title: Validity of the online PREDICT tool in older patients with breast cancer: a population-based study.
abstract: 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.
authors.affiliation: Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.|Department of Gerontology and Geriatrics, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: N A
authors.initials: NA
authors.lastname: de Glas
authors.affiliation: Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.|Department of Gerontology and Geriatrics, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: E
authors.initials: E
authors.lastname: Bastiaannet
authors.affiliation: Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: C C
authors.initials: CC
authors.lastname: Engels
authors.affiliation: Department of Gerontology and Geriatrics, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: A J M
authors.initials: AJ
authors.lastname: de Craen
authors.affiliation: Department of Medical Statistics, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: H
authors.initials: H
authors.lastname: Putter
authors.affiliation: Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: C J H
authors.initials: CJ
authors.lastname: van de Velde
authors.affiliation: Cancer and Ageing Research Program, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA.
authors.forename: A
authors.initials: A
authors.lastname: Hurria
authors.affiliation: Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
authors.forename: G J
authors.initials: GJ
authors.lastname: Liefers
authors.affiliation: Department of Medical Oncology, Haga Hospital The Hague, Leyweg 275, 2545 CH Den Haag, The Netherlands.
authors.forename: J E A
authors.initials: JE
authors.lastname: Portielje
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000473:pathology / Q000628:therapy; D015331:Cohort Studies; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D009426:Netherlands / Q000453:epidemiology; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment