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Background  Malnutrition is a significant problem in patients with ovarian cancer. The goal of this study was to investigate the prognostic role of Subjective Global Assessment (SGA) in patients with ovarian cancer treated in an integrative cancer treatment setting.    Methods  We evaluated a case series of 132 ovarian cancer patients treated at Cancer Treatment Centers of America ®  from Jan 2001 to May 2006. SGA was used to assess nutritional status at baseline. Using SGA, patients were classified as well nourished (SGA A), moderately malnourished (SGA B) or severely malnourished (SGA C). Kaplan Meier method was used to calculate survival. Cox proportional hazard models were constructed to evaluate the prognostic effect of SGA independent of other factors.    Results  Of 132 patients, 24 were newly diagnosed while 108 had received prior treatment. 15 had stage I disease at diagnosis, 8 stage II, 85 stage III and 17 stage IV. The median age at presentation was 54.4 years (range 25.5 – 82.5 years). 66 patients were well-nourished (SGA A), 35 moderately malnourished (SGA B) and 31 severely malnourished (SGA C). Well nourished patients had a median survival of 19.3 months (95% CI: 14.1 to 24.5), moderately malnourished 15.5 months (95% CI: 5.8 to 25.1), and severely malnourished 6.7 months (95% CI: 4.1 to 9.3); the difference being statistically significant (p = 0.0003). Multivariate Cox modeling, after adjusting for stage at diagnosis and prior treatment history found that moderately malnourished and severely malnourished status were associated with a relative risk of 2.1 (95% CI: 1.2 to 3.6, p = 0.008) and 3.4 (95% CI: 1.9 to 5.8, p < 0.001) respectively as compared to well nourished status.    Conclusion  Univariate and multivariate survival analyses found that low SGA scores (i.e. well-nourished status) are associated with better survival outcomes. This study lends support to the role of aggressive nutritional intervention in improving patient outcomes in cancer care.

Can subjective global assessment of nutritional status predict survival in ovarian cancer?