9.3%)(9.3%)

173 (6.9%)173 (6.9%) 1.41.4

(1.15-1.69)(1.15-1.69)

CardiovascCardiovasc 1361 1361 (36%)(36%)

797 797

(31%)(31%)

1.21.2

(1.11-1.38)(1.11-1.38)

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Baseline information 4Baseline information 4Cardiovascular diseaseCardiovascular disease

CelecoxibCelecoxib RofecoxibRofecoxib Rate ratioRate ratio

(95% CI)(95% CI)

HypertensionHypertension 843 (22%)843 (22%) 498 (19%)498 (19%) 1.11.1

(1.04-1.26)(1.04-1.26)

MI/anginaMI/angina 547 (14%)547 (14%) 298 (12%)298 (12%) 1.21.2

(1.09-1.42)(1.09-1.42)

HFHF 206 (5.4%)206 (5.4%) 115 (4.5%)115 (4.5%) 1.21.2

(0.97-1.51)(0.97-1.51)

DysrhythmiaDysrhythmia 141 (3.7%)141 (3.7%) 86 (3.3%)86 (3.3%) 1.11.1

(0.85-1.44)(0.85-1.44)

Stroke/TIAStroke/TIA 40 (1.0%)40 (1.0%) 17 (0.7%)17 (0.7%) 1.61.6

(0.90-2.80)(0.90-2.80)

Page 27: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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The eventsThe events

Page 28: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Profile of Events - Celecoxib and Rofecoxib n=1714 n=982

64

2222

156

103

78

58

12

3532

8

44

1932

293301

22

273

33

40

5051

39

28

12

2121

912

28

123

17

179

5

198

181

13

0

5

10

15

20

25

System Organ Class

Per

centa

ge

of T

ota

l Eve

nts

Celecoxib Rofecoxib

Page 29: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Most common events 1Most common events 1rates /1000 patientsrates /1000 patients

CelecoxibCelecoxib RofecoxibRofecoxib

EventEvent No.No. RateRate No. No. RateRate RRRR

ARDARD 129129 3.43.4 8989 3.33.3 NSNS

RashRash 8686 2.62.6 3030 1.11.1 2.3 (1.6-3.6)2.3 (1.6-3.6)

HFHF 7474 2.32.3 5555 2.12.1 NSNS

IHDIHD 5757 1.81.8 3838 1.41.4 NSNS

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Most common events 2Most common events 2

CelecoxibCelecoxib RofecoxibRofecoxib

EventEvent No.No. RateRate No. No. RateRate RRRR

LRTILRTI 5656 1.71.7 2929 1.11.1 1.6 1.6 (1.0-2.5)(1.0-2.5)

DysrhythmiasDysrhythmias 4949 1.51.5 1919 0.70.7 2.1 2.1 (1.2-3.6)(1.2-3.6)

AngioedemaAngioedema 4848 1.51.5 1414 0.50.5 2.8 2.8 (1.6-5.1)(1.6-5.1)

StrokeStroke 3737 1.11.1 1818 0.70.7 NSNS

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Most common events 3Most common events 3CelecoxibCelecoxib RofecoxibRofecoxib

EventEvent NoNo RateRate No. No. RateRate RRRR

DiarrhoeaDiarrhoea 3636 1.11.1 1717 0.60.6 NSNS

AsthmaAsthma 3434 1.01.0 1313 0.50.5 2.1 (1.1-4.1)2.1 (1.1-4.1)

RFRF 3333 1.01.0 2828 1.11.1 NSNS

VomitingVomiting 3333 1.01.0 3434 1.31.3 NSNS

HTHT 1313 0.30.3 2828 1.11.1 2.6 (1.4-5.0)2.6 (1.4-5.0)

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Signals identified 1Signals identified 1

