Clinical Characteristics and Predicting Disease Severity in Chlamydia psittaci Infection Based on Metagenomic Next-Generation Sequencing.

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.