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      A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index


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          Treatment of non-small-cell lung cancer (NSCLC) might take into account comorbidities as an important variable. The aim of this study was to generate a new simplified comorbidity score (SCS) and to determine whether or not it improves the possibility of predicting prognosis of NSCLC patients. A two-step methodology was used. Step 1: An SCS was developed and its prognostic value was compared with classical prognostic determinants in the outcome of 735 previously untreated NSCLC patients. Step 2: the SCS reliability as a prognostic determinant was tested in a different population of 136 prospectively accrued NSCLC patients with a formal comparison between SCS and the classical Charlson comorbidity index (CCI). Prognosis was analysed using both univariate and multivariate (Cox model) statistics. The SCS summarised the following variables: tobacco consumption, diabetes mellitus and renal insufficiency (respective weightings 7, 5 and 4), respiratory, neoplastic and cardiovascular comorbidities and alcoholism (weighting=1 for each item). In step 1, aside from classical variables such as age, stage of the disease and performance status, SCS was a statistically significant prognostic variable in univariate analyses. In the Cox model weight loss, stage grouping, performance status and SCS were independent determinants of a poor outcome. There was a trend towards statistical significance for age ( P=0.08) and leucocytes count ( P=0.06). In Step 2, both SCS and well-known prognostic variables were found as significant determinants in univariate analyses. There was a trend towards a negative prognostic effect for CCI. In multivariate analysis, stage grouping, performance status, histology, leucocytes, lymphocytes, lactate dehydrogenase, CYFRA 21-1 and SCS were independent determinants of a poor prognosis. CCI was removed from the Cox model. In conclusion, the SCS, constructed as an independent prognostic factor in a large NSCLC patient population, is validated in another prospective population and appears more informative than the CCI in predicting NSCLC patient outcome.

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          Revisions in stage grouping of the TNM subsets (T=primary tumor, N=regional lymph nodes, M=distant metastasis) in the International System for Staging Lung Cancer have been adopted by the American Joint Committee on Cancer and the Union Internationale Contre le Cancer. These revisions were made to provide greater specificity for identifying patient groups with similar prognoses and treatment options with the least disruption of the present classification: T1N0M0, stage IA; T2N0M0, stage IB; T1N1M0, stage IIA; T2N1M0 and T3N0M0, stage IIB; and T3N1M0, T1N2M0, T2N2M0, T3N2M0, stage IIIA. The TNM subsets in stage IIIB-T4 any N M0, any T N3M0, and in stage IV-any T any N M1, remain the same. Analysis of a collected database representing all clinical, surgical-pathologic, and follow-up information for 5,319 patients treated for primary lung cancer confirmed the validity of the TNM and stage grouping classification schema.
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            The effect of comorbidity on 3-year survival of women with primary breast cancer.

            To determine the effect of comorbidity and stage of disease on 3-year survival in women with primary breast cancer. Longitudinal, observational study. Metropolitan Detroit. 936 women ages 40 to 84 years. Data on stage of breast cancer, treatment type, and comorbidity were obtained from Metropolitan Detroit Cancer Surveillance System (MDCSS) files and medical records. Personal interviews were the source of information on social and behavioral factors. Vital status and cause of death were obtained from MDCSS files. Patients who had 3 or more of 7 selected comorbid conditions had a 20-fold higher rate of mortality from causes other than breast cancer and a 4-fold higher rate of all-cause mortality when compared with patients who had no comorbid conditions. The effects of comorbidity were independent of age, disease stage, tumor size, histologic type, type of treatment, race, and social and behavioral factors. Moreover, women with severe comorbid conditions had uniformly higher mortality rates, and early diagnosis in these women conferred no survival advantage. Comorbidity in patients with breast cancer appears to be a strong predictor of 3-year survival, independent of the effects of breast cancer stage. This finding suggest that trials assessing the efficacy of screening should routinely include measures of comorbidity.
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              Regression models for prognostic prediction: advantages, problems, and suggested solutions.

              Multiple regression models have wide applicability in predicting the outcome of patients with a variety of diseases. However, many researchers are using such models without validating the necessary assumptions. All too frequently, researchers also "overfit" the data by developing models using too many predictor variables and insufficient sample sizes. Models developed in this way are unlikely to stand the test of validation on a separate patient sample. Without attempting such a validation, the researcher remains unaware that overfitting has occurred. When the ratio of the number of patients suffering endpoints to the number of potential predictors is small (say less than 10), data reduction methods are available that can greatly improve the performance of regression models. Regression models can make more accurate predictions than other methods such as stratification and recursive partitioning, when model assumptions are thoroughly examined; steps are taken (ie, choosing another model or transforming the data) when assumptions are violated; and the method of model formulation does not result in overfitting the data.

                Author and article information

                Br J Cancer
                British Journal of Cancer
                Nature Publishing Group
                18 October 2005
                08 November 2005
                14 November 2005
                : 93
                : 10
                : 1098-1105
                [1 ]Thoracic Oncology Unit, Centre Hospitalier Universitaire de Montpellier, Hôpital Arnaud de Villeneuve, 34295 Montpellier Cedex 5, France
                [2 ]Department of Statistics and Epidemiology, University Institute for Clinical Research, Hôpital Universitaire Arnaud de Villeneuve, France
                Author notes
                [* ]Author for correspondence: jl-pujol@ 123456chu-montpellier.fr

                Current address: Respiratory Diseases Unit, Hôpital Saint Joseph, 6, rue de la Duchère, B-6060, Gilly, Belgium

                Copyright 2005, Cancer Research UK
                : 21 June 2005
                : 15 September 2005
                : 16 September 2005
                Clinical Studies

                Oncology & Radiotherapy
                prognosis,comorbidities,non-small-cell lung cancer
                Oncology & Radiotherapy
                prognosis, comorbidities, non-small-cell lung cancer


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