This document discusses causality assessment of adverse drug reactions (ADRs). It provides an overview of two causality models: the WHO model and Naranjo algorithm. It also distinguishes between intrinsic and extrinsic factors that can be considered when evaluating the causal relationship between a drug and an ADR. An example case report is presented and analyzed using elements of causality assessment like dechallenge, rechallenge, and background information from product labels and literature. The learning objectives are to understand how to apply the two causality models and evaluate factors in ADR causality assessment.

1. Prof E. P. van Puijenbroek, MD, PhD
October 2018
Causality assessment
Using causality models

2. • Know the difference between extrinsic and intrinsic causality
• Be able to apply two different causality models
•WHO model
•Naranjo algorithm
Learning Objectives

3. • Extrinsic and intrinsic
factors
• Causality models
• Practical examples
Outline

4. Question 1
It is important to annotate the strength of the causal
relationship between drug and adverse drug reaction
1. Agree
2. Do not agree

5. Question 2
Do you think it is possible to determine the strength of the
causal relationship between drug and adverse drug reaction?
1. Agree
2. Do not agree

6. • Literature and product information
- MEB / EMA
- Medline
• Background Incidence
- Literature
• Prescription data
- GIPdatabank.nl
• Databse
- Lareb (NL)
- Eudravigilance (EMA), Vigibase (WHO)
Extrinsic factors
“How well known is this ADR?”

7. • Pharmacological plausibility
- kinetic, dynamic, chemical structure
- latency, dechallenge
- co-medication
• Patient-related
- indication, comorbidity, renal function
- Drug metabolism
Intrinsic factors
“What patient- or drug related factors play a role?”

8. • Extrinsic and intrinsic
factors
• Causality models
• Practical examples
Outline

9. • Regulatory requirements: e.g. causality assessment for
each report mandatory when submitting reports to
EMA
• Publications of case reports
• Individual patient care
When to use causality assessment?

10. Agbabiaka TB1,Savovic J,Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf. 2008;31(1):21-37.

11. Agbabiaka TB1,Savovic J,Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf. 2008;31(1):21-37.

12. Types of causality models
• Expert judgement/global introspection: individual
assessments based on previous knowledge and
experience.
• Algorithms: set of specific questions to estimate
te strength of the causal relationship.
• Probabilistic methods (Bayesian approaches):
Transform the prior estimate of probability into a
posterior estimate of probability of drug
causation.

13. WHO causality definitions
• CERTAIN
a clinical event, including laboratory test abnormality, occurring in a plausible time relationship to drug administration, and which
cannot be explained by concurrent disease or other drugs or chemicals. The response to withdrawal of the drug (dechallenge)
should be clinically plausible. The event must be definitive pharmacologically, using a satisfactory rechallenge procedure if
necessary.
• PROBABLE/LIKELY
A clinical event, including laboratory test abnormality, with a reasonable time sequence to administration of the drug, unlikely to
be attributed to concurrent disease or other drug or chemicals, and which follows a clinically reasonable response on withdrawal
(dechallenge). Rechallenge information is not required to fulfil this defenition.
• POSSIBLE
A clinical event, including laboratory test abnormality, with a reasonable timne sequence to administration of the drug, but which
could also be explained by concurrent disease or other drugs or chemicals. Information on drug withdrawal may be lacking or
unclear.
• UNLIKELY
A clinical event, including laboratory test abnormality, with a temporal relationship to drug administration which makes a causal
relationship improbable, and in which other drug, chemicals or underlying disease provide plausible explanations.

