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Abstract: PO0941

Modeling Low Muscle Mass Screening in Dialysis Patients

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis

Authors

  • Senzaki, Daiki, Fujiidera Keijin-kai Clinic, Fujiidera, Japan
  • Ikenoue, Tatsuyoshi, Kyoto Daigaku Daigakuin Igaku Kenkyuka Igakubu, Kyoto, Kyoto, Japan

Group or Team Name

  • Sarcopenia Screening Tool Development Group
Background

Sarcopenia, regarded as low muscle mass, affects the prognosis of dialysis patients, and is a serious problem. Though bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) are used in conventional sarcopenia screening, BIA and DXA may not accurately predict muscle mass because dialysis patients are easily affected by water content. On the other hand, computed tomography (CT) can measure muscle mass with accuracy even in dialysis patients. However, it is not easy to use CT at bedside screening. We aimed to create a prediction model of low muscle mass that can be used at bedside using the psoas muscle mass index (PMI) from CT measurement as the gold standard.

Methods

This is a multi-center, prospective cohort study. Between June 29, 2019 and December 31, 2020, outpatients who had been screened by dialysis and CT imaging for more than 6 months at each facility were included. They were divided into a development group and a verification group based on geographical factors. The PMI was manually measured from abdominal CT to diagnose low muscle mass. From the development group, a logistic regression model was created using 42 items of clinical information as predictor variables, and variables were selected by the stepwise method. External validity was examined using the verification group, and area under the curve (AUC), sensitivity, and specificity were calculated.

Results

Of the 619 subjects, 220 (35.6%) were diagnosed with low muscle mass. The subjects were divided into a development group of 441 and a verification group of 178 patients. A predictive model of low muscle mass was created with 6 variables (mean grip strength, height, dry weight, dialysis water removal, pre-dialysis albumin, and comorbidity of liver disease). The adjusted AUC of the development group was 0.78, sensitivity 82.7%, and specificity 63.3%. The adjusted AUC of the verification group was 0.71, sensitivity 64.3%, and specificity 72.2%.

Conclusion

It is expected that muscle mass screening of dialysis patients will be possible easily, and it is expected to support prevention and intervention decisions for sarcopenia. This study did not directly compare with BIA or DXA and did not show our model is superior to BIA or DXA. Therefore, further researches are needed to support its use.