Potential effects of corticosteroids on physiological dead-space fraction in acute respiratory distress syndrome.

BACKGROUND: Increased dead-space fraction is common in patients with persistent acute respiratory distress syndrome (ARDS). We evaluated the changes in the oxygenation and dead-space fraction in patients with persistent ARDS after corticosteroid therapy. METHODS: This was a non-randomized non-placebo, controlled observational study including 19 patients with persistent ARDS treated with corticosteroids. We measured P(aO(2))/F(IO(2)) and dead-space fraction at days 0, 4, and 7 after corticosteroids treatment (methylprednisolone) initiation. Patients were classified in intermediate group when corticosteroids were initiated between days 8-14 after ARDS onset, and in late group when initiated after 14 days. RESULTS: Mean time from the diagnosis of the ARDS to methylprednisolone treatment was 11 ± 2 days in the intermediate group (10 patients) and 21 ± 8 days in the late group (9 patients). When comparing days 0, 4, and 7 after methylprednisolone treatment, we found an increase in the P(aO(2))/F(IO(2)) (145 ± 64 mm Hg, 190 ± 68 mm Hg, and 226 ± 84 mm Hg, respectively, P < .001) and a decrease in the physiological dead-space fraction (0.66 ± 0.10, 0.58 ± 0.12, and 0.53 ± 0.11, respectively, P < .001). No differences were found between the intermediate and late groups. CONCLUSIONS: In patients with persistent ARDS, the increase in oxygenation was accompanied by a decrease in the dead-space fraction after a few days of corticosteroid treatment. To confirm potential benefit of corticosteroids on physiological parameters and mortality will require a powered randomized placebo controlled trial.

Hypoxic hepatitis in critically ill patients: incidence, etiology and risk factors for mortality.

PURPOSE: Hypoxic hepatitis may be induced by hemodynamic instability or arterial hypoxemia in critically ill patients. We investigated the incidence, etiology, association with systemic ischemic injury and risk factors for mortality in this population. METHODS: Retrospective analysis of patients with hypoxic hepatitis admitted to a multidisciplinary intensive care unit (ICU) of a university hospital. Hypoxic hepatitis was defined as the existence of a compatible clinical setting (cardiocirculatory failure or arterial hypoxemia) and aminotransferase levels higher than 1000 IU/L. RESULTS: During the 8-year study period, 182 out of the 7674 patients admitted presented hypoxic hepatitis (2.4%). The most common cause was septic shock. The rate of in-hospital mortality in hypoxic hepatitis was 61.5% (112 patients), and was higher in patients with septic shock (83.3%) and cardiac arrest (77.7%). Ischemic pancreatitis (25.6%), rhabdomyolysis (41.2%) and renal failure (67.2%) were common in these patients. Risk factors of mortality were prolonged INR (p = 0.005), need for renal replacement therapy (p = 0.001) and septic shock (p = 0.005). CONCLUSIONS: Hypoxic hepatitis was not a rare condition, and was frequently accompanied by multiorgan injury, with high mortality. Risk factors for increased mortality were prolonged INR, need for renal replacement therapy, and septic shock.

[Benefits of an education program and a clinical algorithm in mixed nutritional support to improve nutrition for the critically ill patient: a before-and-after study]. / Beneficios de un programa de formación y de un algoritmo clínico de soporte nutricional mixto para mejorar la nutrición del paciente crítico: estudio antes-después.

INTRODUCTION: Introduction: optimal nutrition in the critically ill patient is a key aspect for recovery. Objectives: to promote training in and knowledge of mixed nutrition support (MNS) by means of a clinical algorithm among intensivists for improving the nutritional status of critically ill patients. Methods: a before-and-after study with the participation of 19 polyvalent intensive care units (ICUs) in 10 autonomous communities. Five members of the scientific committee trained the trainers by means of oral presentations and a clinical algorithm on MNS. Then, trainers were responsible for explaining the algorithm to local intensivists in their ICUs. The 30-item study questionnaire was completed before and after the intervention by 179 and 105 intensivists, respectively. Results: a clear improvement of knowledge was found in six (20 %) specific MNS-related questions. In 11 items (36.6 %), adequate knowledge on different aspects of nutritional support that were already present before the intervention were maintained, and in five items (16.7 %) an improvement in the rate of correct responses was recorded. There were no improvements in correct responses for four items (13.3 %), and for four (13.3 %) additional items the percentage of correct responses decreased. Conclusions: the use of the MNS algorithm has achieved a solid consolidation of the main concepts of MNS. Some aspects regarding how to manage the malnourished patient, how to identify them and what type of nutrition to guide from the beginning of admission to the ICU, nutritional contributions in special situations, and the monitoring of possible complications such as refeeding are areas for which further training strategies are needed.

