STS database risk models: predictors of mortality and major morbidity for lung cancer resection.

BACKGROUND: The aim of this study is to create models for perioperative risk of lung cancer resection using the STS GTDB (Society of Thoracic Surgeons General Thoracic Database). METHODS: The STS GTDB was queried for all patients treated with resection for primary lung cancer between January 1, 2002 and June 30, 2008. Three separate multivariable risk models were constructed (mortality, major morbidity, and composite mortality or major morbidity). RESULTS: There were 18,800 lung cancer resections performed at 111 participating centers. Perioperative mortality was 413 of 18,800 (2.2%). Composite major morbidity or mortality occurred in 1,612 patients (8.6%). Predictors of mortality include the following: pneumonectomy (p < 0.001), bilobectomy (p < 0.001), American Society of Anesthesiology rating (p < 0.018), Zubrod performance status (p < 0.001), renal dysfunction (p = 0.001), induction chemoradiation therapy (p = 0.01), steroids (p = 0.002), age (p < 0.001), urgent procedures (p = 0.015), male gender (p = 0.013), forced expiratory volume in one second (p < 0.001), and body mass index (p = 0.015). CONCLUSIONS: Thoracic surgeons participating in the STS GTDB perform lung cancer resections with a low mortality and morbidity. The risk-adjustment models created have excellent performance characteristics and identify important predictors of mortality and major morbidity for lung cancer resections. These models may be used to inform clinical decisions and to compare risk-adjusted outcomes for quality improvement purposes.

Duke Scholars

Author Sean Michael O'Brien Biostatistics & Bioinformatics, Division of Biostatistics