Abstract

Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on EBUS-TBNA in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies.The goal of this study was to develop a clinical prediction model for estimating the prN2/3.We used the AQuIRE 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.The model derivation cohort (n=633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher PET-CT N stage were associated with a higher prN2/3. Area under the ROC curve was 0.85 (95% CI, 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 ROC curve was 0.88 (95% CI, 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).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.

View details for DOI 10.1164/rccm.201607-1397OC

View details for PubMedID 28002683