Modeling Low Muscle Mass Screening in Hemodialysis Patients
© 2022 The Author(s). Published by S. Karger AG, Basel..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:147 |
|---|---|
| Enthalten in: |
Nephron - 147(2023), 5 vom: 21., Seite 251-259 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Senzaki, Daiki [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Hemodialysis |
|---|
| Anmerkungen: |
Date Completed 29.05.2023 Date Revised 01.06.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1159/000526866 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM347880533 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM347880533 | ||
| 003 | DE-627 | ||
| 005 | 20231226034902.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1159/000526866 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1159.xml |
| 035 | |a (DE-627)NLM347880533 | ||
| 035 | |a (NLM)36273447 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Senzaki, Daiki |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modeling Low Muscle Mass Screening in Hemodialysis Patients |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 29.05.2023 | ||
| 500 | |a Date Revised 01.06.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 The Author(s). Published by S. Karger AG, Basel. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a DISCUSSION/CONCLUSION: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Hemodialysis | |
| 650 | 4 | |a Low muscle mass screening | |
| 650 | 4 | |a Sarcopenia | |
| 700 | 1 | |a Yoshioka, Nobuo |e verfasserin |4 aut | |
| 700 | 1 | |a Nagakawa, Osamu |e verfasserin |4 aut | |
| 700 | 1 | |a Inayama, Emi |e verfasserin |4 aut | |
| 700 | 1 | |a Nakagawa, Takafumi |e verfasserin |4 aut | |
| 700 | 1 | |a Takayama, Hidehito |e verfasserin |4 aut | |
| 700 | 1 | |a Endo, Toko |e verfasserin |4 aut | |
| 700 | 1 | |a Nakajima, Fumitaka |e verfasserin |4 aut | |
| 700 | 1 | |a Fukui, Masayoshi |e verfasserin |4 aut | |
| 700 | 1 | |a Kijima, Yasuaki |e verfasserin |4 aut | |
| 700 | 1 | |a Oyama, Yasuo |e verfasserin |4 aut | |
| 700 | 1 | |a Kudo, Risshi |e verfasserin |4 aut | |
| 700 | 1 | |a Toyama, Tadashi |e verfasserin |4 aut | |
| 700 | 1 | |a Yamada, Yosuke |e verfasserin |4 aut | |
| 700 | 1 | |a Tsurusaki, Kiyoshi |e verfasserin |4 aut | |
| 700 | 1 | |a Aoyama, Naoki |e verfasserin |4 aut | |
| 700 | 1 | |a Matsumura, Takayasu |e verfasserin |4 aut | |
| 700 | 1 | |a Yamahara, Hideki |e verfasserin |4 aut | |
| 700 | 1 | |a Miyasato, Kenro |e verfasserin |4 aut | |
| 700 | 1 | |a Kitamura, Tetsuya |e verfasserin |4 aut | |
| 700 | 1 | |a Ikenoue, Tatsuyoshi |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nephron |d 1964 |g 147(2023), 5 vom: 21., Seite 251-259 |w (DE-627)NLM000008427 |x 2235-3186 |7 nnns |
| 773 | 1 | 8 | |g volume:147 |g year:2023 |g number:5 |g day:21 |g pages:251-259 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1159/000526866 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 147 |j 2023 |e 5 |b 21 |h 251-259 | ||