Cistatina C como marcador precoz de lesión renal aguda en el shock séptico / Cystatin C as an early marker of acute kidney injury in septic shock

Objetivo: Describir la utilidad de la determinación de la concentración plasmática de cistatinaC en el diagnóstico precoz de la lesión renal aguda en el shock séptico. Pacientes y métodos: Serie prospectiva de 50 pacientes ingresados en una unidad de cuidados intensivos con shock séptico y creatinina plasmática <2mg/dl. Seguimiento clínico y analítico con determinaciones de cistatinaC, urea y creatinina plasmáticas desde el diagnóstico del shock séptico hasta 5días más tarde. Su gravedad se valoró con la escala RIFLE. Resultados: Veinte pacientes (40%) desarrollaron lesión renal aguda: 8 (16%) RIFLE «R», 5 (10%) RIFLE «I» y 7 (14%) RIFLE «F». Todos los RIFLE «F» precisaron depuración renal extracorpórea. Fallecieron 18 pacientes (36%); de ellos 8 (20%) habían desarrollado lesión renal aguda en su evolución. Hubo una correlación pobre entre creatinina y cistatinaC plasmáticas (r=0,501; p=0,001), que desaparecía cuando se alcanzaba cualquier grado de deterioro renal en la escala RIFLE. La cistatinaC se elevaba antes e identificaba mejor que la creatinina y la urea a aquellos pacientes que iban a desarrollar un deterioro severo de su función renal (RIFLE «F») y sus valores iniciales se relacionaban con la mortalidad a los 30días (OR=1,16; IC95%: 0,03-0,85). Conclusiones: En los pacientes que desarrollan lesión renal aguda séptica la cistatinaC plasmática se incrementa antes que los marcadores clásicos de función renal. Además constituye un biomarcador de severidad que se correlaciona con la evolución a RIFLE «F», la necesidad depuración extrarrenal y la mortalidad. Esta precocidad puede ser útil para instaurar medidas que eviten la progresión de la disfunción renal (AU)

Objective: To describe the utility of determining plasma cystatinC concentrations in the diagnosis of acute incident kidney injury in septic shock. Patients and methods: Prospective series of 50 patients with septic shock and plasma creatinine levels <2mg/dL hospitalized in an intensive care unit. Clinical and laboratory follow-ups were conducted, with measurements of cystatinC, urea and plasma creatinine levels from the diagnosis of septic shock to 5days later. The severity of the septic shock was assessed with the RIFLE scale. Results: Twenty patients (40%) developed acute kidney injury: 8 (16%) were categorized as RIFLE-R, 5 (10%) as RIFLE-I and 7 (14%) as RIFLE-F. All patients categorized as RIFLE-F required extracorporeal renal clearance. Eighteen (36%) patients died, 8 (20%) of whom had developed acute kidney injury in their evolution. There was poor correlation between plasma creatinine and cystatin C levels (r=.501; P=.001), which disappeared upon reaching any degree of renal impairment on the RIFLE scale. CystatinC levels increased earlier and were better able to identify patients who would develop serious renal function impairment (RIFLE-F) than creatinine and urea levels. The initial cystatinC levels were related to mortality at 30days (OR=1.16; 95%CI: 03-.85). Conclusions: For patients who developed acute septic kidney injury, the plasma cystatinC levels increased before the classical markers of renal function. CystatinC also constitutes a severity biomarker that correlates with progression to RIFLE-F, the need for extrarenal clearance and, ultimately, mortality. This precocity could be useful for starting measures that prevent the progression of renal dysfunction (AU)

Cystatin C as an early marker of acute kidney injury in septic shock. / Cistatina C como marcador precoz de lesión renal aguda en el shock séptico.

