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.


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
24 09 2021
Historique:
entrez: 25 9 2021
pubmed: 26 9 2021
medline: 3 11 2021
Statut: epublish

Résumé

To 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. Algorithm 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. Administrative databases used in a specific geographical area of Emilia-Romagna region, Italy. Algorithm 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. Overall accuracy, positive (PPV) and negative (NPV) predictive values, sensitivity and specificity, positive and negative likelihood ratios and diagnostic OR were calculated. A 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. The 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. NCT04676321.

Identifiants

pubmed: 34561258
pii: bmjopen-2020-048188
doi: 10.1136/bmjopen-2020-048188
pmc: PMC8475132
doi:

Banques de données

ClinicalTrials.gov
['NCT04676321']

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e048188

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

William Balzi (W)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Andrea Roncadori (A)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Valentina Danesi (V)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy valentina.danesi@irst.emr.it.

Ilaria Massa (I)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Silvia Manunta (S)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Nicola Gentili (N)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Angelo Delmonte (A)

Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Emilia-Romagna, Italy.

Lucio Crinò (L)

Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Emilia-Romagna, Italy.

Mattia Altini (M)

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

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Classifications MeSH