causality assessment in poisoning essential for data quality

Swiss Toxicological Information Centre 1

EAPCCT 2008: Causality Assessment in Poisoning

Causality Assessmentin Poisoning

Essential for Data Quality

Hugo Kupferschmidt, M.D.Director

Swiss Toxicological Information Centre

Zuerich

Seville, May 8, 2008XXVIII EAPCCT Congress, Melia Sevilla



Overview

Definitions

History

Rationale

Causality assessment in adverse drug reactions

Limitations and weaknesses

Causality in poisoning

A proposal for standarized causality assessment in poisoning and drug overdose

Discussion



Definitions

Causality assessment

is the evaluation of the likelyhood that a particular event (exposure) is the cause of an observed effect.

investigates the relationship between the exposure and the occurrence of an effect.

is an important component of pharmaco- and toxicovigilance

contributes to better evaluation of risk-benefit profiles

Auriche M et al. Drug Saf 1993; 9: 230-5Edwards IR et al. Drug Saf 1994; 10: 93-102

Meyboom RHB et al. Drug Saf 1997; 17: 374-89Agbabiaka TB et al. Drug Saf 2008; 31: 21-37



Rationale

Source of data on human poisoning

Prospective cohort studies and RCTs are still lacking for most questions and aspects in clinical toxicology.

Poisons Centre data remain an important and sometimes unique source of information, particu-larly on rare kinds of poisoning.

Whereever prospective cohort studies and RCTs are not to be expected in the future, there is an obligation for Poisons Centres and clincal toxico-logists to collect data on such cases accurately, carefully, and as completely as possible.

Brent J. Clin Toxicol 2005; 43: 881-6



Rationale

Quality of data on human poisoning

exposure uncertain no experimental setting

by history only (patient‘s, bystanders‘)

supported by the observed toxic effect

Having a measure on the likelyhood of expo-sure would be a substantial improvement of the data quality. information about the exposure itself

assessment of the toxic effect in the view of the exposure (causality)



Rationale

Causality assessment is necessary

for statistical purposes

for epidemiological purposes

for toxicology databases

for publication (case reports and case series)

for the generation of data on prior probabilities for Bayesian statistics in the diagnostic process

Whyte IM. Clin Toxicol 2002; 40: 211-2Whyte IM et al. Clin Toxicol 2002; 40: 223-30



Rationale

Link between severity grading and causality assess-ment The EAPCCT (together with the IPCS and the

European Commission) has developed a standard severity grading system, the PSS.

Severity grading implies that the symptoms described are related to the toxic exposure (i.e. there is a causal relationship between these symptoms and the exposure)

It is nothing than consequent now to continue in agreeing on a standard system of causality assessment.



Standardisation

Standardized causality assessment

is aimed at decreasing ambiguity of the data

plays a key role in data exchange

limits the drawing of erroneous conclusions

... is therefore a major factor of data quality.




History

Sir Austin Bradford Hill (1965): Strength of the association Consistency of the observed association Specificity Temporality (chronology) Biological gradient Plausibility Coherence Experiment Analogy

Hill AB. Proc R Soc Med 1965; 85: 295-300



Causality in ADR

Assessment of causality is routine in pharmaco-vigilance (spontaneous reporting).


Arimone Y et al. Eur J Clin Pharmacol 2005; 61: 169-73

Categories of methods

Opinion of experts, clinical judgement or global introspection

(n=4; 12%)

algorithms or standardized assessment methods

(n=26; 76%)

Probabilistic or Bayesian approaches

(12%)



Causality in ADR

Determining factors in causality assessment temporal sequence (chronology, temporality) time to onset previous information on the drug background epidemiological and clinical information dose relationship (e.g. overdoses) response pattern characteristics and mechanisms of the ADR rechallenge - dechallenge alternative aetiologies (differential diagnoses) concomitant drugs analytical confirmation

Wiholm BE. Drug Inf J 1984; 18: 267-9.Agbabiaka TB et al. Drug Saf 2008; 31: 21-37



Causality in ADR

Classification of events, degrees of causality

definite / confirmed / certain

causative

probable / likely

possible

non-assessable / unclassifiable

unclassified / conditional

unlikely / coincidental / doubtful / remote / unlikely

exclude / negative / unrelatedWiholm BE. Drug Inf J 1984; 18: 267-9.

