Ellen G Engelhardt1, Alexandra J van den Broek2, Sabine C Linn3, Gordon C Wishart4, Emiel J Th Rutgers5, Anthonie O van de Velde6, Vincent T H B M Smit7, Adri C Voogd8, Sabine Siesling9, Mariël Brinkhuis10, Caroline Seynaeve11, Pieter J Westenend12, Anne M Stiggelbout1, Rob A E M Tollenaar13, Flora E van Leeuwen2, Laura J van 't Veer14, Peter M Ravdin15, Paul D P Pharaoh16, Marjanka K Schmidt17. 1. Department of Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands. 2. Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 3. Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands; Division of Medical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands. 4. Faculty of Medical Science, Anglia Ruskin University, Cambridge, UK. 5. Division of Surgical Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 6. Biometrics Department, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 7. Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands. 8. Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; Department of Epidemiology, Maastricht University Medical Center, Maastricht, The Netherlands. 9. Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. 10. Laboratory Pathology East-Netherlands, Hengelo, The Netherlands. 11. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 12. Laboratory for Pathology, Dordrecht, The Netherlands. 13. Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands. 14. Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 15. University of Texas, Health Sciences Center, San Antonio, USA. 16. Department of Oncology, Strangeways Research Laboratory, University of Cambridge, Worts Causeway, Cambridge, UK. 17. Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands; Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. Electronic address: mk.schmidt@nki.nl.
Abstract
IMPORTANCE: Online prognostication tools such as PREDICT and Adjuvant! are increasingly used in clinical practice by oncologists to inform patients and guide treatment decisions about adjuvant systemic therapy. However, their validity for young breast cancer patients is debated. OBJECTIVE: To assess first, the prognostic accuracy of PREDICT's and Adjuvant! 10-year all-cause mortality, and second, its breast cancer-specific mortality estimates, in a large cohort of breast cancer patients diagnosed <50 years. DESIGN: Hospital-based cohort. SETTING: General and cancer hospitals. PARTICIPANTS: A consecutive series of 2710 patients without a prior history of cancer, diagnosed between 1990 and 2000 with unilateral stage I-III breast cancer aged <50 years. MAIN OUTCOME MEASURES: Calibration and discriminatory accuracy, measured with C-statistics, of estimated 10-year all-cause and breast cancer-specific mortality. RESULTS: Overall, PREDICT's calibration for all-cause mortality was good (predicted versus observed) meandifference: -1.1% (95%CI: -3.2%-0.9%; P = 0.28). PREDICT tended to underestimate all-cause mortality in good prognosis subgroups (range meandifference: -2.9% to -4.8%), overestimated all-cause mortality in poor prognosis subgroups (range meandifference: 2.6%-9.4%) and underestimated survival in patients < 35 by -6.6%. Overall, PREDICT overestimated breast cancer-specific mortality by 3.2% (95%CI: 0.8%-5.6%; P = 0.007); and also overestimated it seemingly indiscriminately in numerous subgroups (range meandifference: 3.2%-14.1%). Calibration was poor in the cohort of patients with the lowest and those with the highest mortality probabilities. Discriminatory accuracy was moderate-to-good for all-cause mortality in PREDICT (0.71 [95%CI: 0.68 to 0.73]), and the results were similar for breast cancer-specific mortality. Adjuvant!'s calibration and discriminatory accuracy for both all-cause and breast cancer-specific mortality were in line with PREDICT's findings. CONCLUSIONS: Although imprecise at the extremes, PREDICT's estimates of 10-year all-cause mortality seem reasonably sound for breast cancer patients <50 years; Adjuvant! findings were similar. Prognostication tools should be used with caution due to the intrinsic variability of their estimates, and because the threshold to discuss adjuvant systemic treatment is low. Thus, seemingly insignificant mortality overestimations or underestimations of a few percentages can significantly impact treatment decision-making.
IMPORTANCE: Online prognostication tools such as PREDICT and Adjuvant! are increasingly used in clinical practice by oncologists to inform patients and guide treatment decisions about adjuvant systemic therapy. However, their validity for young breast cancerpatients is debated. OBJECTIVE: To assess first, the prognostic accuracy of PREDICT's and Adjuvant! 10-year all-cause mortality, and second, its breast cancer-specific mortality estimates, in a large cohort of breast cancerpatients diagnosed <50 years. DESIGN: Hospital-based cohort. SETTING: General and cancer hospitals. PARTICIPANTS: A consecutive series of 2710 patients without a prior history of cancer, diagnosed between 1990 and 2000 with unilateral stage I-III breast cancer aged <50 years. MAIN OUTCOME MEASURES: Calibration and discriminatory accuracy, measured with C-statistics, of estimated 10-year all-cause and breast cancer-specific mortality. RESULTS: Overall, PREDICT's calibration for all-cause mortality was good (predicted versus observed) meandifference: -1.1% (95%CI: -3.2%-0.9%; P = 0.28). PREDICT tended to underestimate all-cause mortality in good prognosis subgroups (range meandifference: -2.9% to -4.8%), overestimated all-cause mortality in poor prognosis subgroups (range meandifference: 2.6%-9.4%) and underestimated survival in patients < 35 by -6.6%. Overall, PREDICT overestimated breast cancer-specific mortality by 3.2% (95%CI: 0.8%-5.6%; P = 0.007); and also overestimated it seemingly indiscriminately in numerous subgroups (range meandifference: 3.2%-14.1%). Calibration was poor in the cohort of patients with the lowest and those with the highest mortality probabilities. Discriminatory accuracy was moderate-to-good for all-cause mortality in PREDICT (0.71 [95%CI: 0.68 to 0.73]), and the results were similar for breast cancer-specific mortality. Adjuvant!'s calibration and discriminatory accuracy for both all-cause and breast cancer-specific mortality were in line with PREDICT's findings. CONCLUSIONS: Although imprecise at the extremes, PREDICT's estimates of 10-year all-cause mortality seem reasonably sound for breast cancerpatients <50 years; Adjuvant! findings were similar. Prognostication tools should be used with caution due to the intrinsic variability of their estimates, and because the threshold to discuss adjuvant systemic treatment is low. Thus, seemingly insignificant mortality overestimations or underestimations of a few percentages can significantly impact treatment decision-making.
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