Clinical and Epidemiological Characteristics of Patients Diagnosed with COVID-19 in a Tertiary Care Center in Mexico City: A Prospective Cohort Study

Ortíz-Brizuela, Edgar; Villanueva-Reza, Marco; González-Lara, María Fernanda; Tamez-Torres, Karla M; Román-Montes, Carla Marina; Díaz-Mejía, Bruno Alejandro; Pérez-García, Esteban; Olivas-Martínez, Antonio; Rajme‐López, Sandra; Martinez-Guerra, Bernardo A; de-León-Cividanes, Nereyda A; Fernández-García, Oscar A.; Guerrero‐Torres, Lorena; Torres-González, Lorena; Carrera-Patiño, Fabián A.; Corral-Herrera, Ever Arturo; Hernández-Alemón, Aldo N.; Tovar-Vargas, Maria de Los Á.; Serrano-Pinto, Yamile G.; Espejo-Ortiz, Cristian E.; Morales-Ortega, María de la L.; Lozano-Cruz, Oscar Arturo; Cárdenas, José L.; Vidal-Mayo, José de Jesús; Hernández‐Gilsoul, Thierry; Rivero-Sigarroa, Eduardo; Domínguez-Cherit, Guillermo; Cervantes-Villar, Luz Elena; Ramos-Cervantes, Maria Del P.; Ibarra-González, Violeta; Calva-Mercado, Juan José; Sierra‐Madero, Juan; López-Íñiguez, Álvaro; Ochoa‐Hein, Eric; Crabtree-Ramírez, Brenda; Galindo-Fraga, Arturo; Guerrero-Almeida, María de Lourdes; Ruíz-Palacios, Guillermo M.; Gulı́as-Herrero, Alfonso; Sifuentes–Osornio, José; Kershenobich-Stalnikowitz, David; Ponce-de-León, Alfredo

doi:10.24875/ric.20000211

Cited by 78 publications

(96 citation statements)

References 22 publications

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“…Many studies reported COVID‐19 hospitalizations, but only a few reported the relationship between individuals with obesity and hospitalization. We identified 19 studies that examined the relationship and included them in this analysis 1,28,38,40,44,47–58 . Table S4 presents the results 1,45,47,48,59,60 ; all showed a significantly higher prevalence of individuals with obesity among hospitalized patients than among patients not hospitalized or the general population.…”

Section: Epidemiological Relationships: Individuals With Overweight Amentioning

confidence: 99%

Individuals with obesity and COVID‐19: A global perspective on the epidemiology and biological relationships

et al. 2020

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Summary The linkage of individuals with obesity and COVID‐19 is controversial and lacks systematic reviews. After a systematic search of the Chinese and English language literature on COVID‐19, 75 studies were used to conduct a series of meta‐analyses on the relationship of individuals with obesity–COVID‐19 over the full spectrum from risk to mortality. A systematic review of the mechanistic pathways for COVID‐19 and individuals with obesity is presented. Pooled analysis show individuals with obesity were more at risk for COVID‐19 positive, >46.0% higher (OR = 1.46; 95% CI, 1.30–1.65; p < 0.0001); for hospitalization, 113% higher (OR = 2.13; 95% CI, 1.74–2.60; p < 0.0001); for ICU admission, 74% higher (OR = 1.74; 95% CI, 1.46–2.08); and for mortality, 48% increase in deaths (OR = 1.48; 95% CI, 1.22–1.80; p < 0.001). Mechanistic pathways for individuals with obesity are presented in depth for factors linked with COVID‐19 risk, severity and their potential for diminished therapeutic and prophylactic treatments among these individuals. Individuals with obesity are linked with large significant increases in morbidity and mortality from COVID‐19. There are many mechanisms that jointly explain this impact. A major concern is that vaccines will be less effective for the individuals with obesity.

