Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission

Peckham, Hannah; Gruijter, Nina M. de; Raine, Charles; Radziszewska, Anna; Ciurtin, Coziana; Wedderburn, Lucy R.; Rosser, Elizabeth C.; Webb, Kate; Deakin, Claire T.

doi:10.1038/s41467-020-19741-6

Cited by 1,200 publications

(1,161 citation statements)

References 67 publications

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“…[13] Interestingly, CDC (Dec 2020) finds similar range of mortality comparison between patients below and above 65 years in US population. [14] Further, male gender has hazard ratios (1.27, CI 0.7 -2.23) signifying higher risk of mortality in male population, which is congruent to other international studies [15] where odds of death has been (1.39; 95% CI 1.31 -1.47) comparable. Respiratory Symptoms like distress, higher rate of respiration (>24/min) or silent hypoxemia detected through lower oxygen level (<90%) have shown higher risk of mortality (see Table 2) and find their due weightage in the Gradient Boosting algorithm.…”

Section: Discussionsupporting

confidence: 79%

Development and Validation of a multivariable prediction model using Machine Learning to predict the outcome of admitted COVID-19 patients at the time of admission (AICOVID)

Kar¹,

Chawla

Haranath

et al. 2021

Preprint

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Introduction In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Methods:Data of 1393 admitted patients (Expired – 8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. Over 50 Clinical and Laboratory parameters were studied based on the patient’s initial clinical state at admission and laboratory parameters within the first 24 hours. The Machine Learning (ML) modelling was performed using Gradient Boosting Algorithm. ‘Time to event’ using Cox Proportional Hazard Model was used and combined with Gradient Boosting Algorithm. The prospective validation cohort was selected of 977 patients (Expired - 8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Results:Out of the 63 clinical and laboratory parameters, Age [Adjusted Hazard Ratio (HR)– 2.31; 95%CI 1.52 – 3.53], Male Gender [HR–1.72, 95%CI 1.06 – 2.85], Respiratory Distress [HR–1.79, 95%CI 1.32 – 2.53], Diabetes Mellitus [HR – 1.21, 95%CI 0.83 – 1.77], Chronic Kidney Disease [HR– 3.04, 95%CI 1.72 – 5.38], Coronary Artery Disease [HR– 1.56, 95%CI – 0.91 – 2.69}, Respiratory Rate >24/min [HR–1.54, 95%CI 1.03 – 2.3], Oxygen Saturation below 90% [HR–2.84, 95%CI 1.87 – 4.3], Lymphocyte% in DLC [HR– 1.99, 95%CI 1.23 – 2.32], INR [HR–1.71, 95%CI 1.31 – 2.13], LDH [HR–4.02, 95%CI 2.66 – 6.07 ] and Ferritin [HR–2.48, 95%CI 1.32 – 4.74 ] were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. Conclusion:The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on ‘time to event’ at the admission, accurately predicting patient outcomes.

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Section: Discussionsupporting

confidence: 79%

Development and Validation of a multivariable prediction model using Machine Learning to predict the outcome of admitted COVID-19 patients at the time of admission (AICOVID)

Kar¹,

Chawla

Haranath

et al. 2021

Preprint

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“…Nonetheless, an inverse tendency was noticed in the elderly group during the reopening and second wave compared to pre-COVID and lockdown. Male and old people have been proven to face severe respiratory diseases more frequently due to Sars-Cov-2 infection, as revealed by the higher number of intensive care unit (ICU) admissions and mortality rate [ 29 , 30 ]. The raising awareness and acknowledge about COVID-19 clinical features, regardless of the actual spread of coronavirus infection, might have induced old male people to follow social distancing rules more, thus also avoiding visiting the UDCS if not strictly necessary during the reopening and second wave.…”

Section: Discussionmentioning

confidence: 99%

Influence of the COVID-19 Pandemic on Dental Emergency Admissions in an Urgent Dental Care Service in North Italy

Cagetti

Balian

Camoni

et al. 2021

IJERPH

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A retrospective study was performed to verify if the number of admissions for urgent dental care in the Urgent Dental Care Service of San Paolo Hospital in Milan (Italy) was directly related to the different phases of the COVID-19 pandemic. Different periods were analyzed: March 25–April 5, 2019 (pre-COVID); March 23–April 3, 2020 (lockdown); June 8–June 19, 2020 (reopening); and November 9–November 20 (second wave). Raw data regarding admissions, diagnoses, and treatments were extracted. Descriptive and bivariate analyses were performed. The survey included 901 admissions, 285 in pre-COVID, 93 during lockdown, 353 in reopening, and 170 in the second wave. In each time period, statistically significant differences were found in the prevalence of each kind of diagnoses (2(3) = 20.33 p = 0.01 for endodontic emergencies, 2(3) = 29.05 p < 0.01 for cellulitis/phlegmon, 2(3) = 28.55 p < 0.01 for periodontal emergencies, Fisher’s Exact Test p < 0.01 for trauma, and 2(3) = 59.94 p < 0.01 for all other kinds of diagnosis). A remarkable increase in consultations (+186.36%) and other treatments (+90.63%) occurred during reopening. Tooth extraction was the most frequently delivered treatment, but suffered the largest reduction during lockdown (−79.82%). The COVID-19 pandemic has highly affected dental activity in north Italy, underling the weaknesses of a private dental system in a pandemic scenario.

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“… 3 , 4 , 5 , 6 In contrast, SLE is a female-predominant disease which may represent a protective factor, as COVID-19 has been shown to affect males more severely. 3 , 7 , 8 , 9 , 10 One interesting feature is that inflammation in SLE is often characterized by elevation of type I interferon (IFN), which has antiviral properties and could potentially be protective. This is not known, however, and even if true, it could be offset by other sociodemographic, biologic, and clinical aspects of the disease and its management.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 in patients with systemic lupus erythematosus: lessons learned from the inflammatory disease

Fernández-Ruiz

Paredes

Niewold

2021

Translational Research

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As the world navigates the Coronavirus disease 2019 (COVID-19) pandemic, there is a growing need to assess its impact in patients with autoimmune rheumatic diseases, such as systemic lupus erythematosus (SLE). Patients with SLE are a unique population when considering the risk of contracting COVID-19 and infection outcomes. The use of systemic glucocorticoids and immunosuppressants, and underlying organ damage from SLE are potential susceptibility factors. Most patients with SLE have evidence of high type I IFN activity, which may theoretically act as an antiviral line of defense or contribute to the development of a deleterious hyperinflammatory response in COVID-19. Other immunopathogenic mechanisms of SLE may overlap with those described in COVID-19, and thus studies in SLE could provide some insight into immune responses occurring in severe cases of the viral infection. We reviewed the literature to date on COVID-19 in patients with SLE and provide an in-depth review of current research in the area, including immune pathway activation, epidemiology, clinical features, outcomes, and the psychosocial impact of the pandemic in those with autoimmune disease.

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Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission

Cited by 1,200 publications

References 67 publications

Development and Validation of a multivariable prediction model using Machine Learning to predict the outcome of admitted COVID-19 patients at the time of admission (AICOVID)

Development and Validation of a multivariable prediction model using Machine Learning to predict the outcome of admitted COVID-19 patients at the time of admission (AICOVID)

Influence of the COVID-19 Pandemic on Dental Emergency Admissions in an Urgent Dental Care Service in North Italy

COVID-19 in patients with systemic lupus erythematosus: lessons learned from the inflammatory disease

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