Huang Dong, He Dingxiu, Gong Linjing, Jiang Wei, Yao Rong, Liang Zongan
Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Department of Emergency Medicine, The People's Hospital of Deyang, Deyang, Sichuan, People's Republic of China.
J Inflamm Res. 2024 Mar 8;17:1549-1560. doi: 10.2147/JIR.S454992. eCollection 2024.
There is no predictive tool developed for pneumonia-associated acute respiratory distress syndrome (ARDS) specifically so far, and the clinical risk classification of these patients is not well defined. Our study aims to construct an early prediction model for hospital mortality in patients with pneumonia-associated ARDS.
In this single-center retrospective study, consecutive patients with pneumonia-associated ARDS admitted into intensive care units (ICUs) in West China Hospital of Sichuan University in China between January 2012 and December 2018 were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and then multivariate logistic regression analysis were used to identify independent predictors which were used to develop a nomogram. We evaluated the performance of differentiation, calibration, and clinical utility of the nomogram.
The included patients were divided into the training cohort (442 patients) and the testing cohort (190 patients) with comparable baseline characteristics. The independent predictors for hospital mortality included age (OR: 1.04; 95% CI: 1.02, 1.05), chronic cardiovascular diseases (OR: 2.62; 95% CI: 1.54, 4.45), chronic respiratory diseases (OR: 1.87; 95% CI: 1.02, 3.43), lymphocytes (OR: 0.56; 95% CI: 0.39, 0.81), albumin (OR: 0.94; 95% CI: 0.90, 1.00), creatinine (OR: 1.00; 95% CI: 1.00, 1.01), D-dimer (OR: 1.06; 95% CI: 1.03, 1.09) and procalcitonin (OR: 1.14; 95% CI: 1.07, 1.22). A web-based dynamic nomogram (https://h1234.shinyapps.io/dynnomapp/) was constructed based on these factors. The concordance index (C index) of the nomogram was 0.798 (95% CI: 0.756, 0.840) in the training cohort and 0.808 (95% CI: 0.747, 0.870) in testing cohort. The precision-recall (PR) curves, calibration curves, decision curve analyses (DCA) and clinical impact curves showed that the nomogram has good predictive value and clinical utility.
We developed and evaluated a convenient nomogram consisting of 8 clinical characteristics for predicting mortality in patients with pneumonia-associated ARDS.
目前尚未专门开发出用于肺炎相关急性呼吸窘迫综合征(ARDS)的预测工具,这些患者的临床风险分类也未明确界定。我们的研究旨在构建一个预测肺炎相关ARDS患者院内死亡率的早期预测模型。
在这项单中心回顾性研究中,纳入了2012年1月至2018年12月期间在中国四川大学华西医院重症监护病房(ICU)收治的连续肺炎相关ARDS患者。采用最小绝对收缩和选择算子(LASSO)回归,然后进行多因素逻辑回归分析,以确定独立预测因素,并用于开发列线图。我们评估了列线图的区分度、校准度和临床实用性。
纳入患者被分为训练队列(442例患者)和测试队列(190例患者),两者基线特征具有可比性。院内死亡的独立预测因素包括年龄(OR:1.04;95%CI:1.02,1.05)、慢性心血管疾病(OR:2.62;95%CI:1.54,4.45)、慢性呼吸系统疾病(OR:1.87;95%CI:1.02,3.43)、淋巴细胞(OR:0.56;95%CI:0.39,0.81)、白蛋白(OR:0.94;95%CI:0.90,1.00)、肌酐(OR:1.00;95%CI:1.00,1.01)、D-二聚体(OR:1.06;95%CI:1.03,1.09)和降钙素原(OR:1.14;95%CI:1.07,1.22)。基于这些因素构建了一个基于网络的动态列线图(https://h1234.shinyapps.io/dynnomapp/)。训练队列中列线图的一致性指数(C指数)为0.798(95%CI:0.756,0.840),测试队列中为0.808(95%CI:0.747,0.870)。精确召回(PR)曲线、校准曲线、决策曲线分析(DCA)和临床影响曲线表明,该列线图具有良好的预测价值和临床实用性。
我们开发并评估了一个由8个临床特征组成的便捷列线图,用于预测肺炎相关ARDS患者的死亡率。