RESUMO
Objetivos: Conocer la edad vascular (EV) de una muestra de población general del área sanitaria de Toledo incluida en el estudio RICARTO.Pacientes y métodoEstudio epidemiológico transversal realizado en población general ≥18 años, aleatorizada según tarjeta sanitaria. La EV se calculó a partir del riesgo cardiovascular (RCV) absoluto estimado con las escalas de Framingham y SCORE (la presencia de diabetes mellitus duplicó el RCV obtenido en varones y lo cuadruplicó en mujeres). Se excluyeron los sujetos con patología cardiovascular o renal. Se realizó ANCOVA para ajustar y comparar las medias de EV por edad y sexo.ResultadosSe analizaron 1.496 individuos (53,54% mujeres), con una edad media (DE) de 48,77 (14,89) años. La EV media fue 51,37 (19,13) años con Framingham y 57,09 (17,63) años con SCORE, resultando significativamente mayor en varones, nivel de estudios bajo, hipertensión arterial, dislipidemia, hipertrigliceridemia, diabetes mellitus, obesidad abdominal, obesidad general, tabaquismo y en sujetos con 5 factores de RCV frente a ninguno (p<0,001 en todos). Las mayores diferencias (D de Cohen >0,5) se hallaron entre no diabéticos y diabéticos (1,58 Framingham; 2,44 SCORE), normotensos e hipertensos (1,64 Framingham; 1,19 SCORE) y no dislipidémicos y dislipidémicos (0,95 Framingham; 0,66 SCORE).ConclusionesEn nuestra muestra la EV es 2,5años superior a la cronológica con la ecuación de Framingham y más de 8años con la del SCORE. El control de los factores de RCV es clave para lograr una EV más próxima a la real y lograr una mejor salud cardiovascular de la población. (AU)
Objective: To know the vascular age (VA) of a sample of general population included in the RICARTO study.Patients and methodEpidemiological study of the general population aged ≥18 from the Health Area of Toledo, based on the health card database. VA was calculated from the absolute cardiovascular risk (CVR) estimated with the Framingham and SCORE equations (type2 diabetes increased CVR in SCORE 2-fold in men and 4-fold in women). Patients with cardiovascular or renal disease were excluded. An ANCOVA analysis was conducted to adjust and compare the mean of VA by age and sex.Results1,496 subjects (53.54% women) were analyzed. Mean (SD) age was 48.77 (14.89) years old and. Mean VA was 51.37 (19.13) with Framingham equation and 57.09 (17.63) years old with SCORE equation. VA was significantly higher in men, low education level, arterial hypertension, dyslipidemia, hypertriglyceridemia, diabetes mellitus, abdominal obesity, general obesity, smoking and in individuals with 5CVR factors vs none (P<.001 in all). Higher differences (Cohen's D >0.5) were found in non-diabetic vs diabetic people (1.58 Framingham; 2.44 SCORE), normotensive vs hypertensive subjects (1.64 Framingham; 1.19 SCORE), and non-dyslipidemia vs presence of dyslipidemia (0.95 Framingham; 0.66 SCORE).ConclusionsVA of our sample is two and a half years older than chronological one with Framingham equation and more than eight years with SCORE equation. Control of CVR factors is the key to get a VA closer to real and to obtain a better cardiovascular health in the population. (AU)
Assuntos
Humanos , Adolescente , Pressão Arterial , Doenças Cardiovasculares/epidemiologia , Hipertensão/epidemiologia , Medição de Risco , Espanha/epidemiologia , Fatores de RiscoRESUMO
OBJECTIVE: To know the vascular age (VA) of a sample of general population included in the RICARTO study. PATIENTS AND METHOD: Epidemiological study of the general population aged ≥18 from the Health Area of Toledo, based on the health card database. VA was calculated from the absolute cardiovascular risk (CVR) estimated with the Framingham and SCORE equations (type2 diabetes increased CVR in SCORE 2-fold in men and 4-fold in women). Patients with cardiovascular or renal disease were excluded. An ANCOVA analysis was conducted to adjust and compare the mean of VA by age and sex. RESULTS: 1,496 subjects (53.54% women) were analyzed. Mean (SD) age was 48.77 (14.89) years old and. Mean VA was 51.37 (19.13) with Framingham equation and 57.09 (17.63) years old with SCORE equation. VA was significantly higher in men, low education level, arterial hypertension, dyslipidemia, hypertriglyceridemia, diabetes mellitus, abdominal obesity, general obesity, smoking and in individuals with 5CVR factors vs none (P<.001 in all). Higher differences (Cohen's D >0.5) were found in non-diabetic vs diabetic people (1.58 Framingham; 2.44 SCORE), normotensive vs hypertensive subjects (1.64 Framingham; 1.19 SCORE), and non-dyslipidemia vs presence of dyslipidemia (0.95 Framingham; 0.66 SCORE). CONCLUSIONS: VA of our sample is two and a half years older than chronological one with Framingham equation and more than eight years with SCORE equation. Control of CVR factors is the key to get a VA closer to real and to obtain a better cardiovascular health in the population.
