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.

The likelihood of severe COVID-19 outcomes among PLHIV with various comorbidities: a comparative frequentist and Bayesian meta-analysis approach.

INTRODUCTION: The SARS-CoV-2 virus can currently pose a serious health threat and can lead to severe COVID-19 outcomes, especially for populations suffering from comorbidities. Currently, the data available on the risk for severe COVID-19 outcomes due to an HIV infection with or without comorbidities paint a heterogenous picture. In this meta-analysis, we summarized the likelihood for severe COVID-19 outcomes among people living with HIV (PLHIV) with or without comorbidities. METHODS: Following PRISMA guidelines, we utilized PubMed, Web of Science and medRxiv to search for studies describing COVID-19 outcomes in PLHIV with or without comorbidities up to 25 June 2021. Consequently, we conducted two meta-analyses, based on a classic frequentist and Bayesian perspective of higher quality studies. RESULTS AND DISCUSSION: We identified 2580 studies (search period: January 2020-25 June 2021, data extraction period: 1 January 2021-25 June 2021) and included nine in the meta-analysis. Based on the frequentist meta-analytical model, PLHIV with diabetes had a seven times higher risk of severe COVID-19 outcomes (odd ratio, OR = 6.69, 95% CI: 3.03-19.30), PLHIV with hypertension a four times higher risk (OR = 4.14, 95% CI: 2.12-8.17), PLHIV with cardiovascular disease an odds ratio of 4.75 (95% CI: 1.89-11.94), PLHIV with respiratory disease an odds ratio of 3.67 (95% CI: 1.79-7.54) and PLHIV with chronic kidney disease an OR of 9.02 (95% CI: 2.53-32.14) compared to PLHIV without comorbidities. Both meta-analytic models converged, thereby providing robust summative evidence. The Bayesian meta-analysis produced similar effects overall, with the exclusion of PLHIV with respiratory diseases who showed a non-significant higher risk to develop severe COVID-19 outcomes compared to PLHIV without comorbidities. CONCLUSIONS: Our meta-analyses show that people with HIV, PLHIV with coexisting diabetes, hypertension, cardiovascular disease, respiratory disease and chronic kidney disease are at a higher likelihood of developing severe COVID-19 outcomes. Bayesian analysis helped to estimate small sample biases and provided predictive likelihoods. Clinical practice should take these risks due to comorbidities into account and not only focus on the HIV status alone, vaccination priorities should be adjusted accordingly.