Objective. To determine the predictive value of lumbar skeletal muscle mass and density for postoperative outcomes in older women with advanced stage ovarian cancer.Methods. A multicenter,... Show moreObjective. To determine the predictive value of lumbar skeletal muscle mass and density for postoperative outcomes in older women with advanced stage ovarian cancer.Methods. A multicenter, retrospective cohort study was performed in women >= 70 years old receiving surgery for primary, advanced stage ovarian cancer. 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 cm(2)/m(2). 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, discontinua-tion of adjuvant chemotherapy and mortality.Results. In analysis of 213 patients, preoperative low skeletal muscle density was associated with postopera-tive 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 infec-tious complications (OR 2.32; 95%CI 1.09-4.92). In a multivariable model, low skeletal muscle density was of added predictive value for postoperative complications (OR 2.57; 95%CI 1.21-5.45) to the strongest existing pre-dictor functional impairment (KATZ-ADL >= 2).Conclusion. Low skeletal muscle density, as a proxy of muscle quality, is associated with poor postoperative outcomes in older patients with advanced stage ovarian cancer. These findings can contribute to postoperative risk assessment and clinical decision making. (C) 2021 The Author(s). Published by Elsevier Inc. Show less
Hofstede, S.N.; Gademan, M.G.J.; Stijnen, T.; Nelissen, R.G.H.H.; Marang-van de Mheen, P.J.; ARGON-OPTIMA Study Grp 2018
