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Clinical Characteristics and Predicting Disease Severity in Chlamydia psittaci Infection Based on Metagenomic
Mingzhu Huang1,2,3, Yuefeng Wang1,4, Yun Lu1,5
1Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Introduction:
Psittacosis pneumonia, as a zoonotic infection, is induced by the pathogen Chlamydia psittaci. In the present study, we sought to characterize the clinical manifestations and prognosticate the severity of psittacosis pneumonia.
Methods:
We retrospectively verified instances of psittacosis pneumonia in Zhejiang province, China, from January 2021 to April 2024. Relevant data pertaining to epidemiological, clinical, and laboratory aspects were compiled and evaluated.
Results:
Among a total of 110 individuals enrolled who were diagnosed with psittacosis pneumonia, the median age being 62.0 years (IQR, 53-69 years). The most common comorbidities were hypertension (36.4%) and diabetes mellitus (17.3%). Patients categorized as having severe disease (n=68) were significantly older than those with mild disease (n=42). Most patients had notable elevations in aspartate aminotransferase (AST), creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), D-dimer, C-reactive protein (CRP), procalcitonin, total bilirubin (TBil), and interleukin-6, as along with significant reductions in lymphocytes, monocytes, albumin, and interleukin-4. Chest CT scans showed bilateral lung involvement in 70 cases. In the cohort of patients having received empirical antibiotic therapy, 57.3% had their antibacterial medication adjusted in light of the mNGS findings. mNGS results indicated that 31.8% (35/110) had suspected coinfections. The random forest classifiers based upon the clinical and laboratory characteristics attained AUC values of 0.822.
Discussion:
The study underscores the efficacy of mNGS as a robust diagnostic tool for detecting Chlamydia psittaci, which can simultaneously detect other pathogens and guide clinical treatment. Severe patients exhibit significant inflammatory imbalances and lymphocyte depletion. A predictive model based on clinical and laboratory data at admission can effectively guide early clinical intervention.
Insights
This study characterizes psittacosis pneumonia, a zoonotic infection. A predictive model using clinical and lab data can guide early intervention for severe cases, with next-generation sequencing proving effective for diagnosis.
Area of Science:
- Infectious Diseases
- Pulmonology
- Clinical Microbiology
Background:
- Psittacosis pneumonia is a zoonotic infection caused by Chlamydia psittaci.
- Understanding its clinical features and prognostic factors is crucial for effective management.
Purpose of the Study:
- To characterize the clinical manifestations of psittacosis pneumonia.
- To develop a predictive model for disease severity.
- To evaluate the utility of next-generation sequencing (mNGS) in diagnosis and treatment guidance.
Main Methods:
- Retrospective analysis of 110 psittacosis pneumonia cases in Zhejiang province, China (Jan 2021-Apr 2024).
- Compilation and evaluation of epidemiological, clinical, and laboratory data.
- Development of random forest classifiers for severity prediction.
Main Results:
- Severe psittacosis pneumonia patients were older and exhibited significant inflammatory marker elevations and lymphocyte depletion.
- Next-generation sequencing (mNGS) identified Chlamydia psittaci and potential coinfections in 31.8% of cases.
- A predictive model achieved an AUC of 0.822 for severity prognostication.
Conclusions:
- mNGS is effective for diagnosing Chlamydia psittaci and identifying coinfections, guiding treatment.
- Severe disease is associated with profound inflammatory response and lymphopenia.
- Clinical and laboratory data at admission can predict severity, enabling early intervention.
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