Validation of a model to predict perioperative mortality from lung cancer resection in the elderly

Rationale: Surgical resection is the mainstay therapy for localized non-small cell lung cancer (NSCLC), yet elderly patients are less likely to be treated due to concerns about morbidity and mortality related to surgery. Objectives: To validate and refine a clinical model to predict 30-day perioperative mortality (POM) in elderly patients undergoing curative resection for lung cancer. Methods: We identified 14,297 patients aged 65 years and older with stage I, II, or IIIA NCSLC from the Surveillance, Epidemiology, and End-Results Registry linked to Medicare claims. We used logistic regression analysis to identify independent risk factors for POM and to validate and refine a previously derived prediction model. Measurements and Main Results: Overall, POM was 4.6% (95% confidence interval, 4.2-4.9%). Multiple regression analysis revealed that greater age, male sex, resections of multiple lobes, advanced stage, greater tumor size, and certain comorbidities were associated with increased risk for POM. These risk factors were similar to those observed in the prior model. When patients were stratified according to their predicted risk of POM, the observed mortality increased from 1.2 to more than 10%. Conclusions: Among elderly patients with lung cancer, a prediction rule can identify those patients at higher risk for fatal complications from surgery. Further studies should evaluate whether use of the model can lead to improvements in treatment decision making.