Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

Hui Miao, Mikael Hartman, Nirmala Bhoo-Pathy, Soo-Chin Lee, Nur Aishah Taib, Ern-Yu Tan, Patrick Chan, Karel G M Moons, Hoong-Seam Wong, Jeremy Goh, Siti Mastura Rahim, Cheng-Har Yip and Helena M Verkooijen

PLOS ONE, 2014, vol. 9, issue 4, 1-12

Abstract: Background: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0093755

DOI: 10.1371/journal.pone.0093755

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