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Causality Assessment in Poisoning Essential for Data Quality

Causality Assessment in Poisoning Essential for Data Quality. Hugo Kupferschmidt, M.D. Director Swiss Toxicological Information Centre Zuerich. Seville, May 8, 2008 XXVIII EAPCCT Congress, Melia Sevilla. Overview. Definitions History Rationale

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Causality Assessment in Poisoning Essential for Data Quality

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  1. Causality Assessmentin PoisoningEssential for Data Quality Hugo Kupferschmidt, M.D. Director Swiss Toxicological Information Centre Zuerich Seville, May 8, 2008 XXVIII EAPCCT Congress, Melia Sevilla Swiss Toxicological Information Centre

  2. 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 Swiss Toxicological Information Centre

  3. 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: 230-5 Edwards IR et al. Drug Saf 1994; 10: 93-102 Meyboom RHB et al. Drug Saf 1997; 17: 374-89 Agbabiaka TB et al. Drug Saf 2008; 31: 21-37 Swiss Toxicological Information Centre

  4. 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 Swiss Toxicological Information Centre

  5. 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) Swiss Toxicological Information Centre

  6. 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: 211-2 Whyte IM et al. Clin Toxicol 2002; 40: 223-30 Swiss Toxicological Information Centre

  7. 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. Swiss Toxicological Information Centre

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

  9. 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 Swiss Toxicological Information Centre

  10. Causality in ADR Assessment of causality is routine in pharmaco-vigilance (spontaneous reporting). 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%) Meyboom RHB et al. Drug Saf 1997; 17: 374-89 Agbabiaka TB et al. Drug Saf 2008; 31: 21-37 Arimone Y et al. Eur J Clin Pharmacol 2005; 61: 169-73 Swiss Toxicological Information Centre

  11. 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 Swiss Toxicological Information Centre

  12. 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 / unrelated Wiholm BE. Drug Inf J 1984; 18: 267-9. Agbabiaka TB et al. Drug Saf 2008; 31: 21-37 Swiss Toxicological Information Centre

  13. 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: 285-9 Blanc S et al. Clin Pharmacol Ther 1979; 25: 493-8 Arimone Y et al. Eur J Clin Pharmacol 2005; 61: 169-73 Benahmed S et al. Eur J Clin Pharmacol 2005; 61: 537-41 Swiss Toxicological Information Centre

  14. 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 Swiss Toxicological Information Centre

  15. 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 Swiss Toxicological Information Centre

  16. 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 Swiss Toxicological Information Centre

  17. Causality in Poisoning Confirmation system by von Clarmann (1982) Level of presumptiveevidence Toxin 1 Exposure 1 Effect 1 Exposure 1 Level of confirmation Toxin 1 Effect 1 Effect 1 Level of independentconfirmation Toxin 1 Exposure 1 Additional evidence Exclusion of other causes: 1 → Score: 1-10 von Clarmann M. Rote Liste 1982, p. 95-6 Swiss Toxicological Information Centre

  18. Exposure-Effect Relationship 1. Exposure assessment • confirmed • likely • unlikely 2. Causality assessment • likely • unlikely • conditional • none • not assessable particularly important in asymptomatic cases feasible only in symptomatic cases Swiss Toxicological Information Centre

  19. 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 Swiss Toxicological Information Centre

  20. Degrees of Causality A causal relationship between exposure and effect is... • likely adequate chronology typical or expected symptoms no other causes • possible adequate chronology typical symptoms but possible other causes • conditional adequate chronology atypical symptoms and no other cause • unlikely no adequate chronology and/or atypical symptoms other causes present • not assessable no symptoms, insufficient information Swiss Toxicological Information Centre

  21. Proposed Algorithm temporal sequenceadequate?(toxicokinetics!) NO unlikely YES NO effect typical/expected?described in literature or pharmacology (mechanism) other causes absent / unlikely NO YES YES other causesabsent or unlikely? NO possible YES conditional„new effect“ likely none Swiss Toxicological Information Centre

  22. 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 Swiss Toxicological Information Centre

  23. 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 Swiss Toxicological Information Centre

  24. 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: 129-30 Buckley NA et al. Lancet 1996; 347: 1167-9 Whyte IM. Clin Toxicol 2002; 40: 211-2 Swiss Toxicological Information Centre

  25. 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. Swiss Toxicological Information Centre

  26. Finis hkupferschmidt@toxi.ch Swiss Toxicological Information Centre

  27. References Agbabiaka TB, Savovic J, Ernst E. Methods for causality assessment of adverse drug reactions. A systematic review. Drug Saf 2008; 31: 21-37. Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965; 85: 295-300. Wiholm BE. the Swedish drug-event assessment methods. Special workshop – regula-tory. Drug Inf J 1984; 18: 267-9. 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. 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. Karch FE, Lasagna L. Towards the operational identification of adeverse drug reactions. Clin Pharmacol Ther 1977; 21: 247-54. 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. Swiss Toxicological Information Centre

  28. References 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. 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. Greller HA. How to position our practice. Clin Toxicol 2004; 42: 129-30. Isbister GK. Data collection in clinical toxinology: Debunking myths and developing diagnosic algorithms. Clin Toxicol 2002; 40: 231-7. Buckley NA, Whyte IM, Dawson AH. Diagnostic data in clinical toxicology – Should we use a Bayesian approach? Clin Toxicol 2002; 40: 213-22. 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. Whyte IM. Introduction: Research in clkinical toxicology – The value of high quality data. Clin Toxicol 2002; 40: 211-2. Swiss Toxicological Information Centre

  29. References Whyte IM, Buckley NA, Dawson AH. Data collection in clinical toxicology: Are there too many variables? Clin Toxicol 2002; 40: 223-30. Hoffman RS. Dies consensus euqal correctness? Clin Toxicol 2000; 38: 689-90. Buckley NA, smith AJ. Evidence-based medicine in toxicology: Where is the evidence? Lancet 1996; 347: 1067-9. 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. 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. Auriche M, Loupi E. Does proff of causality ever exist in pharmacovigilance? Drug Saf 1993; 9: 230-5. Edwards IR, Biriell C. Harmonisation in pharmacovigilance. Drug Saf 1994; 10: 93-102. Swiss Toxicological Information Centre

  30. References 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. 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. 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. von Clarmann M. Rote Liste 1982. p. 95-6. * * * Swiss Toxicological Information Centre

  31. 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) Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  32. A.B. Hill on causality (1965) 2. Consistency: • Has the effect been observed repeatedly?By different persons, in different places, circumstances and times? Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  33. A.B. Hill on causality (1965) 3. Specificity: • The association is limited to specific expo-sures and the disease shows specific features. Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  34. A.B. Hill on causality (1965) 4. Temporality: • „Which is the cart and which is the horse?“ Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  35. A.B. Hill on causality (1965) 5. Biological gradient: • Dose-response effect Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  36. 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) Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  37. A.B. Hill on causation (1965) 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 ...“ Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  38. A.B. Hill on causation (1965) 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 Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

  39. A.B. Hill on causation (1965) 9. Analogy: • Similar effects in similar situations and after similar exposures Hill AB. Proc R Soc Med 1965; 85: 295-300 Swiss Toxicological Information Centre

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