OBJECTIVE: To describe the utility of determining plasma cystatinC concentrations in the diagnosis of acute incident kidney injury in septic shock. PATIENTS AND METHODS: Prospective series of 50 patients with septic shock and plasma creatinine levels <2mg/dL hospitalized in an intensive care unit. Clinical and laboratory follow-ups were conducted, with measurements of cystatinC, urea and plasma creatinine levels from the diagnosis of septic shock to 5days later. The severity of the septic shock was assessed with the RIFLE scale. RESULTS: Twenty patients (40%) developed acute kidney injury: 8 (16%) were categorized as RIFLE-R, 5 (10%) as RIFLE-I and 7 (14%) as RIFLE-F. All patients categorized as RIFLE-F required extracorporeal renal clearance. Eighteen (36%) patients died, 8 (20%) of whom had developed acute kidney injury in their evolution. There was poor correlation between plasma creatinine and cystatin C levels (r=.501; P=.001), which disappeared upon reaching any degree of renal impairment on the RIFLE scale. CystatinC levels increased earlier and were better able to identify patients who would develop serious renal function impairment (RIFLE-F) than creatinine and urea levels. The initial cystatinC levels were related to mortality at 30days (OR=1.16; 95%CI: 03-.85). CONCLUSIONS: For patients who developed acute septic kidney injury, the plasma cystatinC levels increased before the classical markers of renal function. CystatinC also constitutes a severity biomarker that correlates with progression to RIFLE-F, the need for extrarenal clearance and, ultimately, mortality. This precocity could be useful for starting measures that prevent the progression of renal dysfunction.

NEMS: ¿nuevo predictor de mortalidad en el paciente crítico? / Nems: a new predictor of mortality in the critical patient?

En las unidades de cuidados intensivos se utilizan habitualmente escalas que predicen el riesgo de mortalidad hospitalaria y objetivan las necesidades terapéuticas y asistenciales que requieren los pacientes críticos. El objetivo de este trabajo fue estudiar si el NEMS podía ser utilizado como predictor de mortalidad, comparándolo con el APACHE II. Se realizó un estudio prospectivo en una unidad de cuidados intensivos polivalente de 24 camas. El APACHE II y NEMS se estratificaron en 3 niveles. Se recogieron datos demográficos y el valor en las primeras 24 horas del APACHE II y NEMS. Se incluyeron 1.257 pacientes; fallecieron el 16,4%. Fueron quirúrgicos el 69,6%; la mediana para estancia y edad fue de 2 días (1-4) y 66 años (50-77); el 59,3% fueron hombres. La mediana para vivos y muertos de APACHE II fue 10 (6-20) y 22,5 (17,25-29) respectivamente, (p<0,001) y para NEMS, 24 (18-29) y 34 (25-39,7), (p<0,001). La correlación entre ambas escalas fue rho=0,457, (p<0,01). La regresión logística controlada por edad, sexo y APACHE mostró solo para NEMS elevados un OR=3,1 (IC95%: 1,5-6,6), respecto al nivel mas inferior. Según los resultados no se debe utilizar el NEMS como predictor de mortalidad, aunque el riesgo de muerte aumenta tres veces con NEMS altos (AU)

Abstract Numerical scales are commonly used in intensive care units to predict hospital mortality and to assess the therapeutic effort and care that critically ill patients require. The aim of this work was to study whether the NEMS value can be used as a predictor of mortality, comparing it with the APACHE II. A prospective study in a 24 intensive care unit beds was conducted. The APACHE II and NEMS values were stratified into three levels. Demographic data and the first 24 hours values of APACHE II and NEMS scales were collected. A total of 1257 patients were included, 16.4% of whom died. 69.6% were surgical; median stay was 2 days (1-4). Medianage was 66 years (50-77), 59.3% were men. The median APACHE II and NEMS for the living and the dead in the subsequent course was 10 (6-20) versus 22.5 (17.25 to 29) (p <0.001) and 24(18-29) versus 34 (25 to 39.7) (p < 0.001) respectively. The correlation between both scales was rho = 0.457 (p < 0.01). Logistic regression controlled for age, sex and APACHE II showed an OR of3.1 (95% CI: 1.5-6.6) only for high NEMS, compared to the lowest level. According to the results NEMS should not be used as a predictor of mortality, although the risk of death increases by three times with high NEMS (AU)

[NEMS: a new predictor of mortality in the critical patient?]. / NEMS: ¿nuevo predictor de mortalidad en el paciente crítico?

Numerical scales are commonly used in intensive care units to predict hospital mortality and to assess the therapeutic effort and care that critically ill patients require. The aim of this work was to study whether the NEMS value can be used as a predictor of mortality, comparing it with the APACHE II. A prospective study in a 24 intensive care unit beds was conducted. The APACHE II and NEMS values were stratified into three levels. Demographic data and the first 24 hours values of APACHE II and NEMS scales were collected. A total of 1257 patients were included, 16.4% of whom died. 69.6% were surgical; median stay was 2 days (1-4). Median age was 66 years (50-77), 59.3% were men. The median APACHE II and NEMS for the living and the dead in the subsequent course was 10 (6-20) versus 22.5 (17.25 to 29) (p <0.001) and 24 (18-29) versus 34 (25 to 39.7) (p<0.001) respectively. The correlation between both scales was rho=0.457 (p<0.01). Logistic regression controlled for age, sex and APACHE II showed an OR of 3.1 (95% CI: 1.5-6.6) only for high NEMS, compared to the lowest level. According to the results NEMS should not be used as a predictor of mortality, although the risk of death increases by three times with high NEMS.

[Blood glucose levels in the first 24 hours of admission is not a risk factor for mortality in critical care patients]. / La glucemia de las primeras 24 horas no es un factor pronóstico de mortalidad en pacientes críticos.

INTRODUCTION: Glycemic alterations are known as a risk condition of death in several diseases, such as ischemic cardiovascular and neurological disorders. The fact that its tight control under narrow normality levels decreases mortality and morbidity have led to further studies seeking to confirm the results and expand them to other disease areas. OBJECTIVES: To determine whether glycemic changes by themselves are a mortality risk factor in a sample of patients within an Intensive Care Unit (ICU), among which predominates traumatic-surgical patients. METHODS: Demographic and analytical characteristics were revised, as well as common monitoring variables in an ICU, among a sample of 2,554 patients from admissions between 1st January 2004 and 31 December 2008. Data were obtained from a database which endorsed records compiled with the monitoring ICU patients program "Carevue". They were processed with dynamics sheets included in the Excel software with the following variables: initial glycemia, mean glycemia during the first 24 hours and number of determinations performed. We used the mean value in the admission day of the remaining analytical and monitoring variables and the number of test performed on this first day. The sample was stratified in two groups for the statistical analysis: a) General Sample (MG) and b) sample excluding patients admitted after a programmed surgery (EQP). In both cases the effect of initial and averaged glycemia was checked. Group b was divided in two, according to the number of determinations b1) a single blood glucose determination group (EQP1) and b2) a multiple determination group (EQPM). From this group of non-programmed surgical patients the study was repeated in those patients who stayed at the ICU 3 or more days (EQP3D). Chi-square and Mantel-Haenzel test for the ODD ratio determination were performed for qualitative variables; quantitative variables were examined with the Mann-Whitney test. At each analysis level, logistic regression was performed using mortality as the dependent variable, including those variables with p-values < 0.05 which represented more than 60% of the data. An initially saturated model with backward till the final equation was used. A p-value of 0.05 (i.e. p < 0.05) was set as the significant threshold for all statistical analysis. They were performed with SPSS and GSTAT 2 statistical software. RESULTS AND DISCUSSION: A total of 2,165 of the 2,554 admitted patients during the study period were included (96.5%). Exclusion criteria were absence of plasma glucose determinations. In the bivariate analysis, first and mean glucose blood levels showed significant differences in mortality rates in absolute figures and also when data were classified stratified in three levels (< 60 mg/dl; 60-110 mg/dl or > 110 mg/dl) or in two (normal values 60 to 110 mg/dl and unusual figures < 60 mg/dl or > 110 mg/dl). These significant differences were lost when a logistic model was applied. From the remaining variables, renal function and NEMS showed to be mortality risks factors in this sample. CONCLUSIONS: Hyperglycemia is a predominant phenomenon in critically ill patients. Hypoglycemia is less frequent and is associated with higher mortality rates. Initial glucose blood level, by itself, was not a mortality risk factor in the multivariate study and at none of the studied levels. Average glycemia did not add any prediction power. The changes in glucose blood levels seemed to be an adaptation process, which determined by itself a risk for the patient's discharge, at least in the first 24 hours period after ICU admission.

