In:
Nephron, S. Karger AG, Vol. 147, No. 5 ( 2023), p. 251-259
Abstract:
〈 b 〉 〈 i 〉 Introduction: 〈 /i 〉 〈 /b 〉 Computed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement. 〈 b 〉 〈 i 〉 Methods: 〈 /i 〉 〈 /b 〉 Hemodialysis patients ( 〈 i 〉 n 〈 /i 〉 = 619) who had undergone abdominal CT screening were divided into the development ( 〈 i 〉 n 〈 /i 〉 = 441) and validation ( 〈 i 〉 n 〈 /i 〉 = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group’s data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group’s data, and the area under the curve (AUC), sensitivity, and specificity were calculated. 〈 b 〉 〈 i 〉 Results: 〈 /i 〉 〈 /b 〉 Of all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group’s adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group’s adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. 〈 b 〉 〈 i 〉 Discussion/Conclusion: 〈 /i 〉 〈 /b 〉 Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions.
Type of Medium:
Online Resource
ISSN:
1660-8151
,
2235-3186
Language:
English
Publisher:
S. Karger AG
Publication Date:
2023
detail.hit.zdb_id:
2810853-X
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