Atypical pneumonia caused by Chlamydia psittaci during the COVID-19 pandemic.

OBJECTIVES: Here, we retrospectively described the diagnosis and treatment of 32 cases diagnosed with Chlamydia psittaci pneumonia during the COVID-19 pandemic. METHODS: Clinical information was collected from all the patients. Reverse transcription-PCR and ELISAs were conducted for the detection of COVID-19 using nasal swabs and bronchoalveolar lavage fluid (BALF) samples. Metagenomic next-generation sequencing (mNGS) was performed for the identification of causative pathogens using BALF, peripheral blood and sputum samples. End-point PCR was performed to confirm the mNGS results. RESULTS: All 32 patients showed atypical pneumonia and had infection-like symptoms that were similar to COVID-19. Results of reverse transcription-PCR and ELISAs ruled out COVID-19 infection. mNGS identified C. psittaci as the suspected pathogen in these patients within 48 hours, which was validated by PCR, except for three blood samples. The sequence reads that covered fragments of C. psittaci genome were detected more often in BALF than in sputum or blood samples. All patients received doxycycline-based treatment regimens and showed favorable outcomes. CONCLUSION: This retrospective study, with the highest number of C. psittaci pneumonia enrolled cases in China so far, suggests that human psittacosis may be underdiagnosed and misdiagnosed clinically, especially in the midst of the COVID-19 pandemic.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.