La glucemia de las primeras 24 horas no es un factor pronóstico de mortalidad en pacientes críticos / Blood glucose levels in the first 24 hours of admission is not a risk factor for mortality in critical care patients

Introducción: Las alteraciones de la glucemia han mostrado ser un factor de riesgo en patologías como la cardiopatía isquémica y el paciente neurológico. La constatación de que su control estricto en niveles estrechos de la normalidad, determinaba una disminución en mortalidad y morbilidad han dado lugar a estudios intentando corroborar los resultados y ampliar a otros campos patológicos. 
Objetivos: Determinar si la alteración de la glucemia, per se, era un factor de riesgo para mortalidad en una muestra de pacientes ingresados en una unidad de medicina intensiva, con predominio de pacientes quirúrgico-traumáticos. Métodos: Se revisan las características demográficas, analíticas y de variables de monitorización habitual en UCI, en una muestra de 2.554 pacientes correspondientes a ingresos entre el 1 de enero de 2004 y 31 de diciembre de 2008. Se extrajeron los datos de un repositorio que adquiere los datos del programa de monitorización en intensivos CareVue. Se procesaron mediante la aplicación Excel con la utilidad hojas dinámicas, creando las variables glucemia inicial, glucemia promedio de las primeras 24 horas, y número de determinaciones. Del resto de variables analíticas y de monitorización se construyeron las correspondientes al promedio del primer día de ingreso, así como su número de determinaciones realizadas en ese primer día. La glucemia se determino en sangre completa no capilar. Para el estudio estadístico se estratifico la muestra en dos grupos: a) Muestra General (MG) y b) excluyendo a los pacientes con ingreso trás cirugía programada (EQP). En ambos se comprobó el efecto de la glucemia inicial y la promediada. El grupo b se segmento en dos según el número de determinaciones b1) una sola determinación de glucemia (EQP1) y b2) múltiples determinaciones (EQPM). De este grupo sin pacientes quirúrgicos programados, se repitió el estudio en aquellos pacientes con una duración de estancia de 3 o mas días en UCI (EQP3D). Se empleó Chi2 para variables cualitativas y Mantel Haenzel para calculo de ODD ratio; Mann-Whitney para las cuantitativas. En cada nivel de análisis, las variables con p < 0,05 y que tenían una representación superior al 60% en los datos, se incluyeron en análisis de regresión logística, con la mortalidad como variable dependiente. Se empleó el modelo saturado de inicio con backward hasta la ecuación final. Se emplearon las aplicaciones estadísticas SPSS y GSTAT 2. Se estimó como significativo p < 0,05. Resultados y discusión: Se estudiaron un total de 2.165 pacientes de los 2.554 que ingresaron en el periodo de estudio (96,5%). Los excluidos lo fueron por carecer de determinaciones de glucemia plasmática. En el estudio bivariado, la glucemia de inicio y la promedio mostraron diferencias significativas de mortalidad, ya en cifras absolutas, en la estratificación a tres niveles (< 60 mg/dL; 60-110 mg/dL y > 110 mg/dL) y en la estratificación de normal (60 a 110 mg/dL) frente a valores anormales (< 60 o > 110 mg/dL). Esta significación se pierde al realizar el modelo logístico. Del resto de las variables empleadas, la función renal y el NEMS se muestran como factores de riesgo para la mortalidad en esta muestra. 
Conclusiones: La hiperglucemia es un fenómeno prevalente en pacientes graves. La hipoglucemia es menos frecuente, y marca una mayor mortalidad. La glucemia de inicio, per se, no fue factor de riesgo para mortalidad en el estudio multivariado y a ninguno de los niveles estudiados. La glucemia promedio no añadió ningún poder de predicción. Al menos en lo relacionado a las primeras 24 horas, las alteraciones de la glucemia parecen responder más a un proceso de adaptación que marcar per se, un riesgo en el alta del paciente (AU)

