ArticleDigestive diseases and sciences2024
Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration.
Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 1 citations in OpenAlex.
- Reliance on topographical coding alone may overestimate survival in pancreatic ductal adenocarcinoma.Surgery in practice and science · 2026Article
- Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records.Cell reports. Medicine · 2025Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSurvival in pancreatic ductal adenocarcinoma (PDAC) remains poor due to late diagnosis. Electronic Health Records (EHRs) can be used to study this rare disease, but validated algorithms to identify PDAC in the United States EHRs do not currently exist.
aimsTo develop and validate an algorithm using Veterans Health Administration (VHA) EHR data for the identification of patients with PDAC.
methodsWe developed two algorithms to identify patients with PDAC in the VHA from 2002 to 2023. The algorithms required diagnosis of exocrine pancreatic cancer in either ≥ 1 or ≥ 2 of the following domains: (i) the VA national cancer registry, (ii) an inpatient encounter, or (iii) an outpatient encounter in an oncology setting. Among individuals identified with ≥ 1 of the above criteria, a random sample of 100 were reviewed by three gastroenterologists to adjudicate PDAC status. We also adjudicated fifty patients not qualifying for either algorithm. These patients died as inpatients and had alkaline phosphatase values within the interquartile range of patients who met ≥ 2 of the above criteria for PDAC. These expert adjudications allowed us to calculate the positive and negative predictive value of the algorithms.
resultsOf 10.8 million individuals, 25,533 met ≥ 1 criteria (PPV 83.0%, kappa statistic 0.93) and 13,693 individuals met ≥ 2 criteria (PPV 95.2%, kappa statistic 1.00). The NPV for PDAC was 100%.
conclusionsAn algorithm incorporating readily available EHR data elements to identify patients with PDAC achieved excellent PPV and NPV. This algorithm is likely to enable future epidemiologic studies of PDAC.
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