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

基于宏基因组二代测序的鹦鹉热衣原体感染的临床特征及疾病严重程度预测

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Abstract

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.

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