UpToDate
Official reprint from UpToDate®
www.uptodate.com ©2017 UpToDate, Inc. and/or its affiliates. All Rights Reserved.

Medline ® Abstract for Reference 6

of 'The approach to ovarian cancer in older women'

6
TI
Analysis of prognostic factors of comprehensive geriatric assessment and development of a clinical scoring system in elderly Asian patients with cancer.
AU
Kanesvaran R, Li H, Koo KN, Poon D
SO
J Clin Oncol. 2011 Sep;29(27):3620-7. Epub 2011 Aug 22.
 
PURPOSE: To determine the impact of each comprehensive geriatric assessment (CGA) domain on overall survival (OS) and develop a prognostic scoring system for elderly patients with cancer.
PATIENTS AND METHODS: A retrospective analysis of CGA data collected from 249 consecutive patients with cancer who attended the outpatient geriatric oncology clinic at the National Cancer Center Singapore age 70 years or older was performed. Univariate and multivariate analyses were performed using Cox proportional hazards method to identify significant prognostic factors within the CGA. A simple nomogram to predict OS was developed using regression coefficients from the multivariate model. Concordance between predicted and observed response of the individual patient score was evaluated by means of Harrell's c-index. Calibration was performed using simulated data via bootstrap.
RESULTS: Median age of the patients was 77 years (range, 70 to 94 years). In our model, age (hazard ratio [HR], 1.04; 95% CI, 1.01 to 1.07), abnormal albumin level (HR, 1.97; 95% CI, 1.23 to 3.15), poor Eastern Cooperative Oncology Group performance status (≥2 v<2: HR, 1.77; 95% CI, 1.15 to 2.72), abnormal geriatric depression scale status (HR, 1.81; 95% CI, 1.29 to 2.56), high malnutrition risk (high v low risk: HR, 1.84; 95% CI, 1.17 to 2.87), and advanced disease stage (late v early: HR, 1.71; 95% CI, 0.98 to 2.95) were independent predictors of survival.
CONCLUSION: Results confirm the importance of the CGA in assessment of elderly patients with cancer. The development of this nomogram incorporating these prognostic factors helps predict OS of patients, for further intervention.
AD
National Cancer Centre Singapore. Ravindran.Kanesvaran@nccs.com.sg
PMID