14. WHO classification

15. Naranjo algorithm
• Systematic causality assessment
- 10 questions
- Sum score
- 63 cases from the literature
- only 3 assessors
Naranjo et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981;239-245

16. Naranjo algorithm - elements

17. ≥ 9
5-8
1-4
≤ 0
certain
probable
possible
unlikely
Naranjo algorithm – sum score

18. • Extrinsic and intrinsic
factors
• Causality models
• Practical examples
Outline

19. Case 1
• Reporter: pharmacist
• Female, 60 years
• Complaints: flu-like symptoms
• Suspect drug:
risedronate (Actonel®) 1x / week 35 mg for osteoporosis
• Concomitant-medication:
omeprazole and lormetazepam

20. • Severe symptoms: muscle pain neck and shoulders,
headaches, fever and general malaise, about half a day after
ingestion
• Complaints take approximately 5 days
• After next intake same complaints increased
Description symtoms

21. 1. Unlikely
2. Possible
3. Probable
4. Certain
Why?
How do you rate the
causality?
Text

22. • Switch to risedronate 5 mg once daily
no effect on any of the symptoms
• Medication changed into
alendronate (Fosamax®) 1x / week, 70 mg
no complaints anymore
Course of reaction

24. Background information
• Summary of Product Characteristics
• Literature
• Reported ADRs (WHO)

25. Background information
• Flu syndrome
Flu syndrome was reported in 9,8% of patients enrolled in phase 3
Paget’s disease clinical trials with risedronate (Prod Info Actonel®, 2002).
• Flu-like symptoms
Flu-like symptoms (muscle pain, bone pain, hot flushes, increased
sweating) have been reported with the use of ibandronate (Anon, 1996).
• Flu-like symptoms
nonspecific flu-like symptoms including fever, chills, bone pain,
arthralgia, and myalgia have been described in some patients treated
with zoledronic acid…

26. Background information
• WHO:
- > 150 reports of flu-like symptoms and bisphosphonates
Reporting Odds Ratio:
● Alendronate 0,92 (0,69-1,24)
● Pamidronate 10,7 (8,29-13,85)
● Risedronate 3,51 (2,20-5,59)
● Zoledronate 11,0 (7,98-15,17)
- Association flu-like symptoms + alendronate n.s.
- Association other bisphosphonates and this ADR statistical signal

28. Naranjo score
≥ 9
5-8
1-4
≤ 0
certain
probable
possible
unlikely

29. • Causation may be difficult to assess in practice
• There are various models of causality in use
• Validation leaves much to be desired, situations in
which they are applied are often too specific
Summary

30. • To be able to use two different causality model for the
assessment of adverse drug reactions
- Causality scheme of Naranjo
- Knowing the difference between extrinsic and intrinsic
causality
• Evaluation of a case report, using elements that play a role
in the assessment of ADRs
Learning Objectives

31. Exam question –example 1
• Causality assessment van be divided into intrinsic and extrinsic factors. Classify
the factors in the base below:
• A female patient, aged 60 years, suffered from dizziness due to orthostatic
hypotension, a few hours after every administration of metoprolol for
tachycardia. The dizziness resolved after 6 hours. Dizziness is described in the
Summary of Product Characteristics (SmPC) of metoprolol as a frequently
occuring adverse drug reaction (ADR). The patient’s medical history indicates
that she also had sleep disorders and uses temazepam (a benzodiazepine).
Which if the following classifications is TRUE?
A. Description of the ADR in the SmPC and the frequency of the ADR are both
intrinic factors
B. Orthostatic hypotension can pharmacologically be explained, which is an
extrinsic factor
C. A positive dechallenge and a positive rechallenge are both intrinsic factors.
D. Concomitant drug tamezepam can cause dizziness, which is an extrinsic factor.

32. Exam question – example 2
The Naranjo algorithm is a tool that can be used in the causality assessment of
possible adverse drug reactions. In this algorithm, the following aspects play a role:
1) Severity
2) Dechallenge
3) Rechallenge
4) Alternative causes
Which of the following statements is correct?
a) 1 and 2 are true, 3 and 4 are false
b) 2,3 and 4 are true, 1 is false
c) All statements are true
d) All statements are false

33. Taofikat B. Agbabiaka, Jelena Savovi, Edzard Ernst. Methods for Causality Assessment of Adverse Drug
Reactions A Systematic Review. Drug Safety 2008; 31 (1): 21-37
Anonymous. The use of the WHO-UMC system for standardised case causality assessment. Website Uppsala
Monitoring Centre.
http://www.who.int/medicines/areas/quality_safety/safety_efficacy/WHOcausality_assessment.pdf
Naranjo et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther
1981;239-245
Literature causality assessment

34. Thank you for
your attention