CoughingCoughing

Visual field defect / temp blindnessVisual field defect / temp blindness

Acute psychiatric eventsAcute psychiatric events

PancreatitisPancreatitis

HepatotoxicityHepatotoxicity

PsoriasisPsoriasis

Acute urinary retentionAcute urinary retention

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Signals 2Signals 2

Mouth ulcerationMouth ulceration

Lower bowel effectsLower bowel effects

Cardiac dysrhythmiasCardiac dysrhythmias

Cardiac arrestCardiac arrest

Myocardial infarction / strokeMyocardial infarction / stroke

AnaphylaxisAnaphylaxis

Serious skin infectionSerious skin infection

Acute labyrinthitisAcute labyrinthitis

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Signals 3Signals 3

InteractionsInteractions

Tricyclics causing arrhythmiasTricyclics causing arrhythmias

Warfarin causing increased INR Warfarin causing increased INR (rofecoxib)(rofecoxib)

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DeathsDeathsCauses by SOC Causes by SOC (% of total deaths)(% of total deaths)

CelecoxibCelecoxib No. (%)No. (%) RofecoxibRofecoxib No. (%)No. (%)

All deathsAll deaths CausalCausal All deathsAll deaths CausalCausal

CirculatoryCirculatory 116 (40)116 (40) 34 34 (11.6)(11.6) 68 (38)68 (38) 23 23 (12.9)(12.9)

MalignancyMalignancy 115 (39)115 (39) NilNil 92 (51)92 (51) NilNil

RespiratoryRespiratory 59 (20)59 (20) NilNil 24 (13)24 (13) NilNil

RenalRenal 23 (8)23 (8) 18 18 (6)(6) 8 (5)8 (5) 8 8 (5)(5)

InfectionInfection 13 (4)13 (4) NilNil 11 (6)11 (6) NilNil

AlimentaryAlimentary 10 (3)10 (3) 10 10 (3)(3) 8 (5)8 (5) 4 4 (2)(2)

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Risk factors 1Risk factors 1

by multiple logistic regressionby multiple logistic regression

Renal failureRenal failure– AgeAge– Inflammatory arthritisInflammatory arthritis

Heart failureHeart failure– AgeAge– P/H heart failureP/H heart failure– Inflammatory arthritisInflammatory arthritis

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Risk factors 2Risk factors 2

Ischaemic heart diseaseIschaemic heart disease– AgeAge– P/H of any type of heart diseaseP/H of any type of heart disease– Inflammatory arthritis (celecoxib)Inflammatory arthritis (celecoxib)

Cardiac dysrhythmiasCardiac dysrhythmias– AgeAge– Past history of heart failurePast history of heart failure– Inflammatory arthritis (celecoxib)Inflammatory arthritis (celecoxib)

Page 38: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Risk factors 3Risk factors 3

Stroke / TIAStroke / TIA– AgeAge– HypertensionHypertension– Inflammatory arthritisInflammatory arthritis

Page 39: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Did we reach the Did we reach the objectives?objectives?

Page 40: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Study demonstratesStudy demonstrates

High complianceHigh complianceDemographics of cohortsDemographics of cohortsBackground dataBackground data– IndicationIndication– Relevant past/current historyRelevant past/current history

Prescribing practicesPrescribing practicesEarly signal identificationEarly signal identificationSignificant eventsSignificant eventsComparative ratesComparative ratesRisk factorsRisk factors

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Concerns raisedConcerns raised

High volume of prescribingHigh volume of prescribingHigh doses of rofecoxibHigh doses of rofecoxibSubstantial prescribing to patients at Substantial prescribing to patients at high riskhigh risk– very elderlyvery elderly– history of cardiovascular diseasehistory of cardiovascular disease– history of ARDhistory of ARD

Apparent high death rateApparent high death rate

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ConcernsConcerns

High rate of cardiovascular eventsHigh rate of cardiovascular events– Heart failureHeart failure– DysrhythmiasDysrhythmias– Prothrombotic effectsProthrombotic effects

Myocardial infarctionMyocardial infarction

StrokeStroke

Renal infarctionRenal infarction

High rate of alimentary eventsHigh rate of alimentary events

Page 43: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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How do we get results like How do we get results like this?this?

The principlesThe principles

Page 44: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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Cohort event monitoringCohort event monitoringHow is it done?How is it done?

