Clinical features and a prediction model for early prediction of composite outcome in Chlamydia psittaci pneumonia: a multi-centre retrospective study in China

Abstract

Abstract Introduction: There has been growing concern about Chlamydia psittaci (C. psittaci ) pneumonia in recent years. C.psittaci pneumonia has atypical clinical manifestations and often ignored by clinicians. This study analyzed the clinical characteristics, explored the risk factors for composite outcome and established a prediction model for early predicting the risk of composite outcome among C.psittaci pneumonia patients.Methods We carried out a retrospective, observational cohort study in ten Chinese tertiary hospitals to investigate C. psittaci pneumonia. Only patients with confirmed cases of the disease were included, and their epidemiologic and clinical data were thoroughly collected and analyzed. The composite outcome of C. psittaci pneumonia was define as died 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. Additionally, we evaluated the model performance using various methods, including receiver operating characteristics (ROC), C-index, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and clinical impact curve analysis (CICA).Results In total, 119 C. psittaci pneumonia patients were included in the study. The patients were randomly divided into training (n = 83) and validation (n = 36) 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 cell. These factors were employed to construct model 2. The model 2 had acceptable discrimination (AUC of 0.898 and 0.825 for the training and validation sets, respectively) and robust internal validity. Calibration plot demonstrated good agreement between the predicted and the actual composite outcome rate. In the training set, the specificity, sensitivity, NPV, and PPV for predicting composite outcome in nomogram model were 91.7%, 84.5%, 50.0%, and 98.4%, respectively. In the internal validation set, these values were 100.0%, 64.7%, 14.2%, and 100.0%, respectively. DCA and CICA showed that the nomogram model was clinically practical.Conclusions We developed 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 improved clinical outcomes.