Atypical bacterial co-infections among patients with COVID-19: A study from India.

Emerging evidence shows co-infection with atypical bacteria in coronavirus disease 2019 (COVID-19) patients. Respiratory illness caused by atypical bacteria such as Mycoplasma pneumoniae, Chlamydia pneumoniae, and Legionella pneumophila may show overlapping manifestations and imaging features with COVID-19 causing clinical and laboratory diagnostic issues. We conducted a prospective study to identify co-infections with SARS-CoV-2 and atypical bacteria in an Indian tertiary hospital. From June 2020 to January 2021, a total of 194 patients with laboratory-confirmed COVID-19 were also tested for atypical bacterial pathogens. For diagnosing M. pneumoniae, a real-time polymerase chain reaction (PCR) assay and serology (IgM ELISA) were performed. C. pneumoniae diagnosis was made based on IgM serology. L. pneumophila diagnosis was based on PCR or urinary antigen testing. Clinical and epidemiological features of SARS-CoV-2 and atypical bacteria-positive and -negative patient groups were compared. Of the 194 patients admitted with COVID-19, 17 (8.8%) were also diagnosed with M. pneumoniae (n = 10) or C. pneumoniae infection (n = 7). Confusion, headache, and bilateral infiltrate were found more frequently in the SARS CoV-2 and atypical bacteria co-infection group. Patients in the M. pneumoniae or C. pneumoniae co-infection group were more likely to develop ARDS, required ventilatory support, had a longer hospital length of stay, and higher fatality rate compared to patients with only SARS-CoV-2. Our report highlights co-infection with bacteria causing atypical pneumonia should be considered in patients with SARS-CoV-2 depending on the clinical context. Timely identification of co-existing pathogens can provide pathogen-targeted treatment and prevent fatal outcomes of patients infected with SARS-CoV-2 during the current pandemic.

Atypical bacterial co-infections among patients with COVID-19: A study from India

Abstract Emerging evidence shows co-infection with atypical bacteria in coronavirus disease 2019 (COVID-19) patients. Respiratory illness caused by atypical bacteria such as Mycoplasma pneumoniae, Chlamydia pneumoniae, and Legionella pneumophila may show overlapping manifestations and imaging features with COVID-19 causing clinical and laboratory diagnostic issues. We conducted a prospective study to identify co-infections with SARS-CoV-2 and atypical bacteria in an Indian tertiary hospital. From June 2020 to January 2021, a total of 194 patients with laboratory-confirmed COVID-19 were also tested for atypical bacterial pathogens. For diagnosing M. pneumoniae, a real-time polymerase chain reaction (PCR) assay and serology (IgM ELISA) were performed. C. pneumoniae diagnosis was made based on IgM serology. L. pneumophila diagnosis was based on PCR or urinary antigen testing. Clinical and epidemiological features of SARS-CoV-2 and atypical bacteria-positive and -negative patient groups were compared. Of the 194 patients admitted with COVID-19, 17 (8.8%) were also diagnosed with M. pneumoniae (n?=?10) or C. pneumoniae infection (n?=?7). Confusion, headache, and bilateral infiltrate were found more frequently in the SARS CoV-2 and atypical bacteria co-infection group. Patients in the M. pneumoniae or C. pneumoniae co-infection group were more likely to develop ARDS, required ventilatory support, had a longer hospital length of stay, and higher fatality rate compared to patients with only SARS-CoV-2. Our report highlights co-infection with bacteria causing atypical pneumonia should be considered in patients with SARS-CoV-2 depending on the clinical context. Timely identification of co-existing pathogens can provide pathogen-targeted treatment and prevent fatal outcomes of patients infected with SARS-CoV-2 during the current pandemic.

Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19.

OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)-positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques. METHODS: CXR of 487 patients were classified into [4] categories-normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as "normal" and "indeterminate" were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction. RESULTS: The precision of radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying "normal" CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these "normal" radiographs. CONCLUSION: This study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes. KEY POINTS: • Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of radiologists in assessing CXR for possible COVID in comparison to RT-PCR. • We show that AI achieves the best results in images classified as "normal" by radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. • The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone.