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A predictive model for lung cancer screening nonadherence in a community setting health-care network.
Bastani, Mehrad; Chiuzan, Codruta; Silvestri, Gerard; Raoof, Suhail; Chusid, Jesse; Diefenbach, Michael; Cohen, Stuart L.
Afiliación
  • Bastani M; Department of Radiology, Northwell Health, Manhasset, NY, USA.
  • Chiuzan C; Feinstein Institutes for Medical Research, Manhasset, NY, USA.
  • Silvestri G; Feinstein Institutes for Medical Research, Manhasset, NY, USA.
  • Raoof S; Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA.
  • Chusid J; Department of Pulmonary Medicine, Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA.
  • Diefenbach M; Department of Radiology, Northwell Health, Manhasset, NY, USA.
  • Cohen SL; Department of Pulmonary Medicine, Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA.
JNCI Cancer Spectr ; 7(2)2023 03 01.
Article en En | MEDLINE | ID: mdl-37027213
BACKGROUND: Lung cancer screening (LCS) decreases lung cancer mortality. However, its benefit may be limited by nonadherence to screening. Although factors associated with LCS nonadherence have been identified, to the best of our knowledge, no predictive models have been developed to predict LCS nonadherence. The purpose of this study was to develop a predictive model leveraging a machine learning model to predict LCS nonadherence risk. METHODS: A retrospective cohort of patients who enrolled in our LCS program between 2015 and 2018 was used to develop a model to predict the risk of nonadherence to annual LCS after the baseline examination. Clinical and demographic data were used to fit logistic regression, random forest, and gradient-boosting models that were internally validated on the basis of accuracy and area under the receiver operating curve. RESULTS: A total of 1875 individuals with baseline LCS were included in the analysis, with 1264 (67.4%) as nonadherent. Nonadherence was defined on the basis of baseline chest computed tomography (CT) findings. Clinical and demographic predictors were used on the basis of availability and statistical significance. The gradient-boosting model had the highest area under the receiver operating curve (0.89, 95% confidence interval = 0.87 to 0.90), with a mean accuracy of 0.82. Referral specialty, insurance type, and baseline Lung CT Screening Reporting & Data System (LungRADS) score were the best predictors of nonadherence to LCS. CONCLUSIONS: We developed a machine learning model using readily available clinical and demographic data to predict LCS nonadherence with high accuracy and discrimination. After further prospective validation, this model can be used to identify patients for interventions to improve LCS adherence and decrease lung cancer burden.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: JNCI Cancer Spectr Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: JNCI Cancer Spectr Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido