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Causality assessment of adverse reactions to drugs—II. An original model for validation of drug causality assessment methods: Case reports with positive rechallenge
Abstract
Standards are lacking for validation of drug causality assessment methods. An original model is proposed using a positive rechallenge as an external standard. This model was used to validate the novel causality assessment method (RUCAM) described in the previous article (Part I; J Clin Epidemiol 1993; 46: 1323). Seventy seven reports of drug-induced acute liver injuries with positive rechallenge were collected from the medical literature and divided into 49 cases and 28 controls. The RUCAM was applied to information obtained prior to readministration. The score was significantly higher (p < 10(-4)) in cases than in controls with high levels of sensitivity, specificity and predictive values. It is concluded that (1) adverse drug reaction reports with a positive rechallenge can provide a standard for validation of causality assessment methods, (2) RUCAM applied to drug-induced liver injuries has been validated.
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RUCAM was validated in 49 published DILI cases with positive rechallenge and 28 controls, showing a sensitivity of 86%, specificity of 89%, positive predictive value of 93%, and negative predictive value of 78%.
“and was further validated in 49 published DILI cases with positive re‐challenge and 28 controls, showing a sensitivity of 86%, specificity of 89%, positive predictive value of 93%, and 78% of negative predictive value.”
From Methods for causality assessment of idiosyncratic drug‐induced liver injury (2025) · cites this paper as [33]
The Roussel Uclaf Causality Assessment Method (RUCAM) is the oldest available tool for DILI causality assessment, introduced in 1993 and modified in 2016.
“The oldest available tool is the Roussel Uclaf Causality Assessment Method (RUCAM), also known as the Council for International Organizations of Medical Sciences scale, introduced in 1993 and modified in 2016.”
From Drug-induced liver injury. Part I: Classification, diagnosis and treatment (2025) · cites this paper as [33]
The RUCAM score ranges from -9 to +14 points, with five likelihood categories.
“The score ranges from –9 to +14 points, with the same five likelihood categories [10, 33, 34].”
From Drug-induced liver injury. Part I: Classification, diagnosis and treatment (2025) · cites this paper as [33]
In the original RUCAM of 1993, ALT thresholds were lower, with at least 2×ULN.
“In the original RUCAM of 1993, ALT thresholds were lower, with at least 2×ULN,13”
From Herb-induced Liver Injury in Asia and Current Role of RUCAM for Causality Assessment in 11,160 Published Cases (2020) · cites this paper as [13]
This led to the development of a liver-specific, quantitative, objective, transparent, and structured diagnostic algorithm which was well validated using cases with positive re-exposure tests as gold standard.
“This led to the development of a liver-specific, quantitative, objective, transparent, and structured diagnostic algorithm12 which was well validated using cases with positive re-exposure tests as gold standard.13”
From Herb-induced Liver Injury in Asia and Current Role of RUCAM for Causality Assessment in 11,160 Published Cases (2020) · cites this paper as [13]
Causality assessment for each case was performed using the Roussel Uclaf Causality Assessment Method (RUCAM).
“Investigators at each site were asked to complete causality assessment scoring for each case whose diagnosis at discharge was DILI (n=29,478) according to the Roussel Uclaf Causality Assessment Method (RUCAM).”
From Incidence and Etiology of Drug-Induced Liver Injury in Mainland China (2019) · cites this paper as [15]
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Research works printed but not linked: 6
- Hutchinson, 1989, Assessing methods for causality assessment of suspected adverse drug reactions, J Clin Epidendol, 42, 5, 10.1016/0895-4356(89)90020-6 · DOI
- Danan, 1993, Causality assessment of adverse reactions to drugs. I. A novel method based on the conclusions of international consensus meetings: Application to drug-induced liver injuries, J Clin Epidemiol, 46, 1323, 10.1016/0895-4356(93)90101-6 · DOI · Paper page
- Hutchinson, 1979, An algorithm for the operational assessment of adverse drug reactions. II. Demonstration of reproducibility and validity, JAMA, 242, 633, 10.1001/jama.242.7.633 · DOI
- 1990, 11, 272
- Biour, 1990, Hépatotoxicité des médicaments. Fichier bibliographique des atteintes hepatiques et des médicaments responsables, Gastroenterol Clin Biol, 14, 263
- Bégaud, 1985, Unexpected or toxic drug reaction assessment, Thérapie, 40, 111
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Metadata
- Authors
- Benichou, C., Danan, G., & Flahault, A.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 1993-11
- Volume
- 46
- Issue
- 11
- Pages
- 1331-1336
- ISSN
- 0895-4356
- Cited by
- 491
- References
- 0
- Language
- en
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