Aproximación a la presencia de SPD y microorganismos en agua embotellada

Poca información existe en la literatura acerca de la calidad química referente a subproductos de desinfección (SPD) y su relación con la microbiología del agua embotellada. Por tanto, se evaluó el contenido de trihalometanos (THM) y de ácidos haloacéticos (AHA) como principales SPD en siete marcas de agua embotellada del mercado colombiano, al igual que la presencia de indicadores microbiológicos, enterobacterias, aerobios mesófilos, hongos y levaduras. Los resultados mostraron valores máximos de 135 y 140 mg/l de THM y AHA totales, así como incumplimiento del 28% de la norma propuesta por la FDA. Se encontró la presencia de alguno de los indicadores microbiológicos en el 69% de las muestras e incumplimiento de la norma colombiana de agua potable en el 30%. La relación entre la cantidad de SPD y la calidad microbiológica fue diversa, observándose un escenario recomendable de baja concentración de SPD y microorganismos en dos de las marcas evaluadas. Finalmente, se requiere mayor información para analizar el efecto de la presencia de levaduras como indicador de cambios organolépticos en el agua y su posible relación con la proliferación de otro tipo de microorganismos

Harmonized D-dimer levels upon admission for prognosis of COVID-19 severity: Results from a Spanish multicenter registry (BIOCOVID-Spain study).

Coagulopathy is a key feature of COVID-19 and D-dimer has been reported as a predictor of severity. However, because D-dimer test results vary considerably among assays, resolving harmonization issues is fundamental to translate findings into clinical practice. In this retrospective multicenter study (BIOCOVID study), we aimed to analyze the value of harmonized D-dimer levels upon admission for the prediction of in-hospital mortality in COVID-19 patients. All-cause in-hospital mortality was defined as endpoint. For harmonization of D-dimer levels, we designed a model based on the transformation of method-specific regression lines to a reference regression line. The ability of D-dimer for prediction of death was explored by receiver operating characteristic curves analysis and the association with the endpoint by Cox regression analysis. Study population included 2663 patients. In-hospital mortality rate was 14.3%. Harmonized D-dimer upon admission yielded an area under the curve of 0.66, with an optimal cut-off value of 0.945 mg/L FEU. Patients with harmonized D-dimer ≥ 0.945 mg/L FEU had a higher mortality rate (22.4% vs. 9.2%; p

Characteristics and laboratory findings on admission to the emergency department among 2873 hospitalized patients with COVID-19: the impact of adjusted laboratory tests in multicenter studies. A multicenter study in Spain (BIOCOVID-Spain study).

Identification of predictors for severe disease progression is key for risk stratification in COVID-19 patients. We aimed to describe the main characteristics and identify the early predictors for severe outcomes among hospitalized patients with COVID-19 in Spain. This was an observational, retrospective cohort study (BIOCOVID-Spain study) including COVID-19 patients admitted to 32 Spanish hospitals. Demographics, comorbidities and laboratory tests were collected. Outcome was in-hospital mortality. For analysis, laboratory tests values were previously adjusted to assure the comparability of results among participants. Cox regression was performed to identify predictors. Study population included 2873 hospitalized COVID-19 patients. Nine variables were independent predictors for in-hospital mortality, including creatinine (Hazard ratio [HR]:1.327; 95% Confidence Interval [CI]: 1.040-1.695, p = .023), troponin (HR: 2.150; 95% CI: 1.155-4.001; p = .016), platelet count (HR: 0.994; 95% CI: 0.989-0.998; p = .004) and C-reactive protein (HR: 1.037; 95% CI: 1.006-1.068; p = .019). This is the first multicenter study in which an effort was carried out to adjust the results of laboratory tests measured with different methodologies to guarantee their comparability. We reported a comprehensive information about characteristics in a large cohort of hospitalized COVID-19 patients, focusing on the analytical features. Our findings may help to identify patients early at a higher risk for an adverse outcome