Introduction: Glycemic alterations are known as a risk condition of death in several diseases, such as ischemic cardiovascular and neurological disorders. The fact that its tight control under narrow normality levels decreases mortality and morbidity have led to further studies seeking to confirm the results and expand them to other disease areas. Objectives: To determine whether glycemic changes by themselves are a mortality risk factor in a sample of patients within an Intensive Care Unit (ICU), among which predominates traumatic-surgical patients. Methods: Demographic and analytical characteristics were revised, as well as common monitoring variables in an ICU, among a sample of 2,554 patients from admissions between 1st January 2004 and 31 December 2008. Data were obtained from a database which endorsed records compiled with the monitoring ICU patients program Carevue. They were processed with dynamics sheets included in the Excel software with the following variables: initial glycemia, mean glycemia during the first 24 hours and number of determinations performed. We used the mean value in the admission day of the remaining analytical and monitoring variables and the number of test performed on this first day. The sample was stratified in two groups for the statistical analysis: a) General Sample (MG) and b) sample excluding patients admitted after a programmed surgery (EQP). In both cases the effect of initial and averaged glycemia was checked. Group b was divided in two, according to the number of determinations b1) a single blood glucose determination group (EQP1) and b2) a multiple determination group (EQPM). From this group of non-programmed surgical patients the study was repeated in those patients who stayed at the ICU 3 or more days (EQP3D). Chi-square and Mantel-Haenzel test for the ODD ratio determination were performed for qualitative variables; quantitative variables were examined with the Mann-Whitney test. At each analysis level, logistic regression was performed using mortality as the dependent variable, including those variables with p-values < 0.05 which represented more than 60% of the data. An initially saturated model with backward till the final equation was used. A p-value of 0.05 (i.e. p < 0.05) was set as the significant threshold for all statistical analysis. They were performed with SPSS and GSTAT 2 statistical software. 
Results and discussion: A total of 2,165 of the 2,554 admitted patients during the study period were included (96.5%). Exclusion criteria were absence of plasma glucose determinations. In the bivariate analysis, first and mean glucose blood levels showed significant differences in mortality rates in absolute figures and also when data were classified stratified in three levels (< 60 mg/dl; 60-110 mg/dl or > 110 mg/dl) or in two (normal values 60 to 110 mg/dl and unusual figures < 60 mg/dl or > 110 mg/dl). These significant differences were lost when a logistic model was applied. From the remaining variables, renal function and NEMS showed to be mortality risks factors in this sample. Conclusions: Hyperglycemia is a predominant phenomenon in critically ill patients. Hypoglycemia is less frequent and is associated with higher mortality rates. Initial glucose blood level, by itself, was not a mortality risk factor in the multivariate study and at none of the studied levels. Average glycemia did not add any prediction power. The changes in glucose blood levels seemed to be an adaptation process, which determined by itself a risk for the patient's discharge, at least in the first 24 hours period after ICU admission (AU)

Neumotórax y pliegue cutáneo / No disponible

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Fístula broncobiliar congénita diagnosticada en la edad adulta / Congenital bronchobiliary fistula diagnosed in adult age

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Inmunodeficiencia ¿idiopática? de linfocitos T-CD4: a propósito de un caso

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[Hypokalemia-induced paraplegia secondary to acute diarrhea]. / Paraplejia por hipopotasemia secundaria a diarrea aguda.

Hypokalemia can give a variety of syntomatology but more often courses without it or with inespecific clinical manifestations. In our enviroment the etiology of hypokalemia is wide but one of the most common causes in third world countries are diarrheas. We describe a case of severe hypokalemia due to acute diarrhea which was manifested with severe neurologic symtoms but improves with conventional treatment.

Lumbalgia incapacitante en mujer de 53 años / Disabling lumbar pain in a 53-year old woman

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Paraplejia por hipopotasemia secundaria a diarrea aguda / Hipokalemic paraplegia due to acute diarrhea

La hipopotasemia puede dar una clínica variada pero es frecuente que curse de forma asintomática o con síntomas inespecíficos si es de escasa cantidad. En nuestro medio las causas de la hipopotasemia son variadas pero una de las etiologías más frecuentes en paises en vías de desarrollo son los cuadros diarreicos. Presentamos un cuadro de hipopotasemia severa debida a diarrea que se manifestó con intensa clínica neurológica pero que mejoró espectacularmente con tratamiento habitual. (AU)

Supervivencia en un caso de intoxicación por etilenglicol con acidemia extrema

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