Complications of invasive mechanical ventilation in critically Ill Covid-19 patients - A narrative review.

Critically ill COVID-19 patients have to undergo positive pressure ventilation, a non-physiological and invasive intervention that can be lifesaving in severe ARDS. Similar to any other intervention, it has its pros and cons. Despite following Lung Protective Ventilation (LPV), some of the complications are frequently reported in these critically ill patients and significantly impact overall mortality. The complications related to invasive mechanical ventilation (IMV) in critically ill COVID-19 patients can be broadly divided into pulmonary and non-pulmonary. Among pulmonary complications, the most frequent is ventilator-associated pneumonia. Others are barotrauma, including subcutaneous emphysema, pneumomediastinum, pneumothorax, bullous lesions, cardiopulmonary effects of right ventricular dysfunction, and pulmonary complications mimicking cardiac failure, including pulmonary edema. Tracheal complications, including full-thickness tracheal lesions (FTTLs) and tracheoesophageal fistulas (TEFs) are serious but rare complications. Non-Pulmonary complications include neurological, nephrological, ocular, and oral complications.

An Early Catch: Multisystem Inflammatory Syndrome in Adults (MIS-A) in a Young Adult Male With COVID-19 Exposure.

While severe acute respiratory syndrome (SARS) is the most common presentation of coronavirus disease 2019 (COVID-19) infection, several short- and long-term complications from COVID-19 infection are also being recognized. One such complication with life-threatening consequences is known as multisystem inflammatory syndrome in adults (MIS-A). While the phenomenon of multisystem inflammatory syndrome in children (MIS-C) is more recognized, the pathophysiology of both presentations remains a mystery currently. Several theories have been put forward however no consensus has been established yet. We present the case of a 20-year-old male who was admitted to the intensive care unit for a multisystem illness characterized by severe biventricular failure, profound shock, and acute liver and kidney injuries. The severity of illness necessitated the treatment with mechanical ventilation, extracorporeal membrane oxygenation (ECMO), vasopressors, and continuous veno-venous hemofiltration (CVVH). The patient was treated with one dose of intravenous immune globulin (IVIG). In association with the foregoing treatment, the patient made dramatic recovery and came off pulmonary, hemodynamic, and renal support within a week and made remarkably quick and full recovery. This case highlights a rare presentation of a COVID-19 complication that requires prompt recognition, supportive care, and empiric treatment that led to a favorable outcome in this case.

COVID 19 pandemic: Effect on management of patients with breast cancer; single center retrospective cohort study.

BACKGROUND: (SARS-COV-2) infection, led to a pandemic affecting many countries, resulting in hospitals diverting most of their resources to fight the pandemic. Breast cancer, already a healthcare dilemma, is also affected in this scenario. Our aim was to find out the impact of COVID-19 on presentation of breast cancer stage and its effects on overall onco-surgical management. METHODS: This cohort single-centered retrospective review was carried out at our hospital, over a period of 18 months. Females with known breast cancer were included in the study. Data was collected on performas by a single researcher. Effect of COVID pandemic on presentation stage and its impact on overall management was studied. SPSS 23.0 used for data analysis. A 95% CI was used. Descriptive statistics were presented as range/means. Categorical data was analyzed by Fisher exact test, t-test was applied to numerical data, p value ≤ 0.05 was considered significant. RESULTS: Out of 87 patients presenting with suspicious lump, 69 who had malignancy on histo-pathology were included in study. Twelve out of 69 were COVID positive. Sixty patients presented with advanced stage (≥stage 2b) out of which 21 underwent upstaging of disease due to delay in presentation/management. We found that 9 out of 12 (majority) Covid positive patients had disease upstaging. Overall main reason for delay in presentation was found to be unawareness of disease. CONCLUSION: We concluded that COVID-19 pandemic had no impact on presentation delay, breast cancer management/treatment and disease upstaging as compared to figures available for our population before the pandemic. However, our study showed significant correlation between disease upstaging and COVID status. This led us to reconsider our preformed protocols for COVID positive breast cancer patients. Our results can be used by future researchers to investigate if COVID itself can contributes in patho-physiology of upstaging in breast cancer or not.

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.