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Clinical Features and a Prediction Model for Early Prediction of Composite Outcome in Chlamydia psittaci Pneumonia: A
Xue Yang1, Man Wu2, Tangzhiming Li3
1Shenzhen 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.
Introduction:
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.
Methods:
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).
Results:
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.
Conclusion:
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.
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