Publication date: May 2018
Source:European Journal of Surgical Oncology, Volume 44, Issue 5
Author(s): D. Wagner, K. Marsoner, A. Tomberger, J. Haybaeck, J. Haas, G. Werkgartner, H. Cerwenka, H. Bacher, H.J. Mischinger, P. Kornprat
IntroductionLow skeletal muscle mass is a known predictor of morbidity and mortality in patients undergoing major pancreatic surgeries. We sought to combine low skeletal muscle mass with established risk predictors to improve their prognostic capacity for postoperative outcome and morbidity.MethodsAs established parameters to predict preoperative mortality risk for patients, the ASA classification and the Charlson Comorbidity Index (CCI) were used. The Hounsfield Units Average Calculation (HUAC) was measured to define low skeletal muscle mass in 424 patients undergoing pancreatic resections for malignancies. Patients in the lowest sex-adjusted quartile for HUAC were defined as having low skeletal muscle mass (muscle wasting). Multivariable Cox regression analysis was utilized to identify preoperative risk factors associated with postoperative morbidity.ResultsMedian patient age was 63 years (19–87), 47.9% patients were male, and half the cohort had multiple comorbidities (Charlson Comorbidity Index [CCI]>6, 63.2%), 30-day mortality was 5.8% (n = 25). Median HUAC was 19.78 HU (IQR: 15.94–23.54) with 145 patients (34.2%) having low skeletal muscle mass. Preoperative frailty defined by low skeletal muscle mass was associated with an increased risk for postoperative complications (OR 1.55, CI 95% 0.98–2.45, p = 0.014), and a higher 30-day mortality (HR 5.17, CI 95% 1.57–16.69, p = 0.004). With an AUC of 0.85 HUAC showed the highest predictability for 30-day mortality (CI 95% 0.78–0.91, p = 0.0001). Patients with CCI ≥6 and low skeletal muscle mass defined by the HUAC had a 9.78 higher risk of dying in the immediate postoperative phase (HR 9.78, CI 95% 2.98–12.2, p = 0.0001).ConclusionLow skeletal muscle mass predicts postoperative mortality and complications best and it should be incorporated to conventional risk scores to identify high risk patients.
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