Αρχειοθήκη ιστολογίου

Αλέξανδρος Γ. Σφακιανάκης
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5
Άγιος Νικόλαος Κρήτη 72100
2841026182
6032607174

Δευτέρα 22 Ιανουαρίου 2018

Influence of anemia and BMI on prognosis of laryngeal squamous cell carcinoma: Development of an updated prognostic model

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Publication date: March 2018
Source:Oral Oncology, Volume 78
Author(s): R.J.L.M. te Riele, E.A.C. Dronkers, M.H. van den Brink, M.J. De Herdt, A. Sewnaik, J.A. Hardillo, R.J. Baatenburg de Jong
Objectiveevaluating the impact of anemia and body mass index (BMI) on survival, and development of a prognostic model for overall survival for patients with laryngeal squamous cell carcinoma (LSCC).Materials and methodsA retrospective cohort study was performed including all consecutive patients with LSCC diagnosed and treated at the Erasmus Medical Center between January 2006 and December 2013. Patient- and tumor-specific data were collected using data from the Netherlands Comprehensive Cancer Organization and supplemented with data from patient records available in the Erasmus MC. All comorbidities were scored at the time of diagnosis.Resultsin total 788 patients were included. Mean follow-up time was 50 months (SD: ±30), during which 298 patients (37.8%) died. In both univariate and multivariate analysis BMI and anemia were significant predictors for overall survival. Multivariate analysis was performed using known predictors such as age, TNM-stage and comorbidity (ACE-27). The hazard ratio of anemia was 1.41 (95% CI: 1.05–1.90) and of BMI was 0.97 (95% CI: 0.94–0.99). BMI had an inverse association with overall survival in both univariate and multivariate survival analysis. Updating and validating an existing prognostic model with addition of anemia and BMI enhanced the performance of the prognostic model (C-statistic) from 0.77 (95% CI: 0.74–0.79) to 0.79 (95% CI: 0.77–0.82).Conclusionanemia and BMI are predictors of overall survival for LSCC, independent of other known predictors of overall survival. Adding anemia and BMI to an existing prognostic model provides better prediction of overall survival.



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