Wang Xinyu, Wei Chang, He Dingxiu, Huang Dong, Zhao Yuean, Ran Longyi, Wang Xinyuan, Yu He, Liang Zongan, Gong Linjing
Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China.
Department of Emergency Medicine, The People's Hospital of Deyang, Deyang, Sichuan, China.
BMC Infect Dis. 2025 May 2;25(1):649. doi: 10.1186/s12879-025-11027-w.
Sepsis represents a high-risk mortality cohort among patients with severe community-acquired pneumonia (SCAP). Rapid and precise identification along with prompt decision-making, serves as a practical approach to improve patient prognosis.
This retrospective observational study enrolled adult patients with severe community-acquired pneumonia (SCAP) who were continuously hospitalized in the intensive care unit (ICU) of West China Hospital, Sichuan University, from September 2011 to September 2019. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for co-sepsis, followed by the utilization of LASSO regression to filter features to establish a nomogram. Model robustness was evaluated via the C index, receiver operating characteristic (ROC) analysis, and calculation of the area under the curve (AUC). Furthermore, its predictive accuracy was assessed via decision curve analysis (DCA).
In total, 5855 SCAP patients were included in the present study, of whom 654 developed sepsis. Patients with sepsis exhibited a prolonged length of stay in the ICU and higher mortality rates, indicating a worse prognosis than those without sepsis. We identified 15 independent risk factors associated with the development of sepsis in SCAP patients. Further analysis incorporating 9 of these features to construct a nomogram demonstrated a C index of 0.722 (95%CI 0.702-0.742), including lactate, D-dimer, respiratory rate, heart rate, albumin, hemoglobin, activated partial thromboplastin time (APTT), glucose, and C-reactive protein (CRP) levels. The AUC values and DCA curves demonstrated that the model exhibited superior accuracy and overall net benefit in predicting co-sepsis development compared with the qSOFA, CURB-65, SOFA, and APACHE II scores. Additionally, the calibration curve confirmed good concordance between the predicted probabilities of the model.
This study investigated the risk factors for co-sepsis in SCAP patients and constructed an expedited, cost-effective and personalized model for predicting the probability of co-sepsis.
脓毒症是重症社区获得性肺炎（SCAP）患者中具有高死亡风险的群体。快速准确的识别以及及时的决策制定是改善患者预后的实用方法。
这项回顾性观察性研究纳入了2011年9月至2019年9月期间在四川大学华西医院重症监护病房（ICU）持续住院的成年重症社区获得性肺炎（SCAP）患者。采用单因素和多因素逻辑回归分析来确定合并脓毒症的独立危险因素，随后利用LASSO回归筛选特征以建立列线图。通过C指数、受试者工作特征（ROC）分析以及曲线下面积（AUC）计算来评估模型稳健性。此外，通过决策曲线分析（DCA）评估其预测准确性。
本研究共纳入5855例SCAP患者，其中654例发生脓毒症。脓毒症患者在ICU的住院时间延长且死亡率更高，表明其预后比未发生脓毒症的患者更差。我们确定了15个与SCAP患者发生脓毒症相关的独立危险因素。进一步纳入其中9个特征构建列线图的分析显示C指数为0.722（95%CI 0.702 - 0.742），包括乳酸、D - 二聚体、呼吸频率、心率、白蛋白、血红蛋白、活化部分凝血活酶时间（APTT）、血糖和C反应蛋白（CRP）水平。AUC值和DCA曲线表明，与qSOFA、CURB - 65、SOFA和APACHE II评分相比，该模型在预测合并脓毒症发生方面具有更高的准确性和总体净效益。此外，校准曲线证实了模型预测概率之间具有良好的一致性。
本研究调查了SCAP患者合并脓毒症的危险因素，并构建了一个快速、经济有效且个性化的模型来预测合并脓毒症的概率。
