Predictive values of serological biomarkers in progress and prognosis of acute respiratory distress syndrome

Ran WANG; Qiao ZHANG; Qianli MA; Xu YANG; Shengtao ZHAO; Yijie WANG; Zhaoxia DENG; Minghua LIU; Dongpo JIANG; Jian ZHOU; Qi LI; Changzheng WANG

doi:10.16016/j.1000-5404.201703114

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Predictive values of serological biomarkers in progress and prognosis of acute respiratory distress syndrome

Clinical Medicine | 更新时间：2026-01-09

- Predictive values of serological biomarkers in progress and prognosis of acute respiratory distress syndrome
- Journal of Third Military Medical University Vol. 39, Issue 19, Pages: 1926-1932(2017)
- 作者机构：
  
  1.400037 重庆，第三军医大学新桥医院呼吸科，全军呼吸内科研究所
  2.400038 重庆，第三军医大学西南医院急诊科
  3.400042 重庆，第三军医大学大坪医院野战外科研究所重症监护室
- 作者简介：
- 基金信息：
  
  the Key Project of the "Twelfth Five-year Plan" of Medical Science Development of PLA(BWS12J035)
- DOI：10.16016/j.1000-5404.201703114
  CLC： R181.32;R446.11;R563.8
- Received：19 March 2017，
  
  Revised：2017-07-18，
  
  Published：15 October 2017
- 稿件说明：
移动端阅览
Ran WANG, Qiao ZHANG, Qianli MA, et al. Predictive values of serological biomarkers in progress and prognosis of acute respiratory distress syndrome[J]. Journal of Third Military Medical University, 2017, 39(19): 1926-1932.
DOI：

Ran WANG, Qiao ZHANG, Qianli MA, et al. Predictive values of serological biomarkers in progress and prognosis of acute respiratory distress syndrome[J]. Journal of Third Military Medical University, 2017, 39(19): 1926-1932. DOI： 10.16016/j.1000-5404.201703114.

摘要

目的

探讨血清学生物标志物对急性呼吸窘迫综合征(ARDS)早期诊断预警及预后判断的预测效果。

方法

采用多中心、前瞻队列研究的方法，从2014年1月开始纳入各中心收治的ARDS与ARDS高危病例。采集患者的人口学资料，检测患者在入组24 h内的血清学指标(sRAGE、CC16、Ang-2、sICAM-1、PAI-1、SuPAR、HMGB1)。采用Logistic回归分析对影响高危ARDS患者发展成为ARDS危险因素和影响ARDS预后的危险因素。用ROC曲线比较单个危险因素或联合多个危险因素对ARDS发生的预警及ARDS死亡预后的预测效果，计算其预测的灵敏度、特异度及约登指数。

结果

共纳入ARDS患者59例，ARDS高危患者41例。ARDS发生的独立危险因素有HMGB1、sICAM-1、Ang-2、CC16、PAI-1。其中HMGB1预测效果最好(AUC=0.908)，其次根据AUC大小顺序为CC16(AUC=0.861)，Ang-2(AUC=0.858)，PAI-1(AUC=0.801)，sICAM-1(AUC=0.773)。单指标分析中，敏感度最高的单指标为HMGB1与sICAM-1

皆为79.7%，特异度最高的指标为HMGB1、CC16与Ang-2，皆为87.8%。在联合预测中，HMGB1与Ang-2联合预测，特异度最高，为95.1%，其AUC也为最高(0.92)。HMGB1、Ang-2与PAI-1指标联合预测，敏感度最高，为89.9%。多指标联合预测，可以在一定程度上提高对ARDS诊断预测的灵敏度和特异度。对ARDS预后分析，sRAGE与Ang-2是其死亡的独立危险因素。sRAGE(AUC=0.791) 比Ang-2(AUC=0.67) 对ARDS患者死亡预测效果好。两指标联合可以提高ARDS死亡预测的特异性。

结论

在ARDS高危患者中，HMGB1、sICAM-1、Ang-2、CC16、PAI-1是其发展成为ARDS的独立危险因素。HMGB1与Ang-2联合预测有较好的特异性，HMGB1、Ang-2与PAI-1联合预测有较好的敏感性。对于ARDS患者，sRAGE与Ang-2的增高预示着ARDS患者预后不良，两者联合预测可以提高预测的特异性。

Abstract

Objective

To investigate the predictive effect of serological biomarkers in early diagnosis and prognosis of acute respiratory distress syndrome (ARDS).

Methods

A multicenter

prospective cohort study was conducted from January 2014 to enroll ARDS cases and cases at high risk of ARDS in each center. The patients' demographic data were collected and the serological biomarkers

including soluble receptor of advanced glycosylation end products (sRAGE)

Clara cell protein (CC16)

angiopoietin-2 (Ang-2)

soluble intercellular adhesion molecule 1 (sICAM-1)

plasminogen activator inhibitor-1 (PAI-1)

soluble urokinase plasminogen activator receptor (sUPAR)

and high-mobility group box-1 (HMGB1) were tested within 24 h after admission. Logistic regression analysis was used to recognize the risk factors for accelerating the progress of the patients at high risk to ARDS and the factors influencing the prognosis of ARDS. Receiver operating characteristic (ROC) curve was used to compare the predictive effects of single factor or combination of multiple factors in early warning and outcome of ARDS for the sensitivity

specificity and Youden index.

Results

A total of 59 ARDS patients and 41 patients at high risk of ARDS were enrolled in this study. The independent risk factors for ARDS were HMGB1

sICAM-1

Ang-2

CC16 and PAI-1

and HMGB1 showed the best predictive effect with area under curve (AUC) of 0.908

followed by CC16 (0.861)

Ang-2 (0.858)

PAI-1 (0.801)

and sICAM-1 (0.773). In the single index analysis

the most sensitive indexes were HMGB1 and sICAM-1

both with the sensitivity of 79.7%

and the highest specific indexes were HMGB1

CC16 and Ang-2

all with the specificity of 87.8%. In the combined prediction

HMGB1 combined with Ang-2 had the highest specificity of 95.1% and the highest AUC of 0.92. The sensitivity of combining HMGB-1

Ang-2 and PAI-1 (89.9%) was the highest to predict. Multi-index combined prediction

to a certain extent

improved the sensitivity and specificity of diagnosis and prediction. sRAGE and Ang-2 were independent risk factors for death in ARDS prognosis. sRAGE (AUC=0.79) was better than Ang-2 (AUC=0.67) in predicting the death of ARDS patients. While the combination of the 2 indicators improved the specificity of the prediction.

Conclusion

HMGB1

sICAM-1

Ang-2

CC16 and PAI-1 are independent risk factors for patients at high risk of developing into ARDS. HMGB1 combined with Ang-2 shows better specificity

and combined with Ang-2 and PAI-1 together has better sensitivity in the prediction. For ARDS patients

increased sRAGE and Ang-2 indicate a poor prognosis in ARDS patients

and combination of these 2 indexes can improve the specificity of prediction.

关键词

Keywords

references

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