INTRODUCCIÓN: Introducción: la nutrición óptima del paciente crítico es clave para su recuperación. Objetivos: promover la formación y difusión del conocimiento acerca del soporte nutricional mixto (SNM) mediante un algoritmo clínico entre los intensivistas para mejorar el estado nutricional de los pacientes críticos. Métodos: estudio antes-después con la participación de 19 unidades de cuidados intensivos (UCI) olivalentes en 10 comunidades autónomas. Cinco miembros del comité científico formaron a los formadores mediante presentaciones orales y el algoritmo de SNM. Los formadores fueron responsables de la formación de los intensivistas en sus propias UCI. El cuestionario de 30 ítems fue completado por 179 y 105 intensivistas antes y después de la intervención, respectivamente. Resultados: se observó un aumento del conocimiento en seis (20 %) preguntas específicas relacionadas con el SNM. En 11 ítems (36,6 %), el conocimiento adecuado sobre diferentes aspectos del soporte nutricional que ya estaban presentes antes de la formación se mantuvieron, y en cinco ítems (16,7 %) hubo un aumento de la tasa de respuestas correctas. En cuatro ítems (13,3 %), las respuestas correctas no mejoraron y en otros cuatro (13,3 %), los porcentajes de respuestas correctas disminuyeron. Conclusiones: el algoritmo de SNM ha logrado una sólida consolidación de los principales conceptos de esta estrategia. Algunos aspectos referentes a cómo manejar al paciente desnutrido, cómo identificarlo y qué tipo de nutrición pautar desde el inicio del ingreso en la UCI, los aportes nutricionales en situaciones especiales y el seguimiento de posibles complicaciones como la realimentación, son áreas que requerirían estrategias formativas adicionales.

Successful weaning from continuous renal replacement therapy. Associated risk factors.

PURPOSE: To identify risk factors of successful continuous renal replacement therapy (CRRT) weaning and to evaluate the effect of furosemide in the recovery of urine output after CRRT stop. MATERIALS AND METHODS: Retrospective, observational study of critical patients treated with CRRT. Weaning tests (WT) were classified in two groups: successful (urine output was recovered and CRRT was not required again) and failed (CRRT was required again). A multiple logistic regression model was used to identify risk factors of successful CRRT WT. The prediction ability was assessed with the area under the receiver operating characteristic curves (AUC-ROC). RESULTS: Eighty-six patients underwent 101 CRRT WT. The multivariate model identified that the risk factors of successful CRRT weaning were sex and 6h-urine output after CRRT stop. The AUC-ROC was 0.81 (0.72-0.90) for 6h-urine output before and 0.91 (0.84-0.96) for 6h-urine output after CRRT stop. The AUC-ROC for 6h-urine output after WT to predict successful CRRT weaning were 0.94 (0.88-1.0) in patients who received furosemide and 0.85 (0.72-0.99) in patients who did not. CONCLUSIONS: Urine output after CRRT stop was the main risk factor of successful CRRT weaning. Administration of furosemide increased the strength of this association.

Hyperlactatemia in ICU patients: Incidence, causes and associated mortality.

PURPOSE: To describe the incidence, causes and associated mortality of hyperlactatemia in critically ill patients and to evaluate the association between lactate clearance and in-hospital survival. METHODS: Retrospective cohort study of patients with hyperlactatemia admitted to the ICU. Hyperlactatemia was defined as a blood lactate concentration ≥5mmol/L and high-grade hyperlactatemia a lactate level ≥10mmol/L. Lactate clearance was calculated as the percentage of decrease in lactate concentration from the peak value. RESULTS: Of 10,123 patients, 1373 (13.6%) had lactate concentration ≥5mmol/L, and 434(31.6%) of them had ≥10mmol/L. The most common causes of hyperlactatemia were sepsis/septic shock and post-cardiac surgery. An association was found between lactate concentration and in-hospital mortality (p<0.001). The area under the receiver-operating-characteristics (ROC) of lactate concentration and the optimal cut off to predict mortality were 0.72 (0.70-0.75) and 8.6mmol/L, respectively. ROC analysis for lactate clearance to predict in-hospital survival showed that the best area under the curve was obtained at 12h: 0.67 (95% confidence interval 0.59-0.75). CONCLUSIONS: Hyperlactatemia was common and associated with a high mortality in critically ill patients. Lactate clearance had limited utility for predicting in-hospital survival.

