COVID-19 patients with increasing age experience differential time to initial medical care and severity of symptoms.

We conducted a retrospective observational study in patients with laboratory-confirmed coronavirus disease (COVID-19) who received medical care in 688 COVID-19 ambulatory units and hospitals in Mexico City between 24 February 2020 and 24 December 2020, to study if the elderly seek medical care later than younger patients and their severity of symptoms at initial medical evaluation. Patients were categorised into eight groups (<20, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79 and ≥80 years). Symptoms at initial evaluation were classified according to a previously validated classification into respiratory and non-respiratory symptoms. Comparisons between time from symptom onset to medical care for every age category were performed through variance analyses. Logistic regression models were applied to determine the risk of presenting symptoms of severity according to age, and mortality risk according to delays in medical care. In total, 286 020 patients were included (mean age: 42.8, s.d.: 16.8 years; 50.4% were women). Mean time from symptom onset to medical care was 4.04 (s.d.: 3.6) days and increased with older age categories (P < 0.0001). Mortality risk increased by 6.4% for each day of delay in medical care from symptom onset. The risk of presenting with the symptoms of severity was greater with increasing age categories. In conclusion, COVID-19 patients with increasing ages tend to seek medical care later, with higher rates of symptoms of severity at initial presentation in both ambulatory units and hospitals.

Obesity is a strong risk factor for short-term mortality and adverse outcomes in Mexican patients with COVID-19: a national observational study.

Conflicting results have been obtained through meta-analyses for the role of obesity as a risk factor for adverse outcomes in patients with coronavirus disease-2019 (COVID-19), possibly due to the inclusion of predominantly multimorbid patients with severe COVID-19. Here, we aimed to study obesity alone or in combination with other comorbidities as a risk factor for short-term all-cause mortality and other adverse outcomes in Mexican patients evaluated for suspected COVID-19 in ambulatory units and hospitals in Mexico. We performed a retrospective observational analysis in a national cohort of 71 103 patients from all 32 states of Mexico from the National COVID-19 Epidemiological Surveillance Study. Two statistical models were applied through Cox regression to create survival models and logistic regression models to determine risk of death, hospitalisation, invasive mechanical ventilation, pneumonia and admission to an intensive care unit, conferred by obesity and other comorbidities (diabetes mellitus (DM), chronic obstructive pulmonary disease, asthma, immunosuppression, hypertension, cardiovascular disease and chronic kidney disease). Models were adjusted for other risk factors. From 24 February to 26 April 2020, 71 103 patients were evaluated for suspected COVID-19; 15 529 (21.8%) had a positive test for SARS-CoV-2; 46 960 (66.1%), negative and 8614 (12.1%), pending results. Obesity alone increased adjusted mortality risk in positive patients (hazard ratio (HR) = 2.7, 95% confidence interval (CI) 2.04-2.98), but not in negative and pending-result patients. Obesity combined with other comorbidities further increased risk of death (DM: HR = 2.79, 95% CI 2.04-3.80; immunosuppression: HR = 5.06, 95% CI 2.26-11.41; hypertension: HR = 2.30, 95% CI 1.77-3.01) and other adverse outcomes. In conclusion, obesity is a strong risk factor for short-term mortality and critical illness in Mexican patients with COVID-19; risk increases when obesity is present with other comorbidities.

Development and validation of the patient history COVID-19 (PH-Covid19) scoring system: a multivariable prediction model of death in Mexican patients with COVID-19.

Most of the existing prediction models for COVID-19 lack validation, are inadequately reported or are at high risk of bias, a reason which has led to discourage their use. Few existing models have the potential to be extensively used by healthcare providers in low-resource settings since many require laboratory and imaging predictors. Therefore, we sought to develop and validate a multivariable prediction model of death in Mexican patients with COVID-19, by using demographic and patient history predictors. We conducted a national retrospective cohort study in two different sets of patients from the Mexican COVID-19 Epidemiologic Surveillance Study. Patients with a positive reverse transcription-polymerase chain reaction for SARS-CoV-2 and complete unduplicated data were eligible. In total, 83 779 patients were included to develop the scoring system through a multivariable Cox regression model; 100 000, to validate the model. Eight predictors (age, sex, diabetes, chronic obstructive pulmonary disease, immunosuppression, hypertension, obesity and chronic kidney disease) were included in the scoring system called PH-Covid19 (range of values: -2 to 25 points). The predictive model has a discrimination of death of 0.8 (95% confidence interval (CI) 0.796-0.804). The PH-Covid19 scoring system was developed and validated in Mexican patients to aid clinicians to stratify patients with COVID-19 at risk of fatal outcomes, allowing for better and efficient use of resources.

