ArticleBMJ open2021
How to discriminate non-small cell lung cancer (NSCLC) cases from an Italian administrative database? A retrospective, secondary data use study for evaluating a novel algorithm performance.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04676321 (Key Performance Indicators for the Assessment of Diagnostic and Therapeutic Pathway of NSCLC Patients), which is not on this map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Key Performance Indicators for the Assessment of Diagnostic and Therapeutic Pathway of NSCLC Patients: a Multicenter Study (KIND NSCLC)
Who cites it
1 citing paper in PubMed.
- Association Between Chronic Obstructive Pulmonary Disease and Survival in Patients with Lung Cancer: A Nationwide Cohort Study.International journal of chronic obstructive pulmonary disease · 2026Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate an algorithm developed for identifying non-small cell lung cancer (NSCLC) candidates among patients with lung cancer with a diagnosis International Classification of Diseases: ninth revision (ICD-9) 162.x code in administrative databases. Algorithm could then be applied for identifying the NSCLC population in order to assess the appropriateness and quality of care of the NSCLC care pathway.
designAlgorithm discrimination capacity to select both NSCLC or non-NSCLC was carried out on a sample for which electronic health record (EHR) diagnosis was available. A bivariate frequency distribution and other measures were used to evaluate algorithm's performances. Associations between possible factors potentially affecting algorithm accuracy were investigated.
settingAdministrative databases used in a specific geographical area of Emilia-Romagna region, Italy.
participantsAlgorithm was carried out on patients aged >18 years, with a lung cancer diagnosis from January to December 2017 and resident in Emilia-Romagna region who have been hospitalised at IRST or in one of the hospitals placed in the Forlì-Cesena area and for which EHR diagnosis data were available. OUTCOME MEASURES: Overall accuracy, positive (PPV) and negative (NPV) predictive values, sensitivity and specificity, positive and negative likelihood ratios and diagnostic OR were calculated.
resultsA total of 430 patients were identified as lung cancer cases based on ICD-9 diagnosis. Focusing on the total incident cases (n=314), the algorithm had an overall accuracy of 82.8% with a sensitivity of 88.8%. The analysis confirmed a high level of PPV (90.2%), but lower specificity (53.7%) and NPV (50%). Higher length of stay seemed to be associated with a correct classification. Hospitalisation regimen and a supply of antiblastic therapy seemed to increase the level of PPV.
conclusionThe algorithm demonstrated a strong validity for identifying NSCLC among patients with lung cancer in hospital administrative databases and can be used to investigate the quality of cancer care for this population. TRIAL REGISTRATION NUMBER: NCT04676321.
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