Development and validation of case-finding algorithms to identify acute pancreatitis in the Veterans Health Administration.

Purpose: Acute pancreatitis (AP) is a frequently encountered adverse drug reaction. However, the validity of diagnostic codes for AP is unknown. We aimed to determine the positive predictive value (PPV) of a diagnostic code-based algorithm for identifying patients with AP within the US Veterans Health Administration and evaluate the value of adding readily available structured laboratory information.

Methods: We identified patients with possible AP events first based on the presence of a single hospital discharge ICD-9 or ICD-10 diagnosis of AP (Algorithm 1). We then expanded Algorithm 1 by including relevant laboratory test results (Algorithm 2). Specifically, we considered amylase or lipase serum values obtained between 2 days before admission and the end of the hospitalization. Medical records of a random sample of patients identified by the respective algorithms were reviewed by two separate gastroenterologists to adjudicate AP events. The PPV (95% confidence interval [CI]) for the algorithms were calculated.

Results: Algorithm 2, consisting of one ICD-9 or ICD-10 hospital discharge diagnosis of AP and the addition of lipase serum value ≥200 U/L, had a PPV 89.1% (95% CI 83.0%-95.2%), improving from the PPV of algorithm 1 (57.9% [95% CI 46.8-69.0]).

Conclusions: An algorithm consisting of an ICD-9 or ICD-10 diagnosis of AP with a lipase value ≥200 U/L achieved high PPV. This simple algorithm can be readily implemented in any electronic health records (EHR) systems and could be useful for future pharmacoepidemiologic studies on AP.