Lv Lu, Shao Xinyue, Cui Enhai
Department of Respiratory and Critical Care Medicine, Huzhou Central Hospital, Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, People's Republic of China.
Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.
J Inflamm Res. 2024 May 8;17:2825-2834. doi: 10.2147/JIR.S458690. eCollection 2024.
Community-acquired pneumonia (CAP) is a global health concern due to its high rates of morbidity and mortality. Bacterial pathogens are common causes of CAP. It is one of the most common causes of acute respiratory distress syndrome (ARDS), a common severe respiratory system manifestation threatening human health. This study aimed to establish a predictive model for ARDS in patients with bacterial pneumonia, which was conducive to early identification of the occurrence and effective prevention of ARDS.
We collected the clinical data of hospitalized patients with bacterial pneumonia in Affiliated Huzhou Hospital of Zhejiang University School of Medicine from January 2022 to November 2022. The independent risk factors for ARDS in patients with bacterial pneumonia were determined by univariate and multivariate binary logistic regression analyses. The nomogram was constructed to display the predictive model, and the receiver-operating characteristic curve was plotted to evaluate the predictive value of ARDS.
This study included 254 patients with bacterial pneumonia, of which 114 developed ARDS. The multivariate logistic regression analysis revealed age [odds ratio (OR) = 1.041, = 0.003], heart rate (OR = 1.020, = 0.028), lymphocyte count (OR = 0.555, = 0.033), white blood cell count (OR = 1.062, = 0.033), bilateral lung lesions (OR = 7.352, = 0.011) and pleural effusion (OR = 2.512, = 0.002) as the independent risk factors for ARDS. The predictive model was constructed based on the six independent factors, which was valuable in predicting ARDS with area under the curve of 0.794.
The predictive model was beneficial to evaluate the disease progression in patients with bacterial pneumonia and identify ARDS. Further, our nomogram might help doctors predict the incidence of ARDS and conduct treatment as early as possible.
社区获得性肺炎（CAP）因其高发病率和死亡率而成为全球关注的健康问题。细菌病原体是CAP的常见病因。它是急性呼吸窘迫综合征（ARDS）最常见的病因之一，ARDS是一种威胁人类健康的常见严重呼吸系统表现。本研究旨在建立细菌性肺炎患者发生ARDS的预测模型，有助于早期识别ARDS的发生并有效预防ARDS。
收集2022年1月至2022年11月浙江大学医学院附属湖州医院住院的细菌性肺炎患者的临床资料。通过单因素和多因素二元逻辑回归分析确定细菌性肺炎患者发生ARDS的独立危险因素。构建列线图以展示预测模型，并绘制受试者工作特征曲线以评估ARDS的预测价值。
本研究纳入254例细菌性肺炎患者，其中114例发生ARDS。多因素逻辑回归分析显示年龄[比值比（OR）=1.041，P = 0.003]、心率（OR = 1.020，P = 0.028）、淋巴细胞计数（OR = 0.555，P = 0.033）、白细胞计数（OR = 1.062，P = 0.033）、双侧肺部病变（OR = 7.352，P = 0.011）和胸腔积液（OR = 2.512，P = 0.002）是ARDS的独立危险因素。基于这六个独立因素构建了预测模型，其在预测ARDS方面具有价值，曲线下面积为0.794。
该预测模型有助于评估细菌性肺炎患者的疾病进展并识别ARDS。此外，我们的列线图可能有助于医生预测ARDS的发生率并尽早进行治疗。
