脊髓损伤后低钠血症风险估计提名图的开发与验证:一项回顾性单中心研究。

Qian Wei, Xuefeng Lu, Zihong Yang, Jichong Zhu, Jie Jiang, Yaobin Xu, Fengxin Li, Haifeng Bu, Yikai Chen, Sijing Tuo, Ruyu Chen, Xiaoxia Ye, Laoyi Geer, Xiuwei Tan, Jiling Wang, Yanlan Wu, Fangming Song, Yiji Su
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引用次数: 0

摘要

目的本研究旨在建立一种基于提名图的评估方法,用于预测脊髓损伤(SCI)后发生低钠血症的风险。方法我们进行了一项回顾性临床研究,收集了2016年至2020年在广西医科大学第一附属医院住院治疗的SCI患者。根据临床评分将SCI患者分为低钠血症和非低钠血症两组,将2016-2019年的SCI患者确定为训练集,将2020年的患者确定为测试集。结果共检索到 895 例 SCI 患者。在排除数据不完整的患者后,最终有 883 名患者被纳入本研究,并用于构建提名图。提名图中使用的指标包括性别、SCI 的完整性、肺炎、尿路感染、发热、便秘、白细胞(WBC)、白蛋白和血清 Ca2+。这些指数是通过最小绝对收缩和选择算子(LASSO)回归分析确定的。该模型的 C 指数为 0.81,训练集的曲线下面积(AUC)为 0.82(Cl:0.79-0.85),验证集为 0.79(Cl:0.73-0.85)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study.
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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