scholarly journals Covid-19: Arthritis drug trial for severe illness is stopped early after increase in deaths

BMJ ◽  
2021 ◽  
pp. n186
Author(s):  
Elisabeth Mahase
Keyword(s):  
Drug Trial ◽  
Severe Illness

A Potential Role of Coenzyme Q10 Deficiency in Severe SARS-CoV2 Infection

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Vasilios M. Polymeropoulos ◽  
Keyword(s):  
Viral Entry ◽  
Coenzyme Q10 ◽  
Severe Infection ◽  
The Body ◽  
Therapeutic Options ◽  
Severe Illness ◽  
Infected People ◽  
Inflammatory State ◽  
Exogenous Compounds ◽  
Obesity Hypertension

There is a dramatic need for extensive research into the predictors of severe infection with SARS-CoV2 and therapeutic options for infected people. People who suffer from severe illness and higher mortality display a pattern of having specific co-morbidities (diabetes, obesity, hypertension) and are of higher age. Recent research has described methods of viral entry via receptors (ACE2, TMPRSS2) and the hyper-inflammatory state often associated with severe illness (increase in interleukins, increase in TNF-alpha). These discoveries have led to the research of currently available and developing therapies, that are helpful to patients. Deficiencies of specific vitamins and other endogenous molecules of the body should be examined to understand if a pattern exists among the people most severely affected. Coenzyme Q10 (CoQ10) is a fat-soluble substance ubiquitously expressed throughout the body that is important for the generation of ATP and mediation of inflammatory disease. CoQ10 faces a decline with increasing age, genetic predispositions, and ingestion of exogenous compounds that could reduce the level of CoQ10. Deficiencies and subsequent supplementation with CoQ10 recently has displayed encouraging results for the improvement of a wide variety of diseases. This manuscript is significant as it points to a potential relationship of CoQ10 and the population suffering from severe illness of COVID-19, and further encourages the need for research into measuring the levels of CoQ10 and vitamins to understand if levels predict severe illness and mortality. This could offer new avenues into research in combating this pandemic and potentially future therapeutic options.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals Epidemiological, Virological and Serological Features of Coronavirus Disease 2019 (COVID-19) Cases in People Living With Human Immunodeficiency Virus in Wuhan: A Population-based Cohort Study

2020 ◽  
Author(s):  
Jiao Huang ◽  
Nianhua Xie ◽  
Xuejiao Hu ◽  
Han Yan ◽  
Jie Ding ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Cohort Study ◽  
Demographic Data ◽  
Case Fatality ◽  
Population Based ◽  
Severe Illness ◽  
Viral Loads ◽  
Viral Shedding ◽  
Signal Value ◽  
Immunodeficiency Virus

Abstract Background We aimed to describe the epidemiological, virological, and serological features of coronavirus disease 2019 (COVID-19) cases in people living with human immunodeficiency virus (HIV; PLWH). Methods This population-based cohort study identified all COVID-19 cases among all PLWH in Wuhan, China, by 16 April 2020. The epidemiological, virological, and serological features were analyzed based on the demographic data, temporal profile of nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the disease, and SARS-CoV-2–specific immunoglobin (Ig) M and G after recovery. Results From 1 January to 16 April 2020, 35 of 6001 PLWH experienced COVID-19, with a cumulative incidence of COVID-19 of 0.58% (95% confidence interval [CI], .42–.81%). Among the COVID-19 cases, 15 (42.86) had severe illness, with 2 deaths. The incidence, case-severity, and case-fatality rates of COVID-19 in PLWH were comparable to those in the entire population in Wuhan. There were 197 PLWH who had discontinued combination antiretroviral therapy (cART), 4 of whom experienced COVID-19. Risk factors for COVID-19 were age ≥50 years old and cART discontinuation. The median duration of SARS-CoV-2 viral shedding among confirmed COVID-19 cases in PLWH was 30 days (interquartile range, 20–46). Cases with high HIV viral loads (≥20 copies/mL) had lower IgM and IgG levels than those with low HIV viral loads (<20 copies/ml; median signal value divided by the cutoff value [S/CO] for IgM, 0.03 vs 0.11, respectively [P < .001]; median S/CO for IgG, 10.16 vs 17.04, respectively [P = .069]). Conclusions Efforts are needed to maintain the persistent supply of antiretroviral treatment to elderly PLWH aged 50 years or above during the COVID-19 epidemic. The coinfection of HIV and SARS-CoV-2 might change the progression and prognosis of COVID-19 patients in PLWH.

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

scholarly journals Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis

2020 ◽  
Vol 58 (7) ◽  
pp. 1021-1028 ◽  
Author(s):  
Brandon Michael Henry ◽  
Maria Helena Santos de Oliveira ◽  
Stefanie Benoit ◽  
Mario Plebani ◽  
Giuseppe Lippi
Keyword(s):  
Serum Ferritin ◽  
Weighted Mean Difference ◽  
Meta Analysis ◽  
Severe Disease ◽  
Severe Illness ◽  
China National Knowledge Infrastructure ◽  
Discriminative Ability ◽  
Laboratory Findings ◽  
Liver And Kidney ◽  
Wbc Count

AbstractBackgroundAs coronavirus disease 2019 (COVID-19) pandemic rages on, there is urgent need for identification of clinical and laboratory predictors for progression towards severe and fatal forms of this illness. In this study we aimed to evaluate the discriminative ability of hematologic, biochemical and immunologic biomarkers in patients with and without the severe or fatal forms of COVID-19.MethodsAn electronic search in Medline (PubMed interface), Scopus, Web of Science and China National Knowledge Infrastructure (CNKI) was performed, to identify studies reporting on laboratory abnormalities in patients with COVID-19. Studies were divided into two separate cohorts for analysis: severity (severe vs. non-severe and mortality, i.e. non-survivors vs. survivors). Data was pooled into a meta-analysis to estimate weighted mean difference (WMD) with 95% confidence interval (95% CI) for each laboratory parameter.ResultsA total number of 21 studies was included, totaling 3377 patients and 33 laboratory parameters. While 18 studies (n = 2984) compared laboratory findings between patients with severe and non-severe COVID-19, the other three (n = 393) compared survivors and non-survivors of the disease and were thus analyzed separately. Patients with severe and fatal disease had significantly increased white blood cell (WBC) count, and decreased lymphocyte and platelet counts compared to non-severe disease and survivors. Biomarkers of inflammation, cardiac and muscle injury, liver and kidney function and coagulation measures were also significantly elevated in patients with both severe and fatal COVID-19. Interleukins 6 (IL-6) and 10 (IL-10) and serum ferritin were strong discriminators for severe disease.ConclusionsSeveral biomarkers which may potentially aid in risk stratification models for predicting severe and fatal COVID-19 were identified. In hospitalized patients with respiratory distress, we recommend clinicians closely monitor WBC count, lymphocyte count, platelet count, IL-6 and serum ferritin as markers for potential progression to critical illness.

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