Literature DB >> 15767178

A population survival model for breast cancer.

F Stracci1, F La Rosa, E Falsettini, E Ricci, C Aristei, G Bellezza, G B Bolis, D Fenocchio, S Gori, A Rulli, V Mastrandrea.   

Abstract

Breast cancer is a major health problem, and disease control depends on an effective healthcare system. A registry-based tool to monitor the quality of breast cancer care could be useful. The aim of this study was to develop a population survival model for breast cancer based on the Nottingham Prognostic Model (NPM). To this end, 1452 cases of breast cancer diagnosed in the Umbria Region, Italy, during the period 1994-1996 were studied. An extensive search for routinely available variants in prognosis and treatment was performed. In about 80% of cases complete information on factors included in the NPM was available. The Cox model was used to assess the prognostic value of study factors. Nodal stage was the most important prognostic factor. In women who did not undergo axillary dissection (17%) the risk of death was twice that in women with no affected nodes, but they received chemotherapy with the same frequency. Radiotherapy was also less frequently used in this group. Grading was a significant prognostic factor only when women over 80 were excluded. Population survival models based on data from cancer registries may provide a tool that can be used to evaluate healthcare systems and the effectiveness of interventions. The inclusion of older women in our models decreased the significance of many established prognostic factors because of the frequency of incomplete evaluation and less aggressive treatment in these patients. Not undergoing surgical axillary dissection was associated with a worse prognosis and with less aggressive treatment.

Entities:  

Mesh:

Year:  2005        PMID: 15767178     DOI: 10.1016/j.breast.2004.08.011

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  3 in total

Review 1.  Reporting methods in studies developing prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Susan Dutton; Rachel Waters; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

Review 2.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

3.  Cancer mortality trends in the Umbria region of Italy 1978-2004: a joinpoint regression analysis.

Authors:  Fabrizio Stracci; Antonio Canosa; Liliana Minelli; Anna Maria Petrinelli; Tiziana Cassetti; Carlo Romagnoli; Francesco La Rosa
Journal:  BMC Cancer       Date:  2007-01-16       Impact factor: 4.430

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.