Two PrinciplesTwo PrinciplesIdentifying patients exposed (cohort) Identifying patients exposed (cohort) - the - the denominatordenominator– as complete as possibleas complete as possible

Systematically soliciting adverse Systematically soliciting adverse EVENTSEVENTS - the - the numeratornumerator– as complete as possibleas complete as possible

Page 45: Kia Ora!. November 2009 Tanzania Cohort Event Monitoring Prescription event monitoring (PEM) Dr. David Coulter formerly Research Associate Professor Intensive.

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1. Identifying the patients1. Identifying the patients

How can this be done?How can this be done?The cohort of patients is established using The cohort of patients is established using the best source of usage data availablethe best source of usage data available– Dispensings (pharmacies or central records)Dispensings (pharmacies or central records)– Patient recordsPatient records

DoctorsDoctorsClinicsClinicsHospitalsHospitalsOtherOther

– Programme recordsProgramme records

Adequate cohort (10,000 patients)Adequate cohort (10,000 patients)

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IMMP ProcessIMMP Process

Prescription

Patient and Prescription details

Follow-up questionnairesEvent information

Cohort data Relationship

assessment

NZHIS

Other Rx Sources

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Cohort sizeCohort size

General aim 10,000 (IMMP 11,000)General aim 10,000 (IMMP 11,000)Greater numbers required to detect Greater numbers required to detect differences differences – if events naturally commonif events naturally common– for sub-group analysesfor sub-group analysesSmaller numbers still produce good dataSmaller numbers still produce good data– fluoxetine <7000fluoxetine <7000Signals can be identified / confirmed with Signals can be identified / confirmed with much smaller numbers (<1000)much smaller numbers (<1000)– eg nifedipine & eye pain eg nifedipine & eye pain

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2. Soliciting the events2. Soliciting the eventsHow can this be done?How can this be done?

ActivelyActively asking for the eventsasking for the events

SystematicallySystematically asking for the eventsasking for the events

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Soliciting the eventsSoliciting the eventsHow is it done?How is it done?

The events are collected using the best The events are collected using the best source(s) availablesource(s) available– Survey prescribers (questionnaires or other)Survey prescribers (questionnaires or other)– Survey patients (questionnaires or other)Survey patients (questionnaires or other)– Real-time recording*Real-time recording*– Telephone, or visit*Telephone, or visit*– Record searches (manual, electronic)Record searches (manual, electronic)– Registers of death or morbidityRegisters of death or morbidity– Record linkage with registers or hospital Record linkage with registers or hospital

recordsrecords– Intensified spontaneous reportingIntensified spontaneous reporting– OtherOther– SeveralSeveral

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IMMP ProcessIMMP Process

Prescription

Patient and Prescription details Follow-up

questionnairesEvent information

Cohort data Relationship

assessment

NZHIS

Other Rx Sources

Other Sources

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Actively & systematically Actively & systematically askingasking

Ask after every treatmentAsk after every treatment

Patients in cohort checked to see that Patients in cohort checked to see that follow-up information receivedfollow-up information received

Repeat request for missed patientsRepeat request for missed patients

Make strenuous efforts to avoid missing Make strenuous efforts to avoid missing anyoneanyone

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Adverse event (experience)Definition (WHO)

Untoward medical occurrence

temporally associated with the use

of a medicinal product, but not

necessarily causally related

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It is It is EVENTEVENT monitoring monitoring

Any new clinical experience Any new clinical experience

(favourable or unfavourable) that is (favourable or unfavourable) that is

worthy of a record in the patient’s worthy of a record in the patient’s

file, regardless of its severity and file, regardless of its severity and

without judgement on its causality.without judgement on its causality.

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Events = reactions + incidentsEvents = reactions + incidents

ReactionsReactions11 DefiniteDefinite22 ProbableProbable33 PossiblePossible

IncidentsIncidents (background noise)(background noise)4 Unlikely4 Unlikely5 Unclassified (conditional) 5 Unclassified (conditional) 6 Unassessable6 Unassessable

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IncidentsIncidents((MakingMaking music from the noisemusic from the noise))

Should represent background morbidityShould represent background morbidity

May contain unrecognised signalsMay contain unrecognised signals– unexpected profilesunexpected profiles

Useful for assessing reporting biasUseful for assessing reporting bias– as within-drug controlsas within-drug controls– as between-drug controlsas between-drug controls

UnmaskingUnmasking

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Why adverse events?Why adverse events?To identify signals of new reactionsTo identify signals of new reactions

If only known or expected adverse reactions are If only known or expected adverse reactions are reported, unexpected adverse reactions will not reported, unexpected adverse reactions will not be identifiedbe identified

It is important to identify signals, validate them, It is important to identify signals, validate them, determine the incidence, understand their determine the incidence, understand their significance and identify the risk factors as soon significance and identify the risk factors as soon as possible. as possible.

