Identifying Causality
for Design of an Effective Pharmacovigilance Database Model
By Craig Paardekooper

The aim of this research project is –1.

To clearly define the methods by which SAFETY (causality of adverse reactions) is
attributed to a drug
This is important for understanding the information or data that must be gathered bya database if it is to provide a strong signal when adverse reactions occur.

2.

To clearly define the methods by which EFFICACY (causality of desired reactions)can be attributed to a drug
This may involve assessing the change in incidence of the target illness.

3.

To design a database entity relationship model for gathering and storing the data
An ER model must be carefully designed to all the required information for its sole
purpose of signalling drug safety and efficacy, whilst maintaining –a) Simplicityb) User authenticationc) Accessibility (for every country)

1. Methods for Assessing Causality
The total number of methods for establishing causality between a drug and its effects is 34 methods
based on the results of a systematic review. These methods are called Causality Assessment Tools(CATS). "The principles and methods of causality assessment or causality assessment tool (CAT) help
clinicians to identify the culprit drugs."
Ref : Agbabiaka TB, Savović J, Ernst E. Methods for causality assessment of adverse drug reactions:
A systematic review. Drug Saf. 2008;31:21–37. [PubMed] [Google Scholar] [Ref list]
However, all of these methods can be reduced to 4 cardinal principles "There are multiple criteria or algorithms available as of now for establishing a causal
relationship in cases of adverse drug reaction (ADR), indicating that none of them is specificor complete. Most of these causality assessment tools (CATs) use four cardinal principles ofdiagnosis of ADR such as temporal relationship of drug with the drug reaction, biologicalplausibility of the drug causing a reaction, dechallenge, and rechallenge."
1. Temporal Relationship
2. Biological plausibility
3. Dechallenge
4. Rechallenge“When dechallenge or rechallenge has occurred in the past, it is called positive prechallenge ornegative prechallenge.”
Main Ref : "Causality or Relatedness Assessment in Adverse Drug Reaction and Its Relevance inDermatology"

2. Difficulties in Attributing Causality
Difficulties in causality assessment arise from
1.
2.
3.
4.

Incomplete information of ADR,
polypharmacy – when the user took multiple drugs,
variable clinical responses,
poor understanding of biological plausibility – not understanding the mechanism – previousstudies not clear about the effects of the drug,
5. other alternative causes – same symptoms can be attributed to other causes, and6. lack of training to clinicians.
Identifying causality in polypharmacy is a tricky situation as dechallenge–rechallenge analysis is notpossible or permitted for every individual drug that is a part of polypharmacy.
Long latency of many adverse reactions is a problem since the reaction does not manifest straightaway.
“When drug reaction is acute or a number of drugs are very few, clinical judgment is possible,
whereas when adverse reactions are slow to develop and when a number of drugs are more
and underlying disease can produce a similar manifestation, clinical judgment or global
introspection has limits.”3. Causality Assessment Method Used by WHO at the Upsala Monitoring Centre
The WHO–UMC scale which is based on the knowledge of clinical pharmacology is widely used inindividual case assessment.[4] The WHO–UMC is shown in Table 1 below.
Basic criteria for causality of these methods are knowledge about pharmacology and previous
information of ADR, association of AE (time, place, etc.) and a drug, biological plausibility andexclusion of other causes.

1. Abnormal test
2. Temporal association
3. No other drugs
4. No other diseases / no diseases
with these symptoms
5. Recovery following withdrawal
6. Plausible biological mechanism
7. Recognised effect of the drug
8. Symptoms resume afterrechallenge

Ref : The Use of the WHO-UMC System for Standardised Case Causality Assessment. [Last accessedon 2017 Jun 25]. Available from: https://www.who-umc. org/media/2768/standardised-casecausality-assessment.pdf . Last updated on 2017 Jun 06.

4. Criteria commonly used across the globe to determine causality
Some of the commonly available criteria used across the globe are Naranjo's algorithm,[i] Kramer
algorithm,[ii] Jones algorithm,[iii] Karch algorithm,[iv] Bégaud algorithm,[v] Adverse Drug Reactions
Advisory Committee guidelines,[vi]Refs :i. Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, et al. A method for estimating theprobability of adverse drug reactions. Clin Pharmacol Ther. 1981;30:239–45. [PubMed] [GoogleScholar]
ii. Kramer MS, Leventhal JM, Hutchinson TA, Feinstein AR. An algorithm for the operational
assessment of adverse drug reactions. I. Background, description, and instructions foruse. JAMA. 1979;242:623–32. [PubMed] [Google Scholar]iii. Jones JK. Adverse drug reactions in the community health setting: Approaches to recognizing,counseling, and reporting. Fam Community Health. 1982;5:58–67. [PubMed] [Google Scholar]
iv. Karch FE, Lasagna L. Toward the operational identification of adverse drug reactions. Clin
Pharmacol Ther. 1977;21:247–54. [PubMed] [Google Scholar]
v. Bégaud B, Evreux JC, Jouglard J, Lagier G. Imputation of the unexpected or toxic effects of drugs.
Actualization of the method used in France. Therapie. 1985;40:111–8. [PubMed] [Google Scholar]
vi. Mashford ML. The Australian method of drug-event assessment. Special workshop –Regulatory. Drug Inf J. 1984;18:271–3. [PubMed] [Google Scholar]

5. Naranjo’s Algorithm

When questions in the above questionnaire are answered by a clinician, a score is obtained. Basedon this score, ADR is categorised into the following four categories: ≥9 = definite ADR, 5–8 = probable
ADR, 1–4 = possible ADR, and 0 = doubtful ADR.

6. Bayesian Assessment

Bayesian Adverse Reaction Diagnostic Instrument (BARDI).
BARDI considers prior probability of the drug in question as obtained by prior epidemiologicstudies (prior probability or prior knowledge) combining it with the likelihood ratio asobtained by a given case information (current case information provided by a clinician). In
short, it calculates so-called posterior probability or posterior odds of the drug causing agiven drug reaction. Prior odds factor is the ratio of expected drug attributable risk to thebackground risk of a certain AE. The likelihood ratio is calculated for history, timing of the AEwith regard to drug, characteristics of the event, dechallenge referring to signs andsymptoms after drug withdrawal, and rechallenge.
Ref :Hutchinson TA. Computerized Bayesian ADE assessment. Drug Inf J. 1991;25:235–4. [GoogleScholar]
Lanctôt KL, Naranjo CA. Computer-assisted evaluation of adverse events using a Bayesianapproach. J Clin Pharmacol. 1994;34:142–7. [PubMed] [Google Scholar]