Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.

BACKGROUND: The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE: In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS: We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS: We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION: The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.

Increased frequency of pneumothorax and pneumomediastinum in COVID-19 patients admitted in the ICU: A multicentre study from Mumbai, India.

BACKGROUND: There are limited data regarding the incidence of pneumothorax in COVID-19 patients as well as the impact of the same on patient outcomes. METHODS: A retrospective review of the medical records at three large tertiary care hospitals in Mumbai was performed to identify patients hospitalised with COVID-19 from March 2020 to October 2020. The presence of pneumothorax and/or pneumomediastinum was noted when chest radiographs or CT scans were performed. Demographic and clinical characteristics of patients who developed air leak were recorded. RESULTS: 4,906 patients with COVID-19 were admitted, with 1,324 (27%) having severe COVID-19 disease. The overall incidence of pneumothorax and/or pneumomediastinum in patients with severe disease was 3.2% (42/1,324). Eighteen patients had pneumothorax, 16 had pneumomediastinum and 8 patients had both. Fourteen patients (33.3%) developed this complication breathing spontaneously, 28 patients (66.6%) developed it during mechanical ventilation. Overall mortality in this cohort was 74%, compared with 17% in the COVID-19 patients without pneumothorax (p<0.001). CONCLUSIONS: Our study demonstrates that air leaks occur with a higher frequency in patients with COVID-19 than in other ICU patients. When present, such air leaks contributed to poor outcomes with almost 74% mortality rates in these patients.

Reinfection in COVID-19: A scoping review.

Reinfections in COVID-19 are being reported all around the world and are a cause for concern, considering that a lot of our assumptions and modeling (including vaccination) related to the disease have relied on long-term immunity. We were one of the first groups to report a series of 4 healthcare workers to have been reinfected. This review article reports a scoping review of the available literature on reinfections, with a discussion of the implications of reinfections.

Prognostic Factors for Adverse Outcomes in COVID-19 Infection.

Whilst COVID-19 infection generally run a mild course in up to 80% of those affected, a number of pre-existing co-morbidities determine the severity of infection and the outcome in an individual patient. The most important of these co-morbidities that have consistently emerged in studies from across the globe, are the patients age and sex. Other important co-morbidities that adversely affect outcomes include pre-existing diabetes, obesity, hypertension, chronic lung disease and malignancy. This comprehensive review discusses the impact of these co-morbidities and the role of laboratory predictors of poor patient outcomes.