Assuntos
Doenças Cardiovasculares , Hipertensão , Adolescente , Pressão Sanguínea , Doenças Cardiovasculares/epidemiologia , Feminino , Humanos , Hipertensão/epidemiologia , Masculino , Pessoa de Meia-Idade , Medição de Risco , Fatores de Risco , Espanha/epidemiologiaRESUMO
Introducción. La hemólisis constituye una de las incidencias más frecuentes en el laboratorio clínico, y se erige como una de las principales causas del rechazo de muestras. El presente estudio tiene como objetivo el diseño de una ecuación matemática que permita estimar y corregir las posibles interferencias producidas por la hemólisis en 6 analitos de uso frecuente en el laboratorio clínico. Material y métodos. Se emplearon sueros procedentes de 100 pacientes sanos, que se hemolizaron y se procesaron de acuerdo con las recomendaciones de la Comisión de Interferencias y Efectos de los Medicamentos de la Sociedad Española de Química Clínica. Las determinaciones se realizaron en el autoanalizador Modular Analytics D/P/ISE (Roche Diagnostics) y los datos obtenidos se trataron estadísticamente con la aplicación SPSS versión 15.0 (SPSS Inc., Chicago, IL, EE. UU.). Resultados. La hemólisis induce una sobreestimación en la determinación de potasio (K), aspartato aminotransferasa (AST), alanina aminotransferasa y lactato deshidrogenasa (LDH), mientras que se traduce en una infravaloración de gamma-glutamiltransferasa y bilirrubina total. La existencia de una correlación estadísticamente significativa entre el grado de hemólisis y el porcentaje de variación en la concentración de los analitos sólo se ha observado en el caso del K, el AST y la LDH. La ecuación de corrección surge del establecimiento de una igualdad entre el porcentaje de variación empírico que muestran los analitos en sueros hemolizados y el porcentaje de variación teórico obtenido a partir de análisis de regresión. Las estimaciones se enmarcaron dentro de un intervalo de confianza del 95% definido por el error analíticamente permisible. Conclusiones. Proponemos que la ecuación matemática puede ser de utilidad para la corrección de los valores de K, AST y LDH en el laboratorio clínico, y el rango de hemólisis susceptible de corrección es dependiente del analito analizado (AU)
Introduction. Haemolysis is the most common and undesirable occurrence in clinical laboratories and represents the main reason for specimen rejection. The aim of this study is to propose a mathematical equation to estimate and correct for the haemolysis interference in six common laboratory tests. Materials and methods. A toat of 100 fresh samples from healthy people were used. They were haemolysed and analysed following the instructions from the Interferences and Drug effects Commission (SEQC). Every analysis was carried out using an Modular analytics D/P/ISE (Roche Diagnostics) autoanalyser and SPSS (V15.0; SPSS Inc., Chicago, IL) statistical analysis software was used. Results. Haemolysis effect leads to overestimation of K, AST, ALT and LDH. On the other hand, haemolysis leads to an underestimation of GGT and BT. A statistically significant and positive linear correlation between the increase in free serum haemoglobin and the increase of the analyte were only observed in K, AST and LDH. When the real % variation of each analyte obtained in the experimental step of this study was equal to the previously calculated % variation using the regression equation, the correction equation shown was obtained. A 95% confidence interval was used to obtain the calculated results. Conclusions. The proposed mathematical equation is useful to the correction of K, AST and LDH in clinical laboratories. The haemolysis range depends on the tests analysed (AU)
