4.7 Article

Validity of the online PREDICT tool in older patients with breast cancer: a population-based study

期刊

BRITISH JOURNAL OF CANCER
卷 114, 期 4, 页码 395-400

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SPRINGERNATURE
DOI: 10.1038/bjc.2015.466

关键词

breast cancer; prediction; survival; older patients; geriatric oncology; PREDICT

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资金

  1. Dutch Cancer Foundation [2007-3968]
  2. Alpe d'Huzes Foundation [2011-5263]

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Background: Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer. Methods: Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan-Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices. Results: In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI) = -0.3-3.7, for 5-year overall survival, and 4.5%, 95% CI = 2.3-6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR) = 1.07, 95% CI = 0.98-1.16, P = 0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR = 1.12, 95% CI = 1.05-1.20, P = 0.0004). The c-index for 5-year overall survival was 0.73, 95% CI = 0.70-0.75, and 0.74, 95% CI = 0.72-0.76, for 10-year overall survival. Conclusions: PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.

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