Agbabiaka TB et al. Drug Saf 2008; 31: 21-37



Causality in ADR

Limitations and weaknesses High inter-rater variability:

Miremont (1994): Physicians tend to assign very high scores to suspected ADRs. Agreement of methods: 6%

Blanc (1979): Overall inter-rater agreement on a VAS was low (κ=0.20).

Some depend grossly on raters‘ knowledge Some are organ-specific No „gold standard“ algorithm Not all suitable to assess drug-drug interactions Either superficial or very time-consuming Data to compute prior odds often unavailable

Miremont G et al. Eur J clin Pharmacol 1994; 46: 285-9Blanc S et al. Clin Pharmacol Ther 1979; 25: 493-8

Arimone Y et al. Eur J Clin Pharmacol 2005; 61: 169-73Benahmed S et al. Eur J Clin Pharmacol 2005; 61: 537-41



Causality in ADR

Consequences of limitations and weaknesses None of the assessment systems has ever been validated

(i.e. shown to consistently and reproducibly produce a fair approximation of the truth).

Causality assessment has therefore limited scientific value. It neither eliminates nor quantifies uncertainty but, at best, categorises it in a semiquantitative way.

Standardized causality assessment has not been able to neutralize the inherent limitations of spontaneous repor-ting systems (i.e. uncertainty regarding the causal involve-ment of the drug, and underreporting).

Meyboom RHB et al. Drug Saf 1997; 17: 374-89



Causality in ADR

Perspecvtives (2008) The idea of creating standardized causality assessment

systems to provide reliable and reproducible measures of the relationship-likelihood in suspected cases of ADR seems unfeasible, since no single method has achived this to date.

The differences in ADR causality criteria and the unavoi-dable subjectivity of judgements may be responsible for the lack or reproducibility of most methods.

So far, no ADR causality assessment method has shown consistent and reproducible measurement of causality.

Therefore, no single method is universally accepted.

Agbabiaka TB et al. Drug Saf 2008; 31: 21-37



Causality in Poisoning

Differences and similarities to adverse drug reactions

Spontaneous reporting similar in pharmacovigilance and Poisons Centres

Incomplete data frequent

Uncertainty of exposure more important in poisoning

Uncertainty of dose and differential diagnoses moreimportant in poisoning

Concept of dechallenge and rechallenge not feasible in toxicology



Causality in Poisoning

Confirmation system by von Clarmann (1982)

von Clarmann M. Rote Liste 1982, p. 95-6

Toxin1

Exposure1

Effect1

Toxin1

Exposure1

Effect1

Toxin1

Exposure1

Effect1

Exclusion ofother causes: 1 → Score: 1-10

Level of presumptiveevidence

Level of confirmation

Level of independentconfirmation

Additional evidence



Exposure-Effect Relationship

1. Exposure assessment confirmed

likely

unlikely

2. Causality assessment likely

unlikely

conditional

none

not assessable

particularly importantin asymptomatic cases

feasible onlyin symptomatic cases



Likelihood of exposure

Exposure is... if... confirmed analytical detection of substance

(= objective measure)

likely observed exposure by others

realiable reliable history from patient

possible indirect evidence of exposure

unlikely no evidence of exposure

no exposure excluded by negative analytics



Degrees of Causality

A causal relationship between exposure and effect is... likely adequate chronology

typical or expected symptomsno other causes

possible adequate chronologytypical symptoms but possible othercauses

conditional adequate chronologyatypical symptoms andno other cause

unlikely no adequate chronology and/oratypical symptomsother causes present

not assessable no symptoms, insufficient information



Proposed Algorithm

temporal sequenceadequate?

