Cargando…
Modeling Low Muscle Mass Screening in Hemodialysis Patients
INTRODUCTION: 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 theref...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
S. Karger AG
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10210080/ https://www.ncbi.nlm.nih.gov/pubmed/36273447 http://dx.doi.org/10.1159/000526866 |
_version_ | 1785046997567275008 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Senzaki, Daiki |
collection | PubMed |
description | INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION/CONCLUSION: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions. |
format | Online Article Text |
id | pubmed-10210080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | S. Karger AG |
record_format | MEDLINE/PubMed |
spelling | pubmed-102100802023-05-26 Modeling Low Muscle Mass Screening in Hemodialysis Patients 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 Nephron Clin Pract Clinical Practice: Research Article INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION/CONCLUSION: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions. S. Karger AG 2023-05 2022-10-21 /pmc/articles/PMC10210080/ /pubmed/36273447 http://dx.doi.org/10.1159/000526866 Text en Copyright © 2022 by The Author(s). Published by S. Karger AG, Basel https://creativecommons.org/licenses/by-nc/4.0/This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC). Usage and distribution for commercial purposes requires written permission. |
spellingShingle | Clinical Practice: Research Article 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 Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title | Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title_full | Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title_fullStr | Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title_full_unstemmed | Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title_short | Modeling Low Muscle Mass Screening in Hemodialysis Patients |
title_sort | modeling low muscle mass screening in hemodialysis patients |
topic | Clinical Practice: Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10210080/ https://www.ncbi.nlm.nih.gov/pubmed/36273447 http://dx.doi.org/10.1159/000526866 |
work_keys_str_mv | AT senzakidaiki modelinglowmusclemassscreeninginhemodialysispatients AT yoshiokanobuo modelinglowmusclemassscreeninginhemodialysispatients AT nagakawaosamu modelinglowmusclemassscreeninginhemodialysispatients AT inayamaemi modelinglowmusclemassscreeninginhemodialysispatients AT nakagawatakafumi modelinglowmusclemassscreeninginhemodialysispatients AT takayamahidehito modelinglowmusclemassscreeninginhemodialysispatients AT endotoko modelinglowmusclemassscreeninginhemodialysispatients AT nakajimafumitaka modelinglowmusclemassscreeninginhemodialysispatients AT fukuimasayoshi modelinglowmusclemassscreeninginhemodialysispatients AT kijimayasuaki modelinglowmusclemassscreeninginhemodialysispatients AT oyamayasuo modelinglowmusclemassscreeninginhemodialysispatients AT kudorisshi modelinglowmusclemassscreeninginhemodialysispatients AT toyamatadashi modelinglowmusclemassscreeninginhemodialysispatients AT yamadayosuke modelinglowmusclemassscreeninginhemodialysispatients AT tsurusakikiyoshi modelinglowmusclemassscreeninginhemodialysispatients AT aoyamanaoki modelinglowmusclemassscreeninginhemodialysispatients AT matsumuratakayasu modelinglowmusclemassscreeninginhemodialysispatients AT yamaharahideki modelinglowmusclemassscreeninginhemodialysispatients AT miyasatokenro modelinglowmusclemassscreeninginhemodialysispatients AT kitamuratetsuya modelinglowmusclemassscreeninginhemodialysispatients AT ikenouetatsuyoshi modelinglowmusclemassscreeninginhemodialysispatients |