Elliot A Asare1, Lei Liu2, Kenneth R Hess3, Elisa J Gordon4, Jennifer L Paruch5, Bryan Palis6, Allison R Dahlke7, Ryan McCabe6, Mark E Cohen8, David P Winchester6, Karl Y Bilimoria9. 1. Cancer Programs, American College of Surgeons, Chicago, IL; Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: easare@facs.org. 2. Department of Preventive Medicine-Biostatistics, Northwestern University, Chicago, IL. 3. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX. 4. Center for Healthcare Studies and Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL. 5. Department of Surgery, Pritzker School of Medicine, University of Chicago, Chicago, IL. 6. Cancer Programs, American College of Surgeons, Chicago, IL. 7. Northwestern Institute for Comparative Effectiveness Research in Oncology (NICER-Onc) and Surgical Outcomes and Quality Improvement Center (SOQIC), Feinberg School of Medicine, Northwestern University, Chicago, IL. 8. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL. 9. Cancer Programs, American College of Surgeons, Chicago, IL; Northwestern Institute for Comparative Effectiveness Research in Oncology (NICER-Onc) and Surgical Outcomes and Quality Improvement Center (SOQIC), Feinberg School of Medicine, Northwestern University, Chicago, IL; Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL.
Abstract
BACKGROUND: With the large amounts of data on patient, tumor, and treatment factors available to clinicians, it has become critically important to harness this information to guide clinicians in discussing a patient's prognosis. However, no widely accepted survival calculator is available that uses national data and includes multiple prognostic factors. Our objective was to develop a model for predicting survival among patients diagnosed with breast cancer using the National Cancer Data Base (NCDB) to serve as a prototype for the Commission on Cancer's "Cancer Survival Prognostic Calculator." PATIENTS AND METHODS: A retrospective cohort of patients diagnosed with breast cancer (2003-2006) in the NCDB was included. A multivariable Cox proportional hazards regression model to predict overall survival was developed. Model discrimination by 10-fold internal cross-validation and calibration was assessed. RESULTS: There were 296,284 patients for model development and internal validation. The c-index for the 10-fold cross-validation ranged from 0.779 to 0.788 after inclusion of all available pertinent prognostic factors. A plot of the observed versus predicted 5 year overall survival showed minimal deviation from the reference line. CONCLUSION: This breast cancer survival prognostic model to be used as a prototype for building the Commission on Cancer's "Cancer Survival Prognostic Calculator" will offer patients and clinicians an objective opportunity to estimate personalized long-term survival based on patient demographic characteristics, tumor factors, and treatment delivered.
BACKGROUND: With the large amounts of data on patient, tumor, and treatment factors available to clinicians, it has become critically important to harness this information to guide clinicians in discussing a patient's prognosis. However, no widely accepted survival calculator is available that uses national data and includes multiple prognostic factors. Our objective was to develop a model for predicting survival among patients diagnosed with breast cancer using the National Cancer Data Base (NCDB) to serve as a prototype for the Commission on Cancer's "Cancer Survival Prognostic Calculator." PATIENTS AND METHODS: A retrospective cohort of patients diagnosed with breast cancer (2003-2006) in the NCDB was included. A multivariable Cox proportional hazards regression model to predict overall survival was developed. Model discrimination by 10-fold internal cross-validation and calibration was assessed. RESULTS: There were 296,284 patients for model development and internal validation. The c-index for the 10-fold cross-validation ranged from 0.779 to 0.788 after inclusion of all available pertinent prognostic factors. A plot of the observed versus predicted 5 year overall survival showed minimal deviation from the reference line. CONCLUSION: This breast cancer survival prognostic model to be used as a prototype for building the Commission on Cancer's "Cancer Survival Prognostic Calculator" will offer patients and clinicians an objective opportunity to estimate personalized long-term survival based on patient demographic characteristics, tumor factors, and treatment delivered.
Authors: S B Lee; H-K Kim; Y Choi; Y W Ju; H-B Lee; W Han; D-Y Noh; B H Son; S H Ahn; K S Kim; S J Nam; E-K Kim; H Y Park; W-C Park; J W Lee; H-G Moon Journal: Sci Rep Date: 2020-09-22 Impact factor: 4.379