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

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

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

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

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

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

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

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

10. Agbabiaka TB1,Savovic J,Ernst E
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
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 questions - Sum score cases from the literature - only 3 assessors
Naranjo et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981;

16. Naranjo algorithm - elements

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

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

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. Description symtoms
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

21. How do you rate the causality?
Text
How do you rate the causality?
Unlikely
Possible
Probable
Certain
Why?

22. Course of reaction
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

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. Summary 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

30. Learning Objectives
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

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?
Description of the ADR in the SmPC and the frequency of the ADR are both intrinic factors
Orthostatic hypotension can pharmacologically be explained, which is an extrinsic factor
A positive dechallenge and a positive rechallenge are both intrinsic factors.
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? 
1 and 2 are true, 3 and 4 are false
2,3 and 4 are true, 1 is false
All statements are true
All statements are false

33. Literature causality assessment
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.
Naranjo et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther ;

34. Thank you for your attention