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Section: Epidemiological Relationships: Individuals With Overweight Amentioning

confidence: 99%

Individuals with obesity and COVID‐19: A global perspective on the epidemiology and biological relationships

et al. 2020

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“…A total of 777 records were collected by the databases and manual searching. After the exclusion of the duplicates, review articles, case reports, pre‐printed versions without peer‐review, and irrelevant studies by title or abstract screening, 187 studies remained for full‐text review; 52 studies 6,17‐67 were finally included into the meta‐analysis, and 18 studies were included in the qualitative synthesis (Figure 1). A total of 52 records involving 10 614 COVID‐19 patients confirmed between December 25, 2019, and June 1, 2020, were included.…”

Section: Resultsmentioning

confidence: 99%

Ferritin in the coronavirus disease 2019 (COVID‐19): A systematic review and meta‐analysis

Cheng

et al. 2020

Clinical Laboratory Analysis

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Objective The coronavirus disease 2019 (COVID‐19) has rapidly developed into a pandemic. Increased levels of ferritin due to cytokine storm and secondary hemophagocytic lymphohistiocytosis were found in severe COVID‐19 patients. Therefore, the aim of this study was to determine the role of ferritin in COVID‐19. Methods Studies investigating ferritin in COVID‐19 were collected from PubMed, EMBASE, CNKI, SinoMed, and WANFANG. A meta‐analysis was performed to compare the ferritin level between different patient groups: non‐survivors versus survivors; more severe versus less severe; with comorbidity versus without comorbidity; ICU versus non‐ICU; with mechanical ventilation versus without mechanical ventilation. Results A total of 52 records involving 10 614 COVID‐19‐confirmed patients between December 25, 2019, and June 1, 2020, were included in this meta‐analysis, and 18 studies were included in the qualitative synthesis. The ferritin level was significantly increased in severe patients compared with the level in non‐severe patients [WMD 397.77 (95% CI 306.51‐489.02), P < .001]. Non‐survivors had a significantly higher ferritin level compared with the one in survivors [WMD 677.17 (95% CI 391.01‐963.33), P < .001]. Patients with one or more comorbidities including diabetes, thrombotic complication, and cancer had significantly higher levels of ferritin than those without (P < .01). Severe acute liver injury was significantly associated with high levels of ferritin, and its level was associated with intensive supportive care, including ICU transfer and mechanical ventilation. Conclusions Ferritin was associated with poor prognosis and could predict the worsening of COVID‐19 patients.

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“…Information collected at the time of triage and emergency department evaluation included demographic variables, medical history of comorbidities including type 2 diabetes (T2D), obesity, chronic obstructive pulmonary disease (COPD), asthma, hypertension, immunosuppression, HIV infection, cardiovascular disease (CVD), chronic kidney disease (CKD), chronic liver disease (CLD), smoking habits and current symptoms, as described elsewhere [11]. Physical examination included weight (measured in kilograms) and height (measured in meters) to estimate the body-mass index (BMI) and vital signs including oxygen saturation measured by pulse oximetry (SpO2), respiratory rate (RR), heart rate (HR), temperature and arterial blood pressure (BP).…”

Section: Clinical Informationmentioning

confidence: 99%

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

Márquez‐Salinas

Antonio-Villa

et al. 2020

Preprint

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INTRODUCTION: Chronological age (CA) is a predictor of adverse COVID-19 outcomes; however, CA alone has not shown to be the better predictor of adverse outcomes in COVID-19 as it does not capture individual responses to SARS-CoV-2 infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAccelAge on the adaptive responses to SARS-CoV-2 infection in hospitalized patients. METHODS: We assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAccelAge were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (ICU admission, intubation, or death), and k-means clustering was performed to explore reproducible patterns of adaptive response to SARS-CoV-2 infection using PhenoAge components. RESULTS: We included 1069 subjects of whom 401 presented critical illness and 204 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated PhenoAccelAge >0 had higher risk of death and critical illness compared to those who had values according to CA (log-rank p<0.001). Using unsupervised clustering we identified four adaptive responses to SARS-CoV-2 infection: 1) Inflammaging associated with CA, 2) adaptive metabolic dysfunction associated with cardio-metabolic comorbidities, 3) adaptive unfavorable hematological response, and 4) response associated with favorable outcomes. CONCLUSIONS: Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

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Clinical and Epidemiological Characteristics of Patients Diagnosed with COVID-19 in a Tertiary Care Center in Mexico City: A Prospective Cohort Study

Cited by 78 publications

References 22 publications

Individuals with obesity and COVID‐19: A global perspective on the epidemiology and biological relationships

Individuals with obesity and COVID‐19: A global perspective on the epidemiology and biological relationships

Ferritin in the coronavirus disease 2019 (COVID‐19): A systematic review and meta‐analysis

Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

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