Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis
Background Muscle mass is important in determining patients’ nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Renal Failure |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2231097 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850187125037727744 |
|---|---|
| author | Rongrong Tian Liyang Chang Ying Zhang Hongmei Zhang |
| author_facet | Rongrong Tian Liyang Chang Ying Zhang Hongmei Zhang |
| author_sort | Rongrong Tian |
| collection | DOAJ |
| description | Background Muscle mass is important in determining patients’ nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis (HD).Methods A total of 346 patients undergoing HD were enrolled and randomly divided into a 70% training set and a 30% validation set. The training set was used to develop the nomogram model, and the validation set was used to validate the developed model. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, a calibration curve, and the Hosmer–Lemeshow test. A decision curve analysis (DCA) was used to evaluate the clinical practicality of the nomogram model.Results Age, sex, body mass index (BMI), handgrip strength (HGS), and gait speed (GS) were included in the nomogram for predicting low skeletal muscle mass index (LSMI). The diagnostic nomogram model exhibited good discrimination with an area under the ROC curve (AUC) of 0.906 (95% CI, 0.862–0.940) in the training set and 0.917 (95% CI, 0.846–0.962) in the validation set. The calibration analysis also showed excellent results. The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.Conclusions The prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. This nomogram provides an accurate visual tool for medical staff for prediction, early intervention, and graded management. |
| format | Article |
| id | doaj-art-d666d69e33394caeb072d49ab64198f4 |
| institution | OA Journals |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-d666d69e33394caeb072d49ab64198f42025-08-20T02:16:10ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492023-12-0145110.1080/0886022X.2023.2231097Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysisRongrong Tian0Liyang Chang1Ying Zhang2Hongmei Zhang3Department of Blood Purification Centre, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaDepartment of Blood Purification Centre, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaDepartment of Science and Development, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Blood Purification Centre, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaBackground Muscle mass is important in determining patients’ nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis (HD).Methods A total of 346 patients undergoing HD were enrolled and randomly divided into a 70% training set and a 30% validation set. The training set was used to develop the nomogram model, and the validation set was used to validate the developed model. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, a calibration curve, and the Hosmer–Lemeshow test. A decision curve analysis (DCA) was used to evaluate the clinical practicality of the nomogram model.Results Age, sex, body mass index (BMI), handgrip strength (HGS), and gait speed (GS) were included in the nomogram for predicting low skeletal muscle mass index (LSMI). The diagnostic nomogram model exhibited good discrimination with an area under the ROC curve (AUC) of 0.906 (95% CI, 0.862–0.940) in the training set and 0.917 (95% CI, 0.846–0.962) in the validation set. The calibration analysis also showed excellent results. The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.Conclusions The prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. This nomogram provides an accurate visual tool for medical staff for prediction, early intervention, and graded management.https://www.tandfonline.com/doi/10.1080/0886022X.2023.2231097Muscle masshemodialysisnomogramhandgrip strengthgait speed |
| spellingShingle | Rongrong Tian Liyang Chang Ying Zhang Hongmei Zhang Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis Renal Failure Muscle mass hemodialysis nomogram handgrip strength gait speed |
| title | Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| title_full | Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| title_fullStr | Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| title_full_unstemmed | Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| title_short | Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| title_sort | development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis |
| topic | Muscle mass hemodialysis nomogram handgrip strength gait speed |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2231097 |
| work_keys_str_mv | AT rongrongtian developmentandvalidationofanomogrammodelforpredictinglowmusclemassinpatientsundergoinghemodialysis AT liyangchang developmentandvalidationofanomogrammodelforpredictinglowmusclemassinpatientsundergoinghemodialysis AT yingzhang developmentandvalidationofanomogrammodelforpredictinglowmusclemassinpatientsundergoinghemodialysis AT hongmeizhang developmentandvalidationofanomogrammodelforpredictinglowmusclemassinpatientsundergoinghemodialysis |