Role of systemic corticosteroids in preventing hypoxia among patients with mild COVID-19: An observational study.

Use of systemic corticosteroids is well-established in COVID-19 patients with hypoxia; however, there is scant data on its role in patients with mild disease and prolonged symptoms as a measure to prevent disease progression. The aim of this study is to evaluate the role of systemic corticosteroids in preventing hypoxia (SpO2 ≤ 93% on room-air) among mild COVID-19 patients. An observational study was conducted among symptomatic COVID-19 patients taking oral corticosteroids and attending institute teleconsultation facility between 10th-30th June 2021. Patients who were already on corticosteroids for other indication or required oxygen supplementation before or within 24-hours of initiation of corticosteroids were excluded. A total of 140 consecutive symptomatic COVID-19 patients were included. Higher baseline C-reactive protein (OR: 1.03, 95% CI: 1.02-1.06, p < 0.001) and early systemic corticosteroid (within 7 days) initiation (OR: 6.5, 95% CI: 2.1-20.1, p = 0.001) were independent risk factors for developing hypoxia (SpO2 ≤ 93%). Progression to hypoxia was significantly higher in patients who received corticosteroids before day 7 of illness (36.7%, 95% CI, 23.4-51.7%) compared to ≥ 7 of illness (14.3%, 95% CI, 7.8-23.2%) for persistent fever. Systemic corticosteroids within 7 days from symptom-onset were harmful and increased the risk of progression to hypoxia, whereas it may decrease the risk of progression when administered on or beyond 7 days in patients with mild COVID-19 and persistent symptoms. A well-designed randomised controlled trial is required to validate the findings.

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.

Mild COVID-19 infection-predicting symptomatic phase and outcome: A study from AIIMS, New Delhi.

CONTEXT: Comprehensive management of mild COVID infection calls for better understanding of symptomatology in these group of patients as well as early identification and close monitoring of patients at risk, data on which is limited. AIM: To study association between inflammatory markers and clinical presentation with progression of disease and the duration of resolution of symptoms. SETTINGS AND DESIGN: This is a retrospective study that has been conducted at a designated COVID -19 medical ward at AIIMS, New Delhi. METHODS AND MATERIAL: Fifty healthcare workers and their dependents who were admitted with asymptomatic and mild COVID-19 infection were included. Their records were retrospectively reviewed, entered into a predesigned proforma and analyzed. RESULTS: A total of 50 participants were included in the study of which 70% were healthcare workers. The patients were admitted with mild COVID illness out of which 22 (44%) were males. Most common symptom at presentation was fever (72%). Among patients who had mild disease versus those who progressed to moderate illness (n = 3), the patients with moderate illness were older [mean (SD): 57.33 (10.21) vs. 36.13 (14.05); P = 0.014] and had a longer duration of hospital stay [17 (1.41) days vs. 11.20 (3.86) days; P = 0.04]. Inflammatory markers, C-Reactive Protein (CRP) [2.46 vs. 0.20 (P = 0.024)], and Ferritin [306.15 vs. 72.53 (P = 0.023)] were higher in patients with moderate illness. There is also a significant correlation between the number of days taken for symptoms to resolve with Serum Ferritin (P = 0.007), CRP (P = 0.0256), and neutrophil lymphocyte ratio (NLR) (P = 0.044). CONCLUSIONS: Acute phase reactants/Inflammatory markers serve as good indicators of time taken to resolution of symptoms in acute COVID infection. NLR is a simple and inexpensive method to provide insight into symptomatic phase. These may be utility tools for primary care physician in the management in periphery and timely decision.

Risk factors and outcome among COVID-19 exposed and quarantined healthcare workers: A study on the status of existing practices of standard precautions.

CONTEXT: Health care workers (HCWs) are at high risk of COVID-19 infection but data on the risk factors for exposure and infection rate among Indian HCWs are limited. AIMS: Our study aims to identify the risk factors and behavior of HCWs which make them high risk for COVID-19 infection and the infection rate among them. SETTINGS AND DESIGN: This is a retrospective study conducted at All India Institute of Medical Sciences, New Delhi. METHODS AND MATERIAL: Fifty HCWs quarantined at our institute in April and May 2020 following exposure to confirmed or suspected COVID-19 cases, or due to development of Influenza-Like Illness (ILI) were included. Data was collected from medical records in a predesigned proforma and analyzed. RESULTS: Thirty-eight (76%) of the 50 quarantined HCWs had high-risk exposure and there was a significant breach in personal protective measures. N-95 masks were worn by 59.6%, gloves by 61.7%, and goggles or face shields by 2%. Exposures were more common in non-COVID areas of the hospital. Hydroxychloroquine pre-exposure prophylaxis was taken by 7 (14%). 3 (6%) were confirmed to be COVID-19 positive during the quarantine period. CONCLUSIONS: Our study has shown leniency among HCWs in adhering to infection control and personal protective measures resulting in an increased quarantine and infection rate and loss of manpower. The safety of our HCWs must be given paramount importance during this pandemic and should be ensured by educating them about infection control, and persistently reinforcing and strictly adhering to standard precautions.