Surgical resection remains the optimal therapy for the management of patients with early stage lung cancer. Determining which patients have the greatest capacity to benefit from surgical therapy requires estimating the combined effects of the patient's severity of disease, age, and co-morbid disease on mortality risk. The goal of this application is to investigate mortality assessment following lung cancer resection.
Specific Aim 1 will develop and validate an efficient and clinically useful method to estimate patient specific survival following lung cancer resection. A clinically useful tool is needed to facilitate informed clinical decision making, patient counseling and appropriate resource allocation.
Specific Aim 2 will determine the specific threshold value of hospital volume at which mortality risk is substantially Increased following lung cancer resection. Most studies of the volume-outcome relationship have used arbitrary thresholds rather than identifying a specific threshold value. This value is imperative to determine the impact of implementing volume based policies on access and delivery of care.
Specific Aim 1 will be addressed using detailed clinical data from the Society of Thoracic Surgeons Database. Cox proportional hazard regression will estimate the probability of survival as a function of patient preoperative characteristics. The resulting comprehensive model will be used as the basis of a clinically practical decision support tool for use in estimating patient specific survival.
Specific Aim 2 uses the Nationwide Inpatient Sample to identify patients having lung cancer resection. Piecewise polynomial functions (spline regression) will be used to determine the specific threshold value for the volume of lung cancer resections that substantially increases mortality risk following lung cancer resection. This application combines a detailed career development plan along with the research application. With the guidance from Dr. Kozower's advisory committee and the institutional support he will receive. Dr. Kozower will develop the skills required to evolve into an Independent Investigator In health services research.

Public Health Relevance

Lung cancer is the leading cause of cancer death In the United States. Morbidity and mortality following lung cancer resection are common due to the age and comorbidiy of the patients along with the complexity of the treatment. Determining which patients have the greatest capacity to benefit from surgical treatment of lung cancer is crucial as the number of early stage lung cancers is increasing.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Clinical Investigator Award (CIA) (K08)
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Health Care Technology and Decision Science (HTDS)
Program Officer
Anderson, Kay
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University of Virginia
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United States
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Hu, Yinin; McMurry, Timothy L; Stukenborg, George J et al. (2015) Readmission predicts 90-day mortality after esophagectomy: Analysis of Surveillance, Epidemiology, and End Results Registry linked to Medicare outcomes. J Thorac Cardiovasc Surg 150:1254-60
Walters, Dustin M; McMurry, Timothy L; Isbell, James M et al. (2014) Understanding mortality as a quality indicator after esophagectomy. Ann Thorac Surg 98:506-11; discussion 511-2
Kozower, Benjamin D; Stukenborg, George J (2012) Lung cancer resection volume: is procedure volume really an indicator of quality? Semin Thorac Cardiovasc Surg 24:93-8
LaPar, Damien J; Kron, Irving L; Jones, David R et al. (2012) Hospital procedure volume should not be used as a measure of surgical quality. Ann Surg 256:606-15
Kozower, Benjamin D; Stukenborg, George J (2012) Hospital esophageal cancer resection volume does not predict patient mortality risk. Ann Thorac Surg 93:1690-6; discussion 1696-8
Kozower, Benjamin D; Stukenborg, George J (2011) The relationship between hospital lung cancer resection volume and patient mortality risk. Ann Surg 254:1032-7
Kozower, Benjamin D; Lau, Christine L; Phillips, Jennifer V et al. (2010) A thoracic surgeon-directed tobacco cessation intervention. Ann Thorac Surg 89:926-30; discussion 930
Kozower, Benjamin D; Sheng, Shubin; O'Brien, Sean M et al. (2010) STS database risk models: predictors of mortality and major morbidity for lung cancer resection. Ann Thorac Surg 90:875-81; discussion 881-3