Thomas V Bilfinger1, Denise Albano1, Muhammed Perwaiz2, Roger Keresztes2, Barbara Nemesure3. 1. Department of Surgery, Stony Brook Medicine, Stony Brook, NY. 2. Department of Medicine, Stony Brook Medicine, Stony Brook, NY. 3. Department of Family, Population and Preventive Medicine, Stony Brook Medicine, Stony Brook, NY. Electronic address: Barbara.Nemesure@stonybrookmedicine.edu.
Abstract
BACKGROUND: Evidence favoring a multidisciplinary team (MDT) approach in the treatment of lung cancer is scarce, especially in the United States. The purpose of the present investigation was to evaluate survival outcomes of lung cancer patients treated with an MDT compared with a traditional care model. PATIENTS AND METHODS: The Stony Brook Cancer Center Registry was used to identify all lung cancer cases diagnosed between 2002 and 2016. We compared survival outcomes among 1956 lung cancer patients participating in our institution's Lung Cancer Evaluation Center's (LCEC) MDT program and 2315 lung cancer patients receiving traditional care. Log-ranks tests were used to evaluate differences in the 1-, 3-, 5-, and 10-year survival outcomes between the 2 groups. To address inherent biases, Cox proportional hazard models were used to estimate the effects on survival outcomes and adjust for possible confounders. Propensity matching was also performed to account for the effects of selection bias. RESULTS: The 5-year survival rates in the propensity-matched sample were one third greater among LCEC patients compared with those receiving a traditional care approach (33.6% vs. 23.0%; P < .001). After adjusting for potential confounders in the multivariable propensity-matched analyses, the LCEC model demonstrated a significant beneficial effect on 5-year survival outcomes compared with the standard treatment model (hazard ratio, 0.65; 95% confidence interval, 0.54-0.77). CONCLUSION: The results of the present investigation suggest an improved survival benefit from usage of an MDT model versus a traditional care model in the treatment of lung cancer. Despite the use of sophisticated statistical methods to mitigate bias in a nonrandomized study, additional research is needed to determine the extent to which an MDT approach for lung cancer influences patient outcomes.
BACKGROUND: Evidence favoring a multidisciplinary team (MDT) approach in the treatment of lung cancer is scarce, especially in the United States. The purpose of the present investigation was to evaluate survival outcomes of lung cancerpatients treated with an MDT compared with a traditional care model. PATIENTS AND METHODS: The Stony Brook Cancer Center Registry was used to identify all lung cancer cases diagnosed between 2002 and 2016. We compared survival outcomes among 1956 lung cancerpatients participating in our institution's Lung Cancer Evaluation Center's (LCEC) MDT program and 2315 lung cancerpatients receiving traditional care. Log-ranks tests were used to evaluate differences in the 1-, 3-, 5-, and 10-year survival outcomes between the 2 groups. To address inherent biases, Cox proportional hazard models were used to estimate the effects on survival outcomes and adjust for possible confounders. Propensity matching was also performed to account for the effects of selection bias. RESULTS: The 5-year survival rates in the propensity-matched sample were one third greater among LCEC patients compared with those receiving a traditional care approach (33.6% vs. 23.0%; P < .001). After adjusting for potential confounders in the multivariable propensity-matched analyses, the LCEC model demonstrated a significant beneficial effect on 5-year survival outcomes compared with the standard treatment model (hazard ratio, 0.65; 95% confidence interval, 0.54-0.77). CONCLUSION: The results of the present investigation suggest an improved survival benefit from usage of an MDT model versus a traditional care model in the treatment of lung cancer. Despite the use of sophisticated statistical methods to mitigate bias in a nonrandomized study, additional research is needed to determine the extent to which an MDT approach for lung cancer influences patient outcomes.
Authors: Dylan R Jones; Derek P Chew; Matthew J Horsfall; Anthony Ming-Yu Chuang; Ajay R Sinhal; Majo X Joseph; Robert A Baker; Jayme S Bennetts; Joseph B Selvanayagam; Sam J Lehman Journal: Open Heart Date: 2019-07-29
Authors: Marius Freytag; Ulrich Herrlinger; Stefan Hauser; Franz G Bauernfeind; Maria A Gonzalez-Carmona; Jennifer Landsberg; Jens Buermann; Hartmut Vatter; Tobias Holderried; Thorsten Send; Martin Schumacher; Arne Koscielny; Georg Feldmann; Mario Heine; Dirk Skowasch; Niklas Schäfer; Benjamin Funke; Michael Neumann; Ingo G H Schmidt-Wolf Journal: BMC Cancer Date: 2020-04-28 Impact factor: 4.430