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Estimation and external validation of a new prognostic model for predicting recurrence-free survival for early breast cancer patients in the UK.

Campbell HE, Gray AM, Harris AL, Briggs AH, Taylor MA - Br. J. Cancer (2010)

Bottom Line: Online.When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant!

View Article: PubMed Central - PubMed

Affiliation: Health Economics Research Centre, Department of Public Health, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK. helen.campbell@dphpc.ox.ac.uk

ABSTRACT

Background: We aimed to estimate and externally validate a new UK-specific prognostic model for predicting the long-term risk of a first recurrent event (local recurrence, metastatic recurrence, or second primary breast cancer) in women diagnosed with early breast cancer.

Methods: Using data on the prognostic characteristics and outcomes of 1844 women treated for early breast cancer at the Churchill Hospital in Oxford, parametric regression-based survival analysis was used to estimate a prognostic model for recurrence-free survival. The model, which incorporated established prognostic factors, was externally validated using independent data. Its performance was compared with that of the Nottingham Prognostic Index (NPI) and Adjuvant! Online.

Results: The number of positive axillary lymph nodes, tumour grade, tumour size and patient age were strong predictors of recurrence. Oestrogen receptor (ER) positivity was shown to afford a moderate protective effect. The model was able to separate patients into distinct prognostic groups, and predicted well at the patient level, mean Brier Accuracy Score=0.17 (s.e.=0.004) and overall C=0.745 (95% CI, 0.717-0.773). Its performance diminished only slightly when applied to a second independent data set. When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant! Online.

Conclusion: The model estimated here predicts well at both the individual patient and group levels, and appears transportable to patients treated at other UK hospitals. Its parametric form permits long-term extrapolation giving it an advantage over other prognostic tools currently in use. A simple point scoring system and reference table allow 5-, 10-, and 15-year predictions from the model to be quickly and easily estimated. The model is also available to download as an interactive computer program.

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Related in: MedlinePlus

Kaplan–Meier recurrence-free survival curves for the Churchill Hospital (A) and ABC (B) data sets for five prognostic groups. (OPI cutoff values used to create groups are ⩽6.42, >6.42 to ⩽7.59, >7.59 to ⩽8.32, >8.32 to ⩽8.86, and >8.86).
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fig3: Kaplan–Meier recurrence-free survival curves for the Churchill Hospital (A) and ABC (B) data sets for five prognostic groups. (OPI cutoff values used to create groups are ⩽6.42, >6.42 to ⩽7.59, >7.59 to ⩽8.32, >8.32 to ⩽8.86, and >8.86).

Mentions: Calculating the PI (hereafter referred to as the OPI – Oxford Prognostic Index) from the model for each patient in the Churchill Hospital data set generated a normally distributed variable ranging from 4.08 (worst prognosis) to 9.59 (best prognosis). For such a variable, Cox demonstrated that the loss of information from grouping (L) would be minimised by selecting cut points which place the following percentages of individuals into groups 1–5, respectively, 11, 23.7, 30.7, 23.7, 11%. Such grouping achieved a clear separation of the Kaplan–Meier recurrence-free survival curves (Figure 3A). The OPI cut points generated by grouping women into these five categories were ⩽6.42 (poor prognosis), >6.42, and ⩽7.59 (moderate II prognosis), >7.59 and ⩽8.32 (moderate I prognosis), >8.32 and ⩽8.86 (good prognosis), and >8.86 (excellent prognosis).


Estimation and external validation of a new prognostic model for predicting recurrence-free survival for early breast cancer patients in the UK.

Campbell HE, Gray AM, Harris AL, Briggs AH, Taylor MA - Br. J. Cancer (2010)

Kaplan–Meier recurrence-free survival curves for the Churchill Hospital (A) and ABC (B) data sets for five prognostic groups. (OPI cutoff values used to create groups are ⩽6.42, >6.42 to ⩽7.59, >7.59 to ⩽8.32, >8.32 to ⩽8.86, and >8.86).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2966633&req=5

fig3: Kaplan–Meier recurrence-free survival curves for the Churchill Hospital (A) and ABC (B) data sets for five prognostic groups. (OPI cutoff values used to create groups are ⩽6.42, >6.42 to ⩽7.59, >7.59 to ⩽8.32, >8.32 to ⩽8.86, and >8.86).
Mentions: Calculating the PI (hereafter referred to as the OPI – Oxford Prognostic Index) from the model for each patient in the Churchill Hospital data set generated a normally distributed variable ranging from 4.08 (worst prognosis) to 9.59 (best prognosis). For such a variable, Cox demonstrated that the loss of information from grouping (L) would be minimised by selecting cut points which place the following percentages of individuals into groups 1–5, respectively, 11, 23.7, 30.7, 23.7, 11%. Such grouping achieved a clear separation of the Kaplan–Meier recurrence-free survival curves (Figure 3A). The OPI cut points generated by grouping women into these five categories were ⩽6.42 (poor prognosis), >6.42, and ⩽7.59 (moderate II prognosis), >7.59 and ⩽8.32 (moderate I prognosis), >8.32 and ⩽8.86 (good prognosis), and >8.86 (excellent prognosis).

Bottom Line: Online.When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant!

View Article: PubMed Central - PubMed

Affiliation: Health Economics Research Centre, Department of Public Health, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK. helen.campbell@dphpc.ox.ac.uk

ABSTRACT

Background: We aimed to estimate and externally validate a new UK-specific prognostic model for predicting the long-term risk of a first recurrent event (local recurrence, metastatic recurrence, or second primary breast cancer) in women diagnosed with early breast cancer.

Methods: Using data on the prognostic characteristics and outcomes of 1844 women treated for early breast cancer at the Churchill Hospital in Oxford, parametric regression-based survival analysis was used to estimate a prognostic model for recurrence-free survival. The model, which incorporated established prognostic factors, was externally validated using independent data. Its performance was compared with that of the Nottingham Prognostic Index (NPI) and Adjuvant! Online.

Results: The number of positive axillary lymph nodes, tumour grade, tumour size and patient age were strong predictors of recurrence. Oestrogen receptor (ER) positivity was shown to afford a moderate protective effect. The model was able to separate patients into distinct prognostic groups, and predicted well at the patient level, mean Brier Accuracy Score=0.17 (s.e.=0.004) and overall C=0.745 (95% CI, 0.717-0.773). Its performance diminished only slightly when applied to a second independent data set. When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant! Online.

Conclusion: The model estimated here predicts well at both the individual patient and group levels, and appears transportable to patients treated at other UK hospitals. Its parametric form permits long-term extrapolation giving it an advantage over other prognostic tools currently in use. A simple point scoring system and reference table allow 5-, 10-, and 15-year predictions from the model to be quickly and easily estimated. The model is also available to download as an interactive computer program.

Show MeSH
Related in: MedlinePlus