Yang Xue, Wu Man, Li Tangzhiming, Yu Jie, Fu Tian, Li Guoping, Xiong Huanwen, Liao Gang, Zhang Sensen, Li Shaofeng, Zeng Zhonghua, Chen Chun, Liang Benhui, Zhou Zhiguo, Lu Ming
Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People's Republic of China.
Department of Respiratory and Critical Care Medicine, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, People's Republic of China.
Infect Drug Resist. 2024 Sep 6;17:3913-3923. doi: 10.2147/IDR.S431543. eCollection 2024.
C. psittaci pneumonia has atypical clinical manifestations and is often ignored by clinicians. This study analyzed the clinical characteristics, explored the risk factors for composite outcome and established a prediction model for early prediction of composite outcome among C. psittaci pneumonia patients.
A multicenter, retrospective, observational cohort study was conducted in ten Chinese tertiary hospitals. Patients diagnosed with C. psittaci pneumonia were included, and their clinical data were collected and analyzed. The composite outcome of C. psittaci pneumonia included death during hospitalization, ICU admission, and mechanical ventilation. Univariate and multivariable logistic regression analyses were conducted to determine the significant variables. A ten-fold cross-validation was performed to internally validate the model. The model performance was evaluated using various methods, including receiver operating characteristics (ROC), C-index, sensitivity, specificity, positive/negative predictive value (PPV/NPV), decision curve analysis (DCA), and clinical impact curve analysis (CICA).
In total, 83 patients comprised training cohorts and 36 patients comprised validation cohorts. CURB-65 was used to establish predictive Model 1. Multivariate logistic regression analysis identified three independent prognostic factors, including serum albumin, CURB-65, and white blood cells. These factors were employed to construct model 2. Model 2 had acceptable discrimination (AUC of 0.898 and 0.825 for the training and validation sets, respectively) and robust internal validity. The specificity, sensitivity, NPV, and PPV for predicting composite outcome in the nomogram model were 91.7%, 84.5%, 50.0%, and 98.4% in the training sets, and 100.0%, 64.7%, 14.2%, and 100.0% in the validation sets. DCA and CICA showed that the nomogram model was clinically practical.
This study constructs a refined nomogram model for predicting the composite outcome in C. psittaci pneumonia patients. This nomogram model enables early and accurate C. psittaci pneumonia patients' evaluation, which may improve clinical outcomes.
鹦鹉热衣原体肺炎具有非典型临床表现，常被临床医生忽视。本研究分析了鹦鹉热衣原体肺炎患者的临床特征，探讨了复合结局的危险因素，并建立了早期预测鹦鹉热衣原体肺炎患者复合结局的预测模型。
在十家中国三级医院进行了一项多中心、回顾性、观察性队列研究。纳入诊断为鹦鹉热衣原体肺炎的患者，并收集和分析其临床数据。鹦鹉热衣原体肺炎的复合结局包括住院期间死亡、入住重症监护病房（ICU）和机械通气。进行单因素和多因素逻辑回归分析以确定显著变量。进行十折交叉验证以在内部验证模型。使用多种方法评估模型性能，包括受试者操作特征曲线（ROC）、C指数、敏感性、特异性、阳性/阴性预测值（PPV/NPV）、决策曲线分析（DCA）和临床影响曲线分析（CICA）。
总共83例患者组成训练队列，36例患者组成验证队列。使用CURB-65建立预测模型1。多因素逻辑回归分析确定了三个独立的预后因素，包括血清白蛋白、CURB-65和白细胞。这些因素用于构建模型2。模型2具有可接受的区分度（训练集和验证集的AUC分别为0.898和0.825）和稳健的内部效度。列线图模型预测复合结局的特异性、敏感性、NPV和PPV在训练集中分别为91.7%、84.5%、50.0%和98.4%，在验证集中分别为100.0%、64.7%、14.2%和100.0%。DCA和CICA表明列线图模型具有临床实用性。
本研究构建了一个用于预测鹦鹉热衣原体肺炎患者复合结局的精细列线图模型。该列线图模型能够对鹦鹉热衣原体肺炎患者进行早期准确评估，可能改善临床结局。