It is not logical to specify the types of events to It is not logical to specify the types of events to be recorded. Unexpected reactions cannot be be recorded. Unexpected reactions cannot be identified by recording only the known or identified by recording only the known or expected.expected.

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Reporting requirementsReporting requirements

All new events even if common & minorAll new events even if common & minor

Change in a pre-existing conditionChange in a pre-existing condition

Abnormal changes in laboratory testsAbnormal changes in laboratory tests

AccidentsAccidents

All deathsAll deaths with date & cause with date & cause

Possible interactionsPossible interactions– NB alcohol, OCs, CAMsNB alcohol, OCs, CAMs

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Reasons for stoppingReasons for stoppingPoor compliance (adherence)Poor compliance (adherence)

No longer necessaryNo longer necessary

Change of diagnosisChange of diagnosis

Inadequate responseInadequate response

Suspected ADRSuspected ADR

DeathDeath

Lost to follow-upLost to follow-up

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PregnancyPregnancy

Routine questions about pregnancy and lactation Routine questions about pregnancy and lactation for all women of child bearing age –computer for all women of child bearing age –computer generatedgenerated

Pregnancy register establishedPregnancy register established

Time / period of exposure identifiedTime / period of exposure identified

Routine follow-up of all pregnancies after Routine follow-up of all pregnancies after expected delivery dateexpected delivery date

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Non-serious eventsNon-serious events

May indicate serious problemMay indicate serious problem

May affect complianceMay affect compliance– nauseanausea– Rash / pruritusRash / pruritus– DiarrhoeaDiarrhoea

May be more important than serious reactionsMay be more important than serious reactions

Recording all events is easier than being selectiveRecording all events is easier than being selective

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CEM in the IMMPCEM in the IMMP

Prospective observational cohort Prospective observational cohort studies on new drugs in normal studies on new drugs in normal clinical practiceclinical practice

Cohorts established from prescription Cohorts established from prescription data from pharmaciesdata from pharmacies

Events data mainly from Events data mainly from questionnaires sent to prescribersquestionnaires sent to prescribers

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ComplianceCompliance

Voluntary / unpaidVoluntary / unpaid

Doctors 80%Doctors 80%

– Limiting factor is workloadLimiting factor is workload

Patients higherPatients higher

Pharmacists 93%Pharmacists 93%

Good feedback essentialGood feedback essential

Value appreciatedValue appreciated

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‘‘Controls’Controls’

Controls create an artificial situationControls create an artificial situation

The aim is a non-interventional study in normal The aim is a non-interventional study in normal

clinical practiceclinical practice

Comparators are desirableComparators are desirable– not always possiblenot always possible– possibility of confoundingpossibility of confounding

A good study of a single drug A good study of a single drug – provides valuable dataprovides valuable data– has benchmark valuehas benchmark value

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Record linkageRecord linkage

Linking databases using unique IDLinking databases using unique ID

IMMP -routine link with IMMP -routine link with – NZHIS –identify deathsNZHIS –identify deaths– Register of deaths for certified cause(s)Register of deaths for certified cause(s)

IMMP –special studiesIMMP –special studies– Suicide & antidepressantsSuicide & antidepressants– Reactions of long latency –cancer registers / Reactions of long latency –cancer registers /

hospital discharge diagnoseshospital discharge diagnoses– Conditions of interest eg MIConditions of interest eg MI

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Cohort investigationsCohort investigations

Patient questionnairesPatient questionnaires– Eye pain and nifedipine / taste disturbance and captoprilEye pain and nifedipine / taste disturbance and captopril

