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142 Low preoperative skeletal muscle density predicts postoperative complications and functional decline in older women with ovarian cancer
  1. V Van der Zanden1,
  2. NJ Van Soolingen2,
  3. A Viddeleer3,
  4. J Trum2,
  5. F Amant2;4,
  6. MJE Mourits5,
  7. J Portielje6,
  8. F Van den Bos6,
  9. C De Kroon7,
  10. M Kagie8,
  11. S Oei9,
  12. A Baalbergen10,
  13. AMLDVan Haaften-de Jong11,
  14. D Houtsma12,
  15. B Van Munster1 and
  16. E Souwer6
  1. 1University Medical Centre Groningen, Internal Medicine, Groningen, Netherlands
  2. 2The Netherlands Cancer Institute, Department of Gynaecologic Oncology, Amsterdam, Netherlands
  3. 3University Medical Center Groningen, Medical Imaging Center, Department of Radiology, Groningen, Netherlands
  4. 4KU Leuven, Oncology, Leuven, Belgium
  5. 5University Medical Centre Groningen, Gynecological Oncology, Groningen, Netherlands
  6. 6Leiden University Medical Centre, Department of Medical Oncology, Leiden, Netherlands
  7. 7Leiden University Medical Centre, Department of Obstetrics and Gynecology, Leiden, Netherlands
  8. 8Haaglanden Medical Centre, Department of Obstetrics and Gynecology, The Hague, Netherlands
  9. 9Haaglanden Medical Centre, Radiology, The Hague, Netherlands
  10. 10Reinier de Graaf Group, Department of Obstetrics and Gynecology, Delft, Netherlands
  11. 11Haga Medical Centre, Department of Obstetrics and Gynecology, The Hague, Netherlands
  12. 12Haga Medical Centre, Department of Medical Oncology, The Hague, Netherlands


Introduction/Background*Insights in how to select older patients who can benefit from standard care and patients that need adjusted treatment are necessary. This study aims to determine the predictive value of lumbar skeletal muscle mass and density, measured on a computed tomography (CT) scan, for postoperative outcomes in older women with advanced stage ovarian cancer.

Methodology A multicentre, retrospective cohort study was performed in women ≥70 years old with advanced stage ovarian cancer who underwent surgery. Skeletal muscle mass and density were assessed in axial CT slices on level L3. Low skeletal muscle mass was defined as skeletal muscle index <38.50 cm2/m2. Low skeletal muscle density was defined as one standard deviation below the mean (muscle attenuation <22.55 Hounsfield Units). The primary outcome was any postoperative complication ≤30 days after surgery. Secondary outcomes included severe complications, infections, delirium, prolonged hospital stay, discharge destination, discontinuation of adjuvant chemotherapy and mortality.

To investigate whether skeletal muscle density was of added value as a predictor for postoperative complications, we first built a model with pre-existing relevant preoperative predictors only. After this model was built, we added skeletal muscle density to assess if it improved the model. A statistically significant step Chi-square statistic demonstrated that the new model performed better than the model with existing predictors.

Result(s)*213 Patients were included. Preoperative low skeletal muscle density was associated with postoperative complications ≤30 days after surgery (Odds Ratio (OR) 2.83; 95% Confidence Interval (CI) 1.41-5.67), severe complications (OR 3.01; 95%CI 1.09-8.33), infectious complications (OR 2.79; 95%CI 1.30-5.99) and discharge to a care facility (OR 3.04; 95%CI 1.16-7.93). Preoperative low skeletal muscle mass was only associated with infectious complications (OR 2.32; 95%CI 1.09-4.92). In a multivariable model (table 1), low skeletal muscle density was of added predictive value for postoperative complications to the strongest existing predictor functional impairment (KATZ-ADL ≥2) (OR 2.57; 95%CI 1.21-5.45; step Chi-Square statistic p=0.01).

Abstract 142 Table 1

Results from univariable and multivariable analysis of predictors used to build the multivariable predictable model for postoperative complications within 30 Days after surgery

Conclusion*Low skeletal muscle density, as a proxy of muscle quality, is associated with worse postoperative outcomes in older patients with advanced stage ovarian cancer. These findings can contribute to preoperative risk assessment and clinical decision making.

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