Modeling Low Muscle Mass Screening in Hemodialysis Patients

• Published in: Nephron (2015) Vol. 147; no. 5; p. 251
• Main Authors: Senzaki, Daiki, Yoshioka, Nobuo, Nagakawa, Osamu, Inayama, Emi, Nakagawa, Takafumi, Takayama, Hidehito, Endo, Toko, Nakajima, Fumitaka, Fukui, Masayoshi, Kijima, Yasuaki, Oyama, Yasuo, Kudo, Risshi, Toyama, Tadashi, Yamada, Yosuke, Tsurusaki, Kiyoshi, Aoyama, Naoki, Matsumura, Takayasu, Yamahara, Hideki, Miyasato, Kenro, Kitamura, Tetsuya, Ikenoue, Tatsuyoshi
• Format: Journal Article
• Language: English
• Published: Switzerland 01-05-2023
• Subjects: Humans; Kidney Failure, Chronic - complications; Kidney Failure, Chronic - pathology; Kidney Failure, Chronic - therapy; Mass Screening; Muscle, Skeletal - diagnostic imaging; Psoas Muscles - pathology; Renal Dialysis - adverse effects; Retrospective Studies; Sarcopenia - diagnostic imaging; Sarcopenia - etiology; Low muscle mass screening; Sarcopenia; Hemodialysis
• ISSN: 2235-3186
• DOI: 10.1159/000526866

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.
Hemodialysis patients (n = 619) who had undergone abdominal CT screening were divided into the development (n = 441) and validation (n = 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.
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.
Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions.