Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health; Center for Stroke Research Berlin, NeuroCure Clinical Research Center and Department of Neurology, Charité University Hospital Berlin, Germany (B.H., S.H., L.U., A.M.).
Friedrich Loeffler Institute of Medical Microbiology, University Medicine Greifswald, Germany (L.U.).
Stroke. 2021 Jan;52(1):325-330. doi: 10.1161/STROKEAHA.120.031884. Epub 2020 Dec 7.
Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores.
We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models.
The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05.
All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01264549 and NCT01079728.
已有多种临床评分系统和生物标志物被提出用于预测卒中相关性肺炎（SAP）。我们旨在对 SAP 评分进行外部和竞争验证，并假设 5 种选定的生物标志物将提高这些评分的性能。
我们汇集了两项急性卒中研究的临床数据，这些研究具有相同的数据评估：STRAWINSKI 和 PREDICT。生物标志物（超敏降钙素原；中区域促肾上腺皮质激素；中区域利钠肽；超敏 copeptin；C 端内皮素前体）从入院时的血清样本中进行测量。我们进行了文献检索，以确定 SAP 预测评分。然后，我们使用各个评分和生物标志物计算多元回归模型。使用受试者工作特征曲线下的面积来比较这些评分和模型的判别能力。
合并队列包括 683 例患者，其中 573 例有可用的备份样本进行生物标志物分析。文献检索确定了 9 种 SAP 预测评分。我们的数据集使我们能够计算其中的 5 种评分。这些评分对医生确定的 SAP 的受试者工作特征曲线下面积为 0.543 至 0.651，对可能的 SAP 为 0.574 至 0.685，对确定的 SAP 为 0.689 至 0.811，依据卒中相关性肺炎共识组的标准。评分与生物标志物的多元模型几乎改善了所有预测，但主要是在受试者工作特征曲线下面积的差值在 0.05 以内。
所有 SAP 预测评分都以良好到较强的能力识别出可能发生 SAP 的患者，在应用更严格的 SAP 诊断标准时，其具有更好的判别能力。选定的生物标志物仅提供了有限的附加预测价值，目前还没有理由将这些标志物添加到预测模型中。注册：网址：https://www.clinicaltrials.gov。唯一标识符：NCT01264549 和 NCT01079728。
