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Wei Q, Lu X, Yang Z, Zhu J, Jiang J, Xu Y, Li F, Bu H, Chen Y, Tuo S, Chen R, Ye X, Geer L, Tan X, Wang J, Wu Y, Song F, Su Y. Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study. J Spinal Cord Med 2024:1-9. [PMID: 38656250 DOI: 10.1080/10790268.2024.2329437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN The study is a retrospective single-center study. PARTICIPANTS SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.
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Affiliation(s)
- Qian Wei
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xuefeng Lu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Zihong Yang
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Jichong Zhu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jie Jiang
- The Second Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yaobin Xu
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
| | - Fengxin Li
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Haifeng Bu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yikai Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Sijing Tuo
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Ruyu Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiaoxia Ye
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Laoyi Geer
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Xiuwei Tan
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Jiling Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Yanlan Wu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
| | - Fangming Song
- Graduate School of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
| | - Yiji Su
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China
- Guangxi Research Center for Regenerative Medicine, Nanning, People's Republic of China
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Fetherston SM, Geer L, Veazey RS, Goldman L, Murphy DJ, Ketas TJ, Klasse PJ, Blois S, La Colla P, Moore JP, Malcolm RK. Partial protection against multiple RT-SHIV162P3 vaginal challenge of rhesus macaques by a silicone elastomer vaginal ring releasing the NNRTI MC1220. J Antimicrob Chemother 2013. [DOI: 10.1093/jac/dkt087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Abstract
Three-dimensional structures are now known for roughly half of all protein families. It is thus quite likely, in searching sequence databases, that one will encounter a homolog with known structure and be able to use this information to infer structure-function properties. The goal of Entrez's 3D structure database is to make this information accessible and useful to molecular biologists. To this end, Entrez's search engine provides three powerful features: (i) Links between databases; one may search by term matching in Medline((R)), for example, and link to 3D structures reported in these articles. (ii) Sequence and structure neighbors; one may select all sequences similar to one of interest, for example, and link to any known 3D structures. (iii) Sequence and structure visualization; identifying a homolog with known structure, one may view a combined molecular-graphic and alignment display, to infer approximate 3D structure. Entrez's MMDB (Molecular Modeling DataBase) may be accessed at: http://www.ncbi.nlm.nih.gov/Entrez/structure.html
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Affiliation(s)
- Y Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Abstract
The three dimensional structures for representatives of nearly half of all protein families are now available in public databases. Thus, no matter which protein one investigates, it is increasingly likely that the 3D structure of a homolog will be known and may reveal unsuspected structure-function relationships. The goal of Entrez's 3D-structure database is to make this information accessible and usable by molecular biologists (http://www.ncbi.nlm.nih.gov/Entrez). To this end Entrez provides two major analysis tools, a search engine based on sequence and structure 'neighboring' and an integrated visualization system for sequence and structure alignments. From a protein's sequence 'neighbors' one may rapidly identify other members of a protein family, including those where 3D structure is known. By comparing aligned sequences and/or structures in detail, using the visualization system, one may identify conserved features and perhaps infer functional properties. Here we describe how these analysis tools may be used to investigate the structure and function of newly discovered proteins, using the PTEN gene product as an example.
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Affiliation(s)
- A Marchler-Bauer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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