背景:严重的社区获得性肺炎(SCAP)导致全球高死亡率和巨大的经济负担,然而,对与预后相关的生物特征的了解有限阻碍了临床结局的改善.病原体,微生物和宿主是炎症和感染的三个重要因素。本研究旨在发现特异性和敏感的生物标志物来预测SCAP患者的预后。
方法:在本研究中,我们将宏基因组和转录组联合筛选方法应用于多中心的275例SCAP患者的临床标本,前瞻性研究。
结果:我们发现30天死亡率可能与病原体类别或微生物多样性无关,而30天死亡率组和存活组之间的宿主基因表达模式存在显着差异。在我们的研究中确定了12个与结果相关的临床特征。评估了潜在的宿主反应,并富集了与细胞活化相关的基因,免疫调节,确定了炎症和代谢。值得注意的是,组学数据,整合临床特征和参数,以开发具有六个特征的模型,用于预测30天的死亡率,显示AUC为0.953(95%CI:0.92-0.98)。
结论:总之,我们的研究将SCAP患者的临床特征和基础多组学生物特征与不同结局联系起来.综合预测模型的建立将有助于未来SCAP治疗策略和预后的改善。
背景:国家自然科学基金(编号:82161138018),上海市重点临床专科(shslczdzk02202),上海市重点临床重点学科建设项目(2017ZZ02014),上海市突发事件预防重点实验室,呼吸道传染病的诊断和治疗(20dz2261100)。
BACKGROUND: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-
signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients.
METHODS: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study.
RESULTS: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six
signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98).
CONCLUSIONS: In summary, our study linked clinical characteristics and underlying multi-omics bio-
signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP.
BACKGROUND: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).