Relationship between obesity, diabetes and ICU admission in COVID-19 patients

García, Laura Fernández; Gutiérrez, A.B. Puentes; Bascones, María García

doi:10.1016/j.medcle.2020.06.008

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approximately 42% of patients had comorbidities, including 20% with diabetes and 24% with hypertension. These results agree with a recent study from Spain in which they retrospectively described 49 consecutive patients admitted to the internal medicine hospital ward for COVID-19 infection and found a significant association between diabetes and the need for admission to the ICU [ 20 ]. One of the largest studies of this type, conducted in 138 hospitals in France, Belgium, and Switzerland and including over 4000 patients critically ill with COVID-19 admitted to an ICU, reported that patients who were older or had diabetes or obesity were at the highest risk of mortality [ 21 ].…”

Section: Discussionsupporting

confidence: 92%

Epidemiology and Clinical Characteristics of People with Confirmed SARS-CoV-2 Infection during the Early COVID-19 Pandemic in Saudi Arabia

et al. 2022

View full text Add to dashboard Cite

This study provides epidemiologic and clinical characteristics of 492 consecutive patients diagnosed with SARS-CoV-2 infection at King Faisal Specialist Hospital and Research Centre in Saudi Arabia between March and September 2020. Data were collected from electronic case reports. The cohort was 54% male, with 20.4% aged >60 years, 19.9% aged 31–40 years, and 17% aged 41–50 years. The median incubation period was 16 days, with upper and lower 95% quartiles of 27 and 10 days, respectively. Most patients (79.2%) were symptomatic. Variables significantly different between symptomatic and asymptomatic patients were age, blood oxygen saturation percentage, hemoglobin level, lymphocyte count, neutrophil to lymphocyte (NTL) ratio, and alanine aminotransferase (ALT) and aspartate aminotransferase (AST) level. Asymptomatic patients were mostly younger, with lower body mass index and ALT and AST levels but higher lymphocyte counts, NTL ratio, and CD4, CD8, natural killer cell, IgG, and IgM levels. Factors associated with increased risk of mortality were age (>42 years) and comorbidities, particularly diabetes mellitus and hypertension. Patients who were not given an antiviral regimen were associated with better prognosis than patients who received an antiviral regimen (HR, 0.07; 95% CI, 0.011–0.25). These findings will help clinicians and policymakers adopt best management and treatment options for SARS-CoV-2 infection.

show abstract

Section: Discussionsupporting

confidence: 92%

Epidemiology and Clinical Characteristics of People with Confirmed SARS-CoV-2 Infection during the Early COVID-19 Pandemic in Saudi Arabia

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Many studies have focussed on general characteristics presented at the beginning of the disease and tried to identify major risk factors related to mortality, such as advanced age, cardiovascular disease, diabetes, chronic respiratory disease, hypertension and cancer 5–7 . In other studies, obesity and smoking were associated with increased risk 8,9 …”

Section: Introductionmentioning

confidence: 99%

Classification and analysis of outcome predictors in non‐critically ill COVID‐19 patients

Venturini

Orso

Cugini

et al. 2021

Internal Medicine Journal

View full text Add to dashboard Cite

Background Early detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected patients who could develop a severe form of COVID‐19 must be considered of great importance to carry out adequate care and optimise the use of limited resources. Aims To use several machine learning classification models to analyse a series of non‐critically ill COVID‐19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome. Methods We retrospectively analysed non‐critically ill patients with COVID‐19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected. Results In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64–0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction‐inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors. Conclusions In non‐critically ill COVID‐19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID‐19, such as age or dementia, influence clinical outcomes.

show abstract

Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study

et al. 2022

View full text Add to dashboard Cite

The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%, respectively). Data collected for this study included sociodemographic data, baseline comorbidities, baseline treatments, and other background data. Multilevel analyses with generalized estimated equations were used to develop the predictive model. Male sex and the gradual effect of age were the main risk factors for hospital admission. Regarding baseline comorbidities, coagulopathies, cancer, cardiovascular diseases, diabetes with organ damage, and liver disease were among the five most notable. Flu vaccination was a risk factor for hospital admission. Drugs that increased risk were chronic systemic steroids, immunosuppressants, angiotensin-converting enzyme inhibitors, and NSAIDs. The AUC of the risk score was 0.821 and 0.828 in the derivation and validation samples, respectively. Based on the risk score, five risk groups were derived with hospital admission ranging from 2.94 to 51.87%. In conclusion, we propose a classification system for people with COVID-19 with a higher risk of hospitalization, and indirectly with it a greater severity of the disease, easy to be completed both in primary care, as well as in emergency services and in hospital emergency room to help in clinical decision-making. Registration: ClinicalTrials.gov Identifier: NCT04463706.

show abstract

Relationship between obesity, diabetes and ICU admission in COVID-19 patients

Cited by 6 publications

References 6 publications

Epidemiology and Clinical Characteristics of People with Confirmed SARS-CoV-2 Infection during the Early COVID-19 Pandemic in Saudi Arabia

Epidemiology and Clinical Characteristics of People with Confirmed SARS-CoV-2 Infection during the Early COVID-19 Pandemic in Saudi Arabia

Classification and analysis of outcome predictors in non‐critically ill COVID‐19 patients

Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study

Contact Info

Product

Resources

About