Analysis of causality assessment methods in suspected HILI cases shows relevant gaps as assessed for accuracy, bias and transparency using data provided through the Freedom of Information Act (FOIA)

Abstract

Causality Assessment Methods (CAMs) are employed to assess adverse effects from various agents. The use of the United States based Drug Induced Liver Injury Network (DILIN) method, which relies on global introspection (GI) or expert opinion (EO) for causality assessment in the case of herbinduced or drug-induced liver injury (HILI/DILI) is examined for bias. The accuracy of the Roussel Uclaf Causality Assessment Method (RUCAM) as used by the DILIN, overall transparency of the DILIN’s use of both CAMs and the ability of the RUCAM to resist bias are also assessed. Data obtained from a Freedom of Information Act (FOIA) production by the National Institutes of Health (NIH) for material related to a publication by the US DILIN were analyzed to determine if any of 10 chosen forms of cognitive dispositions to respond (CDRs) or cognitive bias were present in an investigation of a dietary supplement, OxyELITE Pro (OEP). Data not originally included for publication were also utilized to assess the accuracy of the DILIN’s RUCAM scoring and transparency. To assess the RUCAM’s possible resistance to bias, mean RUCAM scores were calculated to evaluate those produced by a Primary Investigator (PI) versus computer for OEP and non-OEP products. A minimum of 4 and up to 10 CDRs were present. The data also showed the RUCAM may resist bias as there was no difference in causality grading between the mean PI and computer-based RUCAM scores. However, the lack of inferential analyses and small sample size are limitations. RUCAM scores by DILIN authors for OEP consisted of 1 as “unlikely” 4 as “possible” and 2 as “probable.” However, when scores were recalculated based upon previously unreported data, RUCAM scores decreased substantially with 3 that should have been “excluded” and 4 as “possible,” indicating inaccurate scoring. Discrepancies between published data and those obtained via FOIA showed a lack of transparency. It is concluded that the DILIN method lacks transparency while being prone to bias. The RUCAM is the most appropriate method for evidence-based medicine but requires data to be reported objectively and transparently in order to avoid inaccurate scoring, misdiagnoses and incorrect causality attribution.