Doctor questionnairesDoctor questionnaires– Angina and bezafibrateAngina and bezafibrate((confounding by indicationconfounding by indication))

Reactions of long latencyReactions of long latency– OmeprazoleOmeprazole

Case control studies (nested)Case control studies (nested)– Genetic studiesGenetic studies

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Don’t ask for too muchDon’t ask for too much

The more you ask for, the less you getThe more you ask for, the less you get

A delicate balanceA delicate balance

Concomitant therapyConcomitant therapy

Information can be requested if neededInformation can be requested if needed

Unnecessary data increases workloadUnnecessary data increases workload

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Be open mindedBe open mindedUnexpected reactions will occurUnexpected reactions will occur

Predictions of safety unreliablePredictions of safety unreliable

Experience based only on spontaneous reporting unreliableExperience based only on spontaneous reporting unreliable– 2.1 million patient exposures with olanzapine 2.1 million patient exposures with olanzapine

’’no significant safety concernsno significant safety concerns’’

No dominant pre-conceived ideasNo dominant pre-conceived ideas

All dataAll data should be collected & analysed in a totally should be collected & analysed in a totally objectiveobjective mannermanner

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Cohort event monitoringCohort event monitoring

Is an early warning systemIs an early warning system

New drugs (post-marketing surveillance)New drugs (post-marketing surveillance)

Can be used to validate signalsCan be used to validate signals

Can be used to characterize reactionsCan be used to characterize reactions

Normal clinical practice, real life situationsNormal clinical practice, real life situations

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Cohort event monitoringCohort event monitoring

Exposure in pregnancy / lactationExposure in pregnancy / lactation

Death ratesDeath rates

Reasons for stopping therapyReasons for stopping therapy

InefficacyInefficacy

Limited study periodLimited study period

Reactions of long latencyReactions of long latency

Events examined clinically and epidemiologicallyEvents examined clinically and epidemiologically

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The epidemiologyThe epidemiology

observational cohort studiesobservational cohort studies

prospectiveprospective

longitudinallongitudinal

non-interventionalnon-interventional

inceptionalinceptional

dynamicdynamic

descriptivedescriptive

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AnalysisAnalysis

Collation and signal identificationCollation and signal identification

Rates and profilesRates and profiles– Comparisons by drug, age group, etcComparisons by drug, age group, etc– By system organ classBy system organ class– Within system organ classWithin system organ class– Individual eventsIndividual events

Life table or survival analysisLife table or survival analysis

Multiple logistic regressionMultiple logistic regression– esp. for risk factorsesp. for risk factors

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Advantages of CEMAdvantages of CEM

Provides comprehensive informationProvides comprehensive information

Provides near complete informationProvides near complete information– On the target populationOn the target population– Drug utilisationDrug utilisation– EffectivenessEffectiveness– Risks and how to prevent themRisks and how to prevent them

Provides the information needed toProvides the information needed to– Handle drug scaresHandle drug scares– Minimise harmMinimise harm– Ensure treatment successEnsure treatment success

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Advantages of CEMAdvantages of CEM

Stimulates interest in drug safetyStimulates interest in drug safety

Improves spontaneous reportingImproves spontaneous reporting

Can concentrate resources on drugs of particular Can concentrate resources on drugs of particular

importance to a country or programmeimportance to a country or programme

Can be applied regionally Can be applied regionally

AdaptableAdaptable

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The essentialsThe essentials

Identify the cohortIdentify the cohort

Identify the eventsIdentify the events

With this information, you can find all With this information, you can find all you need to know (almost) you need to know (almost)

concerning safetyconcerning safety

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PEM referencesPEM references

Mann & Andrews Mann & Andrews PharmacovigilancePharmacovigilance

Title:Title: Pharmacovigilance (2 Pharmacovigilance (2ndnd Edition) 2007 Edition) 2007

Publisher:Publisher: John Wiley & Sons, Ltd. John Wiley & Sons, Ltd.

Author:Author: Mann, Ronald D.; Andrews, Elizabeth B. Mann, Ronald D.; Andrews, Elizabeth B.

Includes chapters on:Includes chapters on:PEM in the UKPEM in the UK

PEM in NZPEM in NZ

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Thank-you


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