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用于预测社区获得性肺炎和下呼吸道感染不良医疗结局的前体药物。

Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respiratory tract infections.

机构信息

Department of Internal Medicine, Division of Endocrinology, Diabetes and Clinical Nutrition, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.

出版信息

Crit Care. 2010;14(3):R106. doi: 10.1186/cc9055. Epub 2010 Jun 8.

Abstract

INTRODUCTION

Measurement of prohormones representing different pathophysiological pathways could enhance risk stratification in patients with community-acquired pneumonia (CAP) and other lower respiratory tract infections (LRTI).

METHODS

We assessed clinical parameters and five biomarkers, the precursor levels of adrenomedullin (ADM), endothelin-1 (ET1), atrial-natriuretic peptide (ANP), anti-diuretic hormone (copeptin), and procalcitonin in patients with LRTI and CAP enrolled in the multicenter ProHOSP study. We compared the prognostic accuracy of these biomarkers with the pneumonia severity index (PSI) and CURB65 (Confusion, Urea, Respiratory rate, Blood pressure, Age 65) score to predict serious complications defined as death, ICU admission and disease-specific complications using receiver operating curves (ROC) and reclassification methods.

RESULTS

During the 30 days of follow-up, 134 serious complications occurred in 925 (14.5%) patients with CAP. Both PSI and CURB65 overestimated the observed mortality (X2 goodness of fit test: P = 0.003 and 0.01). ProADM or proET1 alone had stronger discriminatory powers than the PSI or CURB65 score or any of either score components to predict serious complications. Adding proADM alone (or all five biomarkers jointly) to the PSI and CURB65 scores, significantly increased the area under the curve (AUC) for PSI from 0.69 to 0.75, and for CURB65 from 0.66 to 0.73 (P < 0.001, for both scores). Reclassification methods also established highly significant improvement (P < 0.001) for models with biomarkers if clinical covariates were more flexibly adjusted for. The developed prediction models with biomarkers extrapolated well if evaluated in 434 patients with non-CAP LRTIs.

CONCLUSIONS

Five biomarkers from distinct biologic pathways were strong and specific predictors for short-term adverse outcome and improved clinical risk scores in CAP and non-pneumonic LRTI. Intervention studies are warranted to show whether an improved risk prognostication with biomarkers translates into a better clinical management and superior allocation of health care resources.

TRIAL REGISTRATION

NCT00350987.

摘要

简介

测量代表不同病理生理途径的前激素可以提高社区获得性肺炎(CAP)和其他下呼吸道感染(LRTI)患者的风险分层。

方法

我们评估了临床参数和 5 种生物标志物,即肾上腺髓质素(ADM)、内皮素-1(ET1)、心房利钠肽(ANP)、抗利尿激素(copeptin)和降钙素原在多中心 ProHOSP 研究中招募的 LRTI 和 CAP 患者中的前体水平。我们比较了这些生物标志物与肺炎严重指数(PSI)和 CURB65(意识模糊、尿素、呼吸频率、血压、年龄 65 岁)评分预测严重并发症的准确性,严重并发症定义为死亡、入住 ICU 和特定疾病并发症,并使用接收者操作曲线(ROC)和再分类方法。

结果

在 30 天的随访期间,925 例 CAP 患者中有 134 例发生严重并发症。PSI 和 CURB65 均高估了观察到的死亡率(X2 拟合优度检验:P = 0.003 和 0.01)。单独使用 proADM 或 proET1 比 PSI 或 CURB65 评分或任何评分成分更能预测严重并发症。将 proADM 单独添加(或所有 5 种生物标志物联合添加)到 PSI 和 CURB65 评分中,显著提高了 PSI 的曲线下面积(AUC)从 0.69 到 0.75,CURB65 从 0.66 到 0.73(P < 0.001,两个评分)。如果更灵活地调整临床协变量,则重新分类方法也为具有生物标志物的模型建立了高度显著的改善(P < 0.001)。在 434 例非 CAP 下呼吸道感染患者中评估的开发预测模型具有良好的外推性。

结论

来自不同生物途径的 5 种生物标志物是 CAP 和非肺炎性 LRTI 短期不良预后的强而特异的预测因子,并改善了临床风险评分。需要进行干预研究,以表明生物标志物改善风险预测是否转化为更好的临床管理和更好的医疗资源分配。

试验注册

NCT00350987。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39e0/2911752/1bd5f2a03b25/cc9055-1.jpg

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