ArticleEuropean journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)2026
The accuracy of administrative data in identifying pulmonary metastases: a population-based study in Northern Italy.
Article in European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveLung cancer remains a highly prevalent and lethal disease, with the majority of tumors identified at advanced stages. Screening with low-dose CT was shown to be effective in reducing mortality through early diagnosis. Administrative data are increasingly utilized in clinical settings and research for identifying metastatic lung cancer, yet their accuracy and limitations require thorough evaluation.
methodsThis study evaluated the use of administrative data to identify lung cancer metastases within the population-based cancer registry (CR) of Reggio Emilia by cross-referencing registry data with administrative hospital discharge records (HDRs). Distant metastases were identified using specific International Classification of Diseases, 9th revision codes, with medical records reviewed to validate and refine the identification process. Standardized incidence and mortality rates were calculated and analyzed using joinpoint regression.
resultsBetween 2018 and 2021, 1391 lung cancers exhibiting at least one HDR were identified. Of these, 383 (27.5%) cases had distant metastases, 80 (6%) had a second tumor, and 12 (1.5%) had unknown tumor sites. After a manual review of cases with codes 196 (secondary and unspecified malignant neoplasm of lymph nodes) and code 199 (malignant neoplasm without specification of the site), the number of identified metastatic tumors increased to 30.5%. Lung cancer metastases identified by the CR showed an HDR sensitivity equal to 32.6%.
conclusionAdministrative data, even with manual verification, achieved a positive predictive value of 30.5 for metastatic tumors. The scientific community and policymakers must be aware of these limitations and the need for additional resources to accurately screen to evaluate lung cancer.
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