A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL
Year: 2017
Type: article
Study design: Observational Study
Abstract: RATIONALE: Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies. OBJECTIVES: To develop a clinical prediction model for estimating the prN2/3. METHODS: We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals. MEASUREMENTS AND MAIN RESULTS: The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132). CONCLUSIONS: Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.
Authors: Oisin J. O’Connell, Francisco A. Almeida, Michael J. Simoff, Lonny Yarmus, Ray Lazarus, Benjamin Young, Yu Chen, Roy Semaan, Timothy M. Saettele, Joseph Cicenia, Harmeet Bedi, Corrine Kliment, Liang Li, Sonali Sethi, Javier Diaz-Mendoza, David Feller-Kopman, Juhee Song, Thomas Gildea, Hans Lee, Horiana B. Grosu, Michael Machuzak, Macarena Rodriguez-Vial, George A. Eapen, Carlos A. Jimenez, Roberto F. Casal, David E. Ost
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Cancer Institute
Award: Cancer Center Support Grant
OpenAlex ID: W2565855322