{"doi":"10.1002/pds.5549","title":"Development and validation of case‐finding algorithms to identify acute pancreatitis in the <scp>Veterans Health Administration</scp>","abstract":"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.","journal":"Pharmacoepidemiology and Drug Safety","year":2022,"id":295936,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9619,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":930416,"name":"Jane Dobkin","orcid":null,"position":1,"is_corresponding":false},{"id":982142,"name":"Sanjay Salgado","orcid":"0000-0002-1470-0628","position":2,"is_corresponding":false},{"id":253981,"name":"David E. Kaplan","orcid":"0000-0002-3839-336X","position":3,"is_corresponding":false},{"id":16047,"name":"Yu‐Xiao Yang","orcid":"0000-0002-3690-9174","position":4,"is_corresponding":false},{"id":982141,"name":"Louise Wang","orcid":"0000-0002-9261-8371","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T00:31:12.531553Z","pmid":"36222554","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}