Liu Xuefei, Zhao Qianqian, He Xiangyan, Min Jiumeng, Yao Rosary Sin Yu, Chen Zhonglin, Ma Jinmin, Hu Weiting, Huang Jingwen, Wan Huanying, Guo Yi, Zhou Min
Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Thorac Dis. 2024 Aug 31;16(8):5262-5273. doi: 10.21037/jtd-24-490. Epub 2024 Aug 16.
The microbial signatures in diabetes with pneumonia and the risk factors of severe pneumonia (SP) in diabetic patients are not clear. Our study explored microbial signatures and the association between clinical characteristics and SP then constructed a risk model to find effective biomarkers for predicting pneumonia severity.
Our study was conducted among 273 patients with pneumonia diagnosed and treated in our hospital from January 2018 to May 2021. Bronchoalveolar lavage fluid (BALF) samples and clinical data were collected. Metagenomic sequencing was applied after extracting the DNA from samples. Appropriate statistical methods were used to compare the microbial signatures and clinical characteristics in patients with or without diabetes mellitus (DM).
In total, sixty-one pneumonia patients with diabetes and 212 pneumonia patients without diabetes were included. Sixty-six differential microorganisms were found to be associated with SP in diabetic patients. Some microbes correlated with clinical indicators of SP. The prediction model for SP was established and the receiver operating characteristic (ROC) curve demonstrated its accuracy, with the sensitivity and specificity of 0.82 and 0.91, respectively.
Some microorganisms affect the severity of pneumonia. We identified the microbial signatures in the lower airways and the association between clinical characteristics and SP. The predictive model was more accurate in predicting SP by combining microbiological indicators and clinical characteristics, which might be beneficial to the early identification and management of patients with SP.
糖尿病合并肺炎患者的微生物特征以及糖尿病患者发生重症肺炎（SP）的危险因素尚不清楚。我们的研究探讨了微生物特征以及临床特征与SP之间的关联，然后构建了一个风险模型以寻找预测肺炎严重程度的有效生物标志物。
我们的研究在2018年1月至2021年5月期间在我院诊断和治疗的273例肺炎患者中进行。收集支气管肺泡灌洗液（BALF）样本和临床数据。从样本中提取DNA后应用宏基因组测序。使用适当的统计方法比较糖尿病（DM）患者和非糖尿病患者的微生物特征及临床特征。
共纳入61例糖尿病肺炎患者和212例非糖尿病肺炎患者。发现66种差异微生物与糖尿病患者的SP相关。一些微生物与SP的临床指标相关。建立了SP的预测模型，受试者工作特征（ROC）曲线显示了其准确性，敏感性和特异性分别为0.82和0.91。
一些微生物会影响肺炎的严重程度。我们确定了下呼吸道的微生物特征以及临床特征与SP之间的关联。通过结合微生物学指标和临床特征，预测模型在预测SP方面更准确，这可能有助于SP患者的早期识别和管理。
