RESUMO
PURPOSE: The development and validation of algorithms to identify cases of idiopathic acute liver injury (ALI) are essential to facilitate epidemiologic studies on drug-induced liver injury. The aim of this study is to determine the ability of diagnostic codes and laboratory measurements to identify idiopathic ALI cases. METHODS: In this cross-sectional validation study, patients were selected from the hospital-based Utrecht Patient Oriented Database between 2008 and 2010. Patients were identified using (I) algorithms based on ICD-9-CM codes indicative of idiopathic ALI combined with sets of liver enzyme values (ALT > 2× upper limit of normal (ULN); AST > 1ULN + AP > 1ULN + bilirubin > 1ULN; ALT > 3ULN; ALT > 3ULN + bilirubin > 2ULN; ALT > 10ULN) and (II) algorithms based on solely liver enzyme values (ALT > 3ULN + bilirubin > 2ULN; ALT > 10ULN). Hospital medical records were reviewed to confirm final diagnosis. The positive predictive value (PPV) of each algorithm was calculated. RESULTS: A total of 707 cases of ALI were identified. After medical review 194 (27%) patients had confirmed idiopathic ALI. The PPV for (I) algorithms with an ICD-9-CM code as well as abnormal tests ranged from 32% (13/41) to 48% (43/90) with the highest PPV found with ALT > 2ULN. The PPV for (II) algorithms with liver test abnormalities was maximally 26% (150/571). CONCLUSIONS: The algorithm based on ICD-9-CM codes indicative of ALI combined with abnormal liver-related laboratory tests is the most efficient algorithm for identifying idiopathic ALI cases. However, cases were missed using this algorithm, because not all ALI cases had been assigned the relevant diagnostic codes in daily practice.
