Background Obesity has been described as a risk factor for COVID‐19 severity and mortality. Previous studies report a linear association between BMI and adverse outcomes, meanwhile in other critical illness, excessive fat tissue is related to improved survival. Whether different BMI is related with the survival of patients with severe COVID‐19 deserves further analysis. Objective To determine the mortality rate among hospitalized patients with severe COVID‐19 stratified according to BMI. Methods The clinical files of all patients hospitalized from March to December 2020 with a positive PCR test for SARS‐CoV‐2 discharged due to improvement or death, were analyzed. A mixed effects logistic regression was carried out to determine which clinical and biochemical characteristics and comorbidities were associated with in‐hospital mortality. Results The cohort consisted of 608 patients with a median age of 59 years (interquartile ranges, IQR 46–69 years), median BMI of 28.7 kg/m 2 (IQR 25.4–32.4 kg/m 2 ), 65.5% were male. In‐hospital mortality rate was 43.4%. Of the cohort 0.8% had low weight, 20.9% normal weight, 36.0% overweight, 26.5% obesity grade I, 10.2% obesity grade II and 5.6% obesity grade III. Mortality rate was highest in patients with low weight (80%), followed by patients with obesity grade III (58.8%) and grade II (50.0%). Overweight and underweight/obesity grade III were associated with higher mortality (OR of 9.75 [1.01–1.10] and OR 4.08 [1.64–10.14]), after adjusting by sex and age. Conclusions The patients in the underweight/overweight and grade 3 obesity categories are at higher risk of COVID‐19 related mortality, compared to those with grade I or II obesity.
Introduction: Neutrophil-to-lymphocyte (NLR) and lymphocyte-to-C-reactive protein (LCR) ratios are used to predict severity and mortality in various infections. Objective: To establish the best NLR and LCR cutoff point to predict mortality in patients hospitalized for COVID-19 in Mexico. Method: Analytical cross-sectional study of patients hospitalized for severe COVID-19 in a specialty hospital. Results: Out of 242 analyzed patients, 34 % died. The deceased subjects were older (62 vs. 51 years; p < 0.001), had a higher prevalence of > 10 years with systemic arterial hypertension (59.4 vs. 45.1 %, p = 0.022), as well as a higher NLR (17.66 vs. 8.31, p < 0.001) and lower LCR (0.03 vs. 0.06, p < 0.002) with regard to those who survived. The cutoff points to predict mortality were NLR > 12 and LCR < 0.03. The combination of NLR/LCR had a sensitivity of 80 %, specificity of 74 %, positive predictive value of 46.15 %, negative predictive value of 93.02 % and an odds ratio of 11.429 to predict mortality. Conclusion: NLR > 12 and LCR < 0.03 are useful biomarkers to evaluate the risk of mortality in Mexican patients with severe COVID-19.
Background: Obesity is frequent in Mexico, but its importance for COVID-19 is still under debate. We aimed to describe its frequency in patients with severe COVID-19 in a referral hospital. Materials and methods: 167 patients hospitalized for suspicious or confirmed COVID-19, 66.7% male with a median age of 54 (interquartile range 43-63) were classified according to BMI and evaluated for comorbidities, coronavirus-2 polymerase chain reaction test results, and reason for discharge. Results: 75.3% of the patients were overweight or obese and 7.8% had grade III obesity. Increasing BMI related to higher probabilities of hyperglycemia (fasting glucose > 100 mg/dL, p = 0.044), but other comorbidities were similar among groups. The mortality rate among patients with Grade I obesity was 11%, whereas 33% of patients with either underweight or Grade III obesity died, depicting a U-shaped mortality curve. Conclusions: Obesity and its comorbidities are common in hospitalized patients in Mexico. Special efforts must be made to detect them, and further interventions to control the obesity pandemic will also be necessary to improve long-term results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.