Relation of body composition indexes to cardiovascular disease risk factors in young adults / Relación de índices de composición corporal con factores de riesgo de enfermedad cardiovascular en adultos jóvenes

BACKGROUND: To evaluate the relationship of body composition indexes with altered metabolic analytical parameters that show higher risk of cardiovascular disease in young adults. MATERIAL AND METHODS: A cross-sectional study. Sample of 1351 young adults, different body composition parameters were obtained such as Waist Circumference (WC), Hip Circumference (HC), Body Mass Index (BMI), Body Fat% (BF%), Waist-to-height Ratio (WHtR), and Waist-Hip Ratio (WHR), conicity index (C-Index), body surface area (BSA), abdominal volume index (AVI) and deep abdominal adipose-tissue (AT). Areas under receiver operating characteristic curves (AUCs) and odds ratios for the parameters were analyzed and their optimal cut-offs. Separately a MANOVA was applied to altered metabolic analytical parameters and two body composition indexes (BMI and BSA) and their interaction. RESULTS: BMI correlate significantly with metabolic analytical parameters (FPG r = 0.08, TCh r = 0.14, TGL r = 0.23, HDL-C r = -0.23, LDL-C r = 0.2, UA r = 0.22, All P < 0.01), BSA correlate with all (All P < 0.001) except FPG. BMI and BSA correlate significantly with all metabolic analytical parameters (All P < 0.001). The BMI individually predicts the changes of the six metabolic analytical parameters as a set [Wilk's Lambda = 0.89, F(18,1570) = 3.4, P < 0.0001], but not BSA [Wilk's Lambda = 0.98, F(6,555) = 1.4, P = 0.18], the interaction between these two indexes (BMI and BSA altered in the same individual) significantly predicts changes of the six metabolic analytical parameters of cardiovascular disease risk [Wilk's Lambda = 0.97, F(6,555) = 2.3, P = 0.03]. CONCLUSIONS: BMI and BSA correlate with cardiovascular disease risk factor. They are superior to WC, WHtR, WHR, BF%, C-index, AVI and AT

OBJETIVO: Evaluar la relación de los índices de composición corporal con los parámetros analíticos metabólicos alterados que muestran mayor riesgo de enfermedad cardiovascular en adultos jóvenes. MATERIAL Y MÉTODOS: Estudio transversal. Muestra de 1.351 adultos jóvenes, se obtuvieron diferentes parámetros de composición corporal: circunferencia de cintura (WC), circunferencia de cadera (HC), índice de masa corporal (IMC), % de grasa corporal (BF%), índice cintura-altura (WHtR) e índice cintura-cadera (WHR), índice de conicidad (C-index), superficie corporal (BSA), índice de volumen abdominal (AVI) y tejido adiposo abdominal profundo (AT). Se analizaron áreas bajo la curva y razones de momios de los parámetros y sus puntos de corte óptimos. Se aplicó un MANOVA por separado a los parámetros analíticos metabólicos alterados y 2 índices de composición corporal (IMC y BSA) y su interacción. RESULTADOS: El IMC se correlacionó con los parámetros analíticos metabólicos (FPG r = 0,08, TCh r = 0,14, TGL r = 0,23, HDL-C r = -0,23, LDL-C r = 0,2, UA r = 0,22; todos p < 0,01), BSA correlacionó con todos (p < 0,001) menos con FPG. El IMC, por sí solo, predice los cambios en los 6 parámetros analíticos metabólicos del riesgo de enfermedad cardiovascular en conjunto (Wilk's Lambda = 0,89, F [18,1570] = 3,4; p < 0,0001), pero no la BSA (Wilk's Lambda = 0,98, F [6,555]=1,4; p = 0,18), la interacción entre estos 2 índices (IMC y BSA alterados en el mismo individuo) predice significativamente los cambios de los 6 componentes metabólicos (Wilk's Lambda = 0,97, F [6,555] = 2,3; p = 0,03). CONCLUSIONES: El IMC y la BSA están relacionados con los factores de riesgo de enfermedad cardiovascular. Son superiores a WC, WHtR, WHR, BF%, C-index, AVI y AT

Relation of body composition indexes to cardiovascular disease risk factors in young adults.

BACKGROUND: To evaluate the relationship of body composition indexes with altered metabolic analytical parameters that show higher risk of cardiovascular disease in young adults. MATERIAL AND METHODS: A cross-sectional study. Sample of 1351 young adults, different body composition parameters were obtained such as Waist Circumference (WC), Hip Circumference (HC), Body Mass Index (BMI), Body Fat% (BF%), Waist-to-height Ratio (WHtR), and Waist-Hip Ratio (WHR), conicity index (C-Index), body surface area (BSA), abdominal volume index (AVI) and deep abdominal adipose-tissue (AT). Areas under receiver operating characteristic curves (AUCs) and odds ratios for the parameters were analyzed and their optimal cut-offs. Separately a MANOVA was applied to altered metabolic analytical parameters and two body composition indexes (BMI and BSA) and their interaction. RESULTS: BMI correlate significantly with metabolic analytical parameters (FPG r=0.08, TCh r=0.14, TGL r=0.23, HDL-C r=-0.23, LDL-C r=0.2, UA r=0.22, All P<0.01), BSA correlate with all (All P<0.001) except FPG. BMI and BSA correlate significantly with all metabolic analytical parameters (All P<0.001). The BMI individually predicts the changes of the six metabolic analytical parameters as a set [Wilk's λ=0.89, F(18,1570)=3.4, P<0.0001], but not BSA [Wilk's λ=0.98, F(6,555)=1.4, P=0.18], the interaction between these two indexes (BMI and BSA altered in the same individual) significantly predicts changes of the six metabolic analytical parameters of cardiovascular disease risk [Wilk's λ=0.97, F(6,555)=2.3, P=0.03]. CONCLUSIONS: BMI and BSA correlate with cardiovascular disease risk factor. They are superior to WC, WHtR, WHR, BF%, C-index, AVI and AT.