(toxicokinetics!)

effect typical/expected?described in literature or

pharmacology (mechanism)

other causesabsent or unlikely?

likely

other causesabsent / unlikely

possible

NO

NO

NO

YES

YES

YES

unlikely

conditional„new effect“

NO

YES

none



10 Year Experience

Swiss Toxicol. Information Centre (1997-2006)

Degree of causality No. Percent S.D.confirmed 4875 10.1% 0.8%likely 27680 57.5% 0.5%possible 2095 4.3% 2.1%conditional 435 0.9% 3.2%unlikely 970 2.0% 1.8%not assessable 1844 3.8% 3.2%none 941 2.0% 1.7%

asymptomat 9247 19.2% 0.8%TOTAL 48162



Discussion

Consequent causality assessment in Poisons Centres to their cases should be added to stan-dard features of data handling.

One important requirement would be routine collection of follow-up data.

A Bayesian approach would be preferable, but is unrealistic as the effort to obtain and calculate the prior odds would be immense. Furthermore these prior odds would not necessarily be appli-cable to different geographical places. Therefore an algorithm-based approach may be more fea-sible.

Buckley NA et al. Clin Toxicol 2002; 40: 213-22



Discussion (2)

A long as prospective studies and RCTs are not available in certain fields of clinical toxicology, Poisons Centre data remain important sources of information.

This does not mean that not every effort should be taken to perform such trials.

Collecting data in Poisons Centres must not be a reason to prevent or impede efforts to perform high quality research.

Greller HA. Clin Toxicol 2004; 42: 129-30Buckley NA et al. Lancet 1996; 347: 1167-9

Whyte IM. Clin Toxicol 2002; 40: 211-2



Discussion (3)

Causality assessment in Poisons Centres has its place mainly for the generation of „epidemio-logical“ data, and for hypothesis generation, rather than data on treatment effects.

Causality assessment will be a necessity for common data collection.

Only cases with sufficient causality (i.e. a likely relationship between exposure and effect) should be reported or published.



Finis

[email protected]



References

1. Agbabiaka TB, Savovic J, Ernst E. Methods for causality assessment of adverse drug reactions. A systematic review. Drug Saf 2008; 31: 21-37.

2. Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965; 85: 295-300.

3. Wiholm BE. the Swedish drug-event assessment methods. Special workshop – regula-tory. Drug Inf J 1984; 18: 267-9.

4. Miremont G, Haramburu F, Bégaud B, Péré JC, Dangoumau J. Adverse drug reaction: Physician‘s opinions versus a causality assessment method. Eur J Clin Pharmacol 1994; 46: 285-9.

5. Blanc S, Leuenberger P, Berger JP, Brooke EM, Schelling JL. Judgements of trained observers on adverse drug reactions. Clin Pharmacol Ther 1979; 25: 493-8.

6. Karch FE, Lasagna L. Towards the operational identification of adeverse drug reactions. Clin Pharmacol Ther 1977; 21: 247-54.

7. Kramer MS, Leventhal JM, Hutchinson TA, Feinstein AR. An algorithm for the operational assessment of adverse drug reactions: I. Background, description, and instructions for use. JAMA 1979; 242: 623-32.



References

8. Benahmed S, Picot MC, Hillaire-Buys D, Blayac JP, Dujols P, Demoly P. Comparison of pharmacovigilance algorithms in drug hypersensitivity reactions. Eur J Clin Pharmacol 2005; 61: 537-41.

9. Brent J. 2005 Louis Roche Lecture. Professional societies and evidence-based clinical toxicology. Delivered at the XXV International Congress of the EAPCCT, Berlin, Germany. Clin Toxicol 2005; 43: 881-6.

10. Greller HA. How to position our practice. Clin Toxicol 2004; 42: 129-30.

11. Isbister GK. Data collection in clinical toxinology: Debunking myths and developing diagnosic algorithms. Clin Toxicol 2002; 40: 231-7.

