1
|
Zhou X, Han J, Zhu F. Development and validation of a nomogram model for accurately predicting severe fatigue in maintenance hemodialysis patients: A multicenter cross-sectional study in China. Ther Apher Dial 2024; 28:390-398. [PMID: 38444376 DOI: 10.1111/1744-9987.14113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
INTRODUCTION This study aims to analyze the risk factors for severe fatigue in maintenance hemodialysis (MHD) patients and develop a clinical prediction model to help doctors and patients prevent severe fatigue. METHODS Multicentre MHD patients were included in this study. The objective was to investigate the risk factors for severe fatigue in MHD patients and develop a prediction model. RESULTS A total of 243 MHD patients were included in the study, and the incidence of severe fatigue was found to be 20.99%. Using age, body mass index, total cholesterol, and albumin levels, a predictive nomogram for fatigue was constructed. In the training set, the nomogram had an area under the curve of 0.851, sensitivity of 82.86%, specificity of 81.76%, and c-index of 0.851. The nomogram was accurate in calibration and proved to be clinically useful. CONCLUSION The nomogram developed in this study is a practical and reliable tool for quickly identifying severe fatigue in MHD patients.
Collapse
Affiliation(s)
- Xinyuan Zhou
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Jiaxing, Zhejiang, China
- Department of Nephrology, The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China
| | - Jiahui Han
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Jiaxing, Zhejiang, China
- Department of Nephrology, The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China
| | - Fuxiang Zhu
- Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, China
| |
Collapse
|
2
|
Chen Q, Hu H, She Y, He Q, Huang X, Shi H, Cao X, Zhang X, Xu Y. An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus. Sci Rep 2024; 14:2197. [PMID: 38273015 PMCID: PMC10810925 DOI: 10.1038/s41598-024-52550-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 01/19/2024] [Indexed: 01/27/2024] Open
Abstract
Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.
Collapse
Affiliation(s)
- Qingquan Chen
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Haiping Hu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Yuanyu She
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qing He
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xinfeng Huang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Huanhuan Shi
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiangyu Cao
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiaoyang Zhang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China.
- School of Public Health, Fujian Medical University, Fuzhou, China.
| | - Youqiong Xu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China.
- School of Public Health, Fujian Medical University, Fuzhou, China.
| |
Collapse
|