RESUMO
ObjectiveTo determine the scores of patients with a confirmed diagnosis of drug-induced liver injury （DILI） using Roussel Uclaf Causality Assessment Method （RUCAM）， Maria ＆ Victorino assessment scale， and Revised Electronic Causality Assessment Method （RECAM）， to compare the accuracy of the three scales in diagnosis， and to investigate their clinical significance in the diagnosis of DILI. MethodsA total of 98 patients with a confirmed diagnosis of DILI who were hospitalized in Peking University First Hospital from January 2011 to December 2022 were enrolled， with liver biopsy results supporting DILI and a clear history of medication. Clinical data were collected from all subjects， and the above causality assessment scales were used for scoring. The chi-square test was used to analyze the diagnostic accuracy of the causality assessment scales， and the weighted kappa coefficient was used to analyze the consistency between the three scales. ResultsFor all patients with DILI enrolled， RECAM had the highest accuracy， with a significant difference compared with RUCAM （χ2=5.667，P=0.017）. RUCAM and RECAM had moderate consistency in diagnosis （κw=0.469）， while RECAM and Maria ＆ Victorino scale had poor consistency （κw=0.156）. For the patients with acute DILI， RECAM， RUCAM， and Maria ＆ Victorino scales had a diagnostic inconsistency rate of 3.7%， 11.1%， and 42.6%， respectively； for the patients with hepatocellular type DILI， the three scales of a diagnostic inconsistency rate of 8.9%， 21.4%， and 62.5%， respectively； for the patients with cholestasis type or mixed type DILI， the three scales of a diagnostic inconsistency rate of 10.0%， 22.5%， and 47.5%， respectively. ConclusionThe use of RECAM and RUCAM scales in acute DILI can improve diagnostic rate， and for hepatocellular type DILI and DILI with the clinical manifestation of cholestasis （cholestasis type DILI and mixed type DILI）， the use of RECAM and RUCAM scales can also improve diagnostic rate. The selection of causality assessment scales with a relatively high accuracy based on the course and clinical classification of the disease may help to further improve clinical diagnostic rate.