12. Buckley NA, Whyte IM, Dawson AH. Diagnostic data in clinical toxicology – Should we use a Bayesian approach? Clin Toxicol 2002; 40: 213-22.

13. Buckley NA, Karalliedde L, Dawson A, Senanayake N, Eddleston M. Where is the evidence for treatments used in pesticide poisoning? Is clinical toxicology fiddling while the developing world burns? J Toxicol Clin Toxicol 2004; 42: 113-6.

14. Whyte IM. Introduction: Research in clkinical toxicology – The value of high quality data. Clin Toxicol 2002; 40: 211-2.



References

15. Whyte IM, Buckley NA, Dawson AH. Data collection in clinical toxicology: Are there too many variables? Clin Toxicol 2002; 40: 223-30.

16. Hoffman RS. Dies consensus euqal correctness? Clin Toxicol 2000; 38: 689-90.

17. Buckley NA, smith AJ. Evidence-based medicine in toxicology: Where is the evidence? Lancet 1996; 347: 1067-9.

18. Arimone Y, Bégaud B, Miremont-Salamé G, Fourrier-Réglat A, Moore N, Molimard M, Harambouru F. Agreement of expert judgement in causality assessment of adverse drug reactions. Eur J Clin Pharmacol 2005; 61: 169-73.

19. Meyboom RHB, Hekster YA, Egberts ACG, Gribnau FWJ, Edwards IR. Causal or casual? The role of causality assessment in pharmacovigilance. Drug Saf 1997; 17: 374-89.

20. Auriche M, Loupi E. Does proff of causality ever exist in pharmacovigilance? Drug Saf 1993; 9: 230-5.

21. Edwards IR, Biriell C. Harmonisation in pharmacovigilance. Drug Saf 1994; 10: 93-102.



References

22. Meyboom RHB, Egberts ACG, Edwards IR, Hekster YA, de Koning FHP, Gribnau FWJ. Principles of signal detection in Pharmacovigilance. Drug Saf 1997; 16: 355-65.

23. Neubert A, Dormann H, Weiss J, Criegee-Rieck M, Ackermann A, Levy M, Brune K, Rascher W. Are computerized monitoring systems of value to improve pharmacovigilance in pediatric patients? Eur J Clin Pharmacol 2006; 62: 959-65.

24. Hauben M, Reich L, Gerrits CM, Younus M. Illusions of objectivity and a recommendation for reporting data mining results. Eur J Clin Pharmacol 2007; 63: 517-21.

25. von Clarmann M. Rote Liste 1982. p. 95-6.

* * *



A.B. Hill on causality (1965)

1. Strength of the association:

Size of the effect

Examples: Scrotal cancer from soot exposure in chimney

sweepers (Pott P, 1775)

Mortality from lung cancer in smokers

Mortality from cholera (Snow J, London 1855)





2. Consistency:

Has the effect been observed repeatedly?By different persons, in different places, circumstances and times?





3. Specificity:

The association is limited to specific expo-sures and the disease shows specific features.





4. Temporality:

„Which is the cart and which is the horse?“





5. Biological gradient:

Dose-response effect




A.B. Hill on causation (1965)

6. Plausibility:

Is the causation biologically plausibe?

Limitations „But this is a feature we cannot demand. What is

biologically plausible depends on the biological knowledge of the day.“

„When you have eliminated the impossible, whatever remains, however improbable, must be the truth“ (Sherlock Holmes to Dr. Watson)





7. Coherence:

„... the cause-and effect interpretation [...] should not seriously conflict with the generally known facts of the natural history and biology of the disease ...“





8. Experiment:

Does experimental evidence support the cause-and-effect interpretation of our observation?

Example: Reduction of the effect after the introduction of

preventive measures





9. Analogy:

Similar effects in similar situations and after similar exposures


causality assessment in poisoning essential for data quality

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