Beneficios de un programa de formación y de un algoritmo clínico de soporte nutricional mixto para mejorar la nutrición del paciente crítico: estudio antes-después / Benefits of an education program and a clinical algorithm in mixed nutritional support to improve nutrition for the critically ill patient: a before-and-after study

Introducción: la nutrición óptima del paciente crítico es clave para su recuperación. Objetivos: promover la formación y difusión del conocimiento acerca del soporte nutricional mixto (SNM) mediante un algoritmo clínico entre los intensivistas para mejorar el estado nutricional de los pacientes críticos. Métodos: estudio antes-después con la participación de 19 unidades de cuidados intensivos (UCI) polivalentes en 10 comunidades autónomas. Cinco miembros del comité científico formaron a los formadores mediante presentaciones orales y el algoritmo de SNM. Los formadores fueron responsables de la formación de los intensivistas en sus propias UCI. El cuestionario de 30 ítems fue completado por 179 y 105 intensivistas antes y después de la intervención, respectivamente. Resultados: se observó un aumento del conocimiento en seis (20 %) preguntas específicas relacionadas con el SNM. En 11 ítems (36,6 %), el conocimiento adecuado sobre diferentes aspectos del soporte nutricional que ya estaban presentes antes de la formación se mantuvieron, y en cinco ítems (16,7 %) hubo un aumento de la tasa de respuestas correctas. En cuatro ítems (13,3 %), las respuestas correctas no mejoraron y en otros cuatro (13,3 %), los porcentajes de respuestas correctas disminuyeron. Conclusiones: el algoritmo de SNM ha logrado una sólida consolidación de los principales conceptos de esta estrategia. Algunos aspectos referentes a cómo manejar al paciente desnutrido, cómo identificarlo y qué tipo de nutrición pautar desde el inicio del ingreso en la UCI, los aportes nutricionales en situaciones especiales y el seguimiento de posibles complicaciones como la realimentación, son áreas que requerirían estrategias formativas adicionales. (AU)

Introduction: optimal nutrition in the critically ill patient is a key aspect for recovery. Objectives: to promote training in and knowledge of mixed nutrition support (MNS) by means of a clinical algorithm among intensivists for improving the nutritional status of critically ill patients. Methods: a before-and-after study with the participation of 19 polyvalent intensive care units (ICUs) in 10 autonomous communities. Five members of the scientific committee trained the trainers by means of oral presentations and a clinical algorithm on MNS. Then, trainers were responsible for explaining the algorithm to local intensivists in their ICUs. The 30-item study questionnaire was completed before and after the intervention by 179 and 105 intensivists, respectively. Results: a clear improvement of knowledge was found in six (20 %) specific MNS-related questions. In 11 items (36.6 %), adequate knowledge on different aspects of nutritional support that were already present before the intervention were maintained, and in five items (16.7 %) an improvement in the rate of correct responses was recorded. There were no improvements in correct responses for four items (13.3 %), and for four (13.3 %) additional items the percentage of correct responses decreased. Conclusions: the use of the MNS algorithm has achieved a solid consolidation of the main concepts of MNS. Some aspects regarding how to manage the malnourished patient, how to identify them and what type of nutrition to guide from the beginning of admission to the ICU, nutritional contributions in special situations, and the monitoring of possible complications such as refeeding are areas for which further training strategies are needed. (AU)

Role of Elevated Aminotransferases in ICU Patients with Rhabdomyolysis.

To evaluate whether patients with rhabdomyolysis and serum alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) higher than 1000 IU/L had higher mortality that patients with low aminotransferases. Retrospective analysis of intensive care unit patients with rhabdomyolysis [creatine kinase (CK) higher than 5000 IU/L]. Patients were classified in two groups: low aminotransferases group, when AST and ALT were equal or lower to 1000 IU/L, and elevated aminotransferases group, when AST or ALT was above 1000 IU/L. Forty-six out of 189 patients included in the analysis (24.3%) had elevated aminotransferases. The mortality of patients with rhabdomyolysis was 25.9 per cent, being higher in patients with elevated aminotransferases compared with patients with low aminotransferases (60.9% vs 14.7%; P < 0.001). Mortality stratified by quartiles of CK in patients with low aminotransferases was independent of the level of CK (P = 0.67). Logistic regression analysis showed that the independent variables associated with mortality were Simplified Acute Physiology Score II [1.11 (1.07-1.16) for each point of increase, P < 0.001], the international normalized ratio value [4.2 (1.6-10.7) for each point of increase, P = 0.003], and the need of renal replacement therapy [5.4 (1.7-17.2), P = 0.004]. Patients with rhabdomyolysis with elevated serum aminotransferases had higher mortality than patients with low serum aminotransferase levels.

