Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
J Med Virol ; 93(7): 4303-4318, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1118166


Here we analyze hospitalized andintensive care unit coronavirus disease 2019 (COVID-19) patient outcomes from the international VIRUS registry ( We find that COVID-19 patients administered unfractionated heparin but not enoxaparin have a higher mortality-rate (390 of 1012 = 39%) compared to patients administered enoxaparin but not unfractionated heparin (270 of 1939 = 14%), presenting a risk ratio of 2.79 (95% confidence interval [CI]: [2.42, 3.16]; p = 4.45e-52). This difference persists even after balancing on a number of covariates including demographics, comorbidities, admission diagnoses, and method of oxygenation, with an increased mortality rate on discharge from the hospital of 37% (268 of 733) for unfractionated heparin versus 22% (154 of 711) for enoxaparin, presenting a risk ratio of 1.69 (95% CI: [1.42, 2.00]; p = 1.5e-8). In these balanced cohorts, a number of complications occurred at an elevated rate for patients administered unfractionated heparin compared to patients administered enoxaparin, including acute kidney injury, acute cardiac injury, septic shock, and anemia. Furthermore, a higher percentage of Black/African American COVID patients (414 of 1294 [32%]) were noted to receive unfractionated heparin compared to White/Caucasian COVID patients (671 of 2644 [25%]), risk ratio 1.26 (95% CI: [1.14, 1.40]; p = 7.5e-5). After balancing upon available clinical covariates, this difference in anticoagulant use remained statistically significant (311 of 1047 [30%] for Black/African American vs. 263 of 1047 [25%] for White/Caucasian, p = .02, risk ratio 1.18; 95% CI: [1.03, 1.36]). While retrospective studies cannot suggest any causality, these findings motivate the need for follow-up prospective research into the observed racial disparity in anticoagulant use and outcomes for severe COVID-19 patients.

Anticoagulants/therapeutic use , COVID-19/mortality , Enoxaparin/therapeutic use , Healthcare Disparities , Heparin/therapeutic use , Thrombosis/prevention & control , Anticoagulants/adverse effects , Blood Coagulation/drug effects , COVID-19/blood , Enoxaparin/adverse effects , Female , Heparin/adverse effects , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Thrombosis/drug therapy , COVID-19 Drug Treatment
Int J Clin Pract ; 75(6): e14116, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1105280


BACKGROUNDS: SARS-CoV-2 is affecting different countries all over the world, with significant variation in infection-rate and death-ratio. We have previously shown a presence of a possible relationship between different variables including the Bacillus Calmette-Guérin (BCG) vaccine, average age, gender, and malaria treatment, and the rate of spread, severity and mortality of COVID-19 disease. This paper focuses on developing machine learning models for this relationship. METHODS: We have used real-datasets collected from the Johns Hopkins University Center for Systems Science and Engineering and the European Centre for Disease Prevention and Control to develop a model from China data as the baseline country. From this model, we predicted and forecasted different countries' daily confirmed-cases and daily death-cases and examined if there was any possible effect of the variables mentioned above. RESULTS: The model was trained based on China data as a baseline model for daily confirmed-cases and daily death-cases. This machine learning application succeeded in modelling and forecasting daily confirmed-cases and daily death-cases. The modelling and forecasting of viral spread resulted in four different regions; these regions were dependent on the malarial treatments, BCG vaccination, weather conditions, and average age. However, the lack of social distancing resulted in variation in the effect of these factors, for example, double-humped spread and mortality cases curves and sudden increases in the spread and mortality cases in different countries. The process of machine learning for time-series prediction and forecasting, especially in the pandemic COVID-19 domain, proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables. CONCLUSION: From the experimental results, we confirm that COVID-19 has a very low spread in the African countries with all the four variables (average young age, hot weather, BCG vaccine and malaria treatment); a very high spread in European countries and the USA with no variable (old people, cold weather, no BCG vaccine and no malaria). The effect of the variables could be on the spread or the severity to the extent that the infected subject might not have symptoms or the case is mild and can be missed as a confirmed-case. Social distancing decreases the effect of these factors.

COVID-19 , Africa , China , Europe , Humans , Machine Learning , Physical Distancing , SARS-CoV-2
Vaccine ; 38(35): 5564-5568, 2020 07 31.
Article in English | MEDLINE | ID: covidwho-650590


COVID-19 is affecting different countries all over the world with great variation in infection rate and death ratio. Some reports suggested a relation between the Bacillus Calmette-Guérin (BCG) vaccine and the malaria treatment to the prevention of SARS-CoV-2 infection. Some reports related infant's lower susceptibility to the COVID-19. Some other reports a higher risk in males compared to females in such COVID-19 pandemic. Also, some other reports claimed the possible use of chloroquine and hydroxychloroquine as prophylactic in such a pandemic. The present commentary is to discuss the possible relation between those factors and SARS-CoV-2 infection.

Aging , BCG Vaccine/immunology , Chemoprevention , Chloroquine/pharmacology , Coronavirus Infections/mortality , Coronavirus Infections/prevention & control , Hydroxychloroquine/pharmacology , Pandemics/prevention & control , Pneumonia, Viral/mortality , Pneumonia, Viral/prevention & control , Sex Characteristics , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19 , Chloroquine/therapeutic use , Coronavirus Infections/immunology , Coronavirus Infections/transmission , Disease Susceptibility/immunology , Female , Geographic Mapping , Humans , Hydroxychloroquine/therapeutic use , Infant , Internationality , Male , Pneumonia, Viral/immunology , Pneumonia, Viral/transmission