TY - JOUR
T1 - Accuracy of the online prognostication tools PREDICT and Adjuvant! for early-stage breast cancer patients younger than 50 years
AU - Engelhardt, Ellen G.
AU - van den Broek, Alexandra J.
AU - Linn, Sabine C.
AU - Wishart, Gordon C.
AU - Rutgers, Emiel J. Th.
AU - van de Velde, Anthonie O.
AU - Smit, Vincent T.H.B.M.
AU - Voogd, Adri C.
AU - Siesling, Sabine
AU - Brinkhuis, Mariël
AU - Seynaeve, Caroline
AU - Westenend, Pieter J.
AU - Stiggelbout, Anne M
AU - Tollenaar, Rob A E M
AU - van Leeuwen, Flora E.
AU - van 't Veer, Laura J
AU - Ravdin, Peter M.
AU - Pharaoh, Paul D.P.
AU - Schmidt, Marjanka K.
N1 - Funding Information:
The study was funded by the Dutch Cancer Society (DCS) grant numbers DCS-NKI 2001-2423 and DCS-NKI 2007-3839; and by notary office Spier & Hazenberg for the coding procedure. M.K. Schmidt was funded by cancer research award DCS-NKI 2009-4363. E.G. Engelhardt was funded by DCS-UL2010-4805. The funding organisations had no influence on the design and conduct of the study; collection, management, analysis?and interpretation of the data; preparation, review?or approval of the manuscript; and decision to submit the manuscript for publication.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Adjuvant!
KW - Breast cancer
KW - PREDICT
KW - Prognostic accuracy
KW - Prognostication tool
KW - Young patients
UR - http://www.scopus.com/inward/record.url?scp=85017414417&partnerID=8YFLogxK
U2 - 10.1016/j.ejca.2017.03.015
DO - 10.1016/j.ejca.2017.03.015
M3 - Article
C2 - 28412587
AN - SCOPUS:85017414417
SN - 0959-8049
VL - 78
SP - 37
EP - 44
JO - European Journal of Cancer
JF - European Journal of Cancer
ER -