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. 2016 Jun;222(6):992-1000.e1.
doi: 10.1016/j.jamcollsurg.2016.02.020. Epub 2016 Mar 8.

Analyzing Risk Factors for Morbidity and Mortality after Lung Resection for Lung Cancer Using the NSQIP Database

Raymond A Jean  1 , Matthew R DeLuzio  1 , Alexander I Kraev  2 , Gongyi Wang  3 , Daniel J Boffa  4 , Frank C Detterbeck  4 , Zuoheng Wang  3 , Anthony W Kim  5
Affiliations

Analyzing Risk Factors for Morbidity and Mortality after Lung Resection for Lung Cancer Using the NSQIP Database

Raymond A Jean et al. J Am Coll Surg. 2016 Jun.

Abstract

Background: Our goal was to develop a predictive model that identifies how preoperative risk factors and perioperative complications lead to mortality after anatomic pulmonary resections.

Study design: This was a retrospective cohort study. The American College of Surgeons NSQIP database was examined for all patients undergoing elective lobectomies for cancer from 2005 through 2012. Fifty-eight pre- and intraoperative risk factors and 13 complications were considered for their impact on perioperative mortality within 30 days of surgery. Multivariate logistic regression and a logistic regression model using least absolute shrinkage and selection operator (LASSO) selection methods were used to identify preoperative risk factors that were significant for predicting mortality, either through or independent of complications. Only factors that were significant under both the multivariate logistic regression and LASSO-selected models were considered to be validated for the final model.

Results: There were 6,435 lobectomies identified. After multivariate logistic regression modeling, 28 risk factors and 5 complications were found to be predictors for mortality. This was then tested against the LASSO method. There were 7 factors shared between the LASSO and multivariate logistic regressions that predicted mortality based on comorbidity: age (p = 0.007), male sex (p = 0.011), open lobectomy (p = 0.001), preoperative dyspnea at rest (p < 0.001), preoperative dyspnea on exertion (p = 0.003), preoperative dysnatremia (serum sodium <135 mEq/L or >145 mEq/L) (p = 0.011), and preoperative anemia (p = 0.002). Of these, 3 variables predicted mortality independent of any complications: dyspnea at rest, dyspnea on exertion, and dysnatremia.

Conclusions: The clinical factors that predict postoperative complications and mortality are multiple and not necessarily aligned. Efforts to improve quality after anatomic pulmonary resections should focus on mechanisms to address both types of adverse outcomes.

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