A SIMPLIFIED EQUATION FOR TOTAL ENERGY EXPENDITURE IN MECHANICALLY VENTILATED CRITICALLY ILL PATIENTS.

INTRODUCTION: "tight calorie control" concept arose to avoid over- and under-feeding of patients. OBJECTIVE: to describe and validate a simplified predictive equation of total energy expenditure (TEE) in mechanically ventilated critically ill patients. METHODS: this was a secondary analysis of measurements of TEE by indirect calorimetry in critically ill patients. Patients were allocated in a 2:1 form by a computer package to develop the new predictive equation TEE (prediction cohort) and the validation cohort. Indirect calorimetry was performed with three different calorimeters: the Douglas-bag, a metabolic computer and the CalorimetR. We developed a new TEE predictive equation using measured TEE (in kcal/kg/d) as dependent variable and as independent variables different factors known to influence energy expenditure: age, gender, body mass index (BMI) and type of injury. RESULTS: prediction cohort: 179 patients. Validation cohort: 91 patients. The equation was: TEEPE (kcal/Kg/d) = 33 - (3 x A) - (3 x BMI) - (1 x G). Where: A (age in years): ≤ 50 = 0; > 50 = 1. BMI (Kg/m2): 18.5 - 24.9 = 0; 25 - 29.9 = 1; 30 - 34.9 = 2; 35 - 39.9 = 3. G (gender): male = 0; female = 1. The bias (95% CI) was -0.1 (-1.0 - 0.7) kcal/kg/d and the limits of agreement (} 2SD) were -8.0 to 7.8 kcal/kg/d. Predicted TEE was accurate (within 85% to 115%) in 73.6% of patients. CONCLUSION: the new predictive equation was acceptable to predict TEE in clinical practice for most mechanically ventilated critically ill patients.

Introducción: el concepto de "control calorico estricto" surgio para evitar la excesiva y la deficiente nutricion de los pacientes. Objetivo: describir y validar una ecuacion simplificada para el calculo del gasto energetico total (GET) en pacientes criticos con ventilacion mecanica. Métodos: analisis secundario de las mediciones de GET por calorimetria indirecta en pacientes criticos. Los pacientes fueron asignados de forma 2:1 por un paquete estadistico; el primer grupo se empleo para desarrollar la nueva ecuacion predictiva del GET (grupo predictivo) y el segundo para validarla (grupo validacion). La calorimetria indirecta se realizo con tres calorimetros diferentes: la bolsa de Douglas, un computador metabolico y el equipo CalorimetR. Hemos desarrollado la nueva ecuacion predictiva del GET utilizando el GET medido (en kcal/kg/d), como variable dependiente, y como variables independientes los diferentes factores que influyen en el gasto energetico: edad, genero, indice de masa corporal (IMC) y tipo de lesion. Resultados: el grupo de prediccion incluyo 179 pacientes y el de validacion 91 pacientes. La ecuacion predictiva fue: GETEP = 33 - (3 x E) - (3 x IMC) - (1 x G). Donde: E (edad en anos): ≤ 50 = 0; > 50 = 1. IMC (kg / m2): 18,5- 24,9 = 0; 25-29,9 = 1; 30-34,9 = 2; 35-39,9 = 3. G (genero): hombre = 0; mujer = 1. El sesgo (IC del 95%) entre el GET medido y el predicho fue de -0,1 (-1,0 a 0,7) kcal/ kg/dia y los limites de acuerdo (} 2SD) fueron -8,0 a 7,8 kcal/kg/d. El GET por la ecuacion predictiva fue preciso (entre el 85% y el 115%) en el 73,6% de los pacientes. Conclusiones: La nueva ecuacion predictiva fue aceptable para predecir el GET de la mayoria de pacientes criticos con ventilacion mecanica en la practica clinica.

A simplified equation for total energy expenditure in mechanically ventilated critically ill patients / Ecuación simplificada para el cálculo del gasto energético total en pacientes críticos con ventilación mