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临床输血与检验

临床输血与检验 ›› 2023, Vol. 25 ›› Issue (2): 254-260.DOI: 10.3969/j.issn.1671-2587.2023.02.019

• 临床检验 • 上一篇    下一篇

尿酸和碱性磷酸酶的早期变化预测普通型新型冠状病毒肺炎患者的血脂异常*

王艺婷, 李雪文, 王艺霏, 周琪, 孙雪娟, 许建成   

  1. 130021 吉林大学第一医院检验科（王艺婷,李雪文,王艺霏,许建成）; 吉林大学第一医院新生儿科（周琪）; 长春传染病医院检验科（孙雪娟）
  • 收稿日期:2022-10-14 发布日期:2023-04-25
  • 通讯作者: 许建成,教授,主要从事临床检验诊断学研究,（E-mail）xjc@jlu.edu.cn。
  • 作者简介:王艺婷,主要从事临床检验诊断学研究,（E-mail）676797789@qq.com。

Early Changes in Uric Acid and Alkaline Phosphatase Can Predict Dyslipidemia in Patients with Moderate Corona Virus Disease 2019

WANG Yi-ting, LI Xue-wen, WANG Yi-fei, et al   

  1. Department of Clinical Laboratory, the First Hospital of Jilin University, Changchun, Jilin 130021
  • Received:2022-10-14 Published:2023-04-25

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摘要： 目的 利用普通型新型冠状病毒肺炎（COVID-19）患者实验室检测数据,建立其血脂异常的预测模型。方法 收集74例普通型COVID-19患者临床资料和入院时首次实验室检查结果。根据血脂水平分为正常组和异常组。利用单因素Logistic回归和共线性诊断确定患者血脂异常的风险因素,利用向后逐步回归建立列线图模型。用受试者工作特征（ROC）曲线、校准曲线和决策曲线评估模型的区分度、拟合优度与临床实用性。结果 入院时血脂异常的患者在出院时血脂水平均恢复正常。Logistic回归分析显示,尿酸（OR=1.024）和碱性磷酸酶（OR=1.417）可联合预测患者血脂异常（P<0.05）。ROC曲线显示,模型的区分度较高（曲线下面积为0.987）。校准曲线显示,模型具有较好的拟合程度（P=0.989）。决策曲线显示,模型具有一定的临床实用性。结论 本研究建立的尿酸与碱性磷酸酶的组合模型可较好的预测普通型COVID-19患者血脂异常。本研究处于初步探索阶段,可供相关研究参考,所建立模型的临床实用性尚需大样本数据进一步验证。

关键词: 新型冠状病毒肺炎, 血脂异常, 尿酸, 碱性磷酸酶, 预测模型

Abstract: Objective A prediction model for dyslipidemia was established based on laboratory data of patients with moderate coronavirus disease 2019 (COVID-19). Methods Clinical data and the results of the first laboratory examination of 74 patients with moderate COVID-19 at admission were collected. Patients were divided into normal and abnormal groups based on lipid levels. Univariate Logistic regression and colinear diagnosis were used to determine the risk factors of dyslipidemia, and backward stepwise regression was used to establish a line graph model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis were used to evaluate the model discrimination, goodness of fit, and clinical usefulness. Results The patients with dyslipidemia at admission returned to normal levels of blood lipid at discharge. Logistic regression analysis showed that uric acid (OR=1.024) and alkaline phosphatase (OR=1.417) were combined to predict dyslipidemia (P<0.05). The ROC curve showed that the model had a high degree of differentiation (area under the curve was 0.987). The calibration curve showed that the model had a good fitting degree (P=0.989). The decision curve showed that the mode had a good clinical usefulness. Conclusion The combined model of uric acid and alkaline phosphatase established in this study can better predict dyslipidemia in patients with moderate COVID-19. This study is in the preliminary exploratory stage, and the clinical usefulness of the model needs further validation with large sample data, which can be used as a reference for related studies.

Key words: Corona virus disease 2019, Dyslipidemia, Uric acid, Alkaline phosphatase, Prediction model

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