If externally validated, these algorithms will facilitate investigations of the epidemiology and outcomes of acetaminophen overdose.Īdministrative databases are ubiquitous and used in all areas of health care financing and delivery. ConclusionĪdministrative databases can be used to identify patients with acetaminophen overdose and hepatic complications. The accuracy of the algorithms was not affected by age, gender, or ICD coding system, but the acetaminophen overdose algorithm varied between hospitals (c-statistics 0.84–0.98 P = 0.003). Algorithms for hepatotoxicity (including codes for hepatic necrosis, toxic hepatitis and encephalopathy) and ALF (hepatic necrosis and encephalopathy) were also highly predictive (c-statistics = 0.88).
An algorithm including codes 965.4 (ICD-9-CM) and T39.1 (ICD-10) was highly accurate (sensitivity 90%, specificity 83%, positive predictive value 95%, negative predictive value 71%, c-statistic 0.87 ). Of 1,776 potential acetaminophen overdose cases, the charts of 181 patients were reviewed 139 (77%) had confirmed acetaminophen overdose. Coding algorithms for acetaminophen overdose, hepatotoxicity (alanine aminotransferase >1,000 U/L) and ALF (encephalopathy and international normalized ratio >1.5) were derived using chart abstraction data as the reference and logistic regression analyses. Patients hospitalized for acetaminophen overdose were identified using population-based administrative data (1995–2004). Our objective was to develop coding algorithms using administrative data for identifying patients with acetaminophen overdose and hepatic complications. Acetaminophen overdose is the most common cause of acute liver failure (ALF).
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