Kozower BD, Sheng S, O'Brien SM, Liptay MJ, Lau CL, Jones DR, Shahian DM, Wright CD. STS database risk models: predictors of mortality and major morbidity for lung cancer resection.
Ann Thorac Surg 2010;
90:875-81; discussion 881-3. [PMID:
20732512 DOI:
10.1016/j.athoracsur.2010.03.115]
[Citation(s) in RCA: 261] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2010] [Revised: 03/16/2010] [Accepted: 03/19/2010] [Indexed: 01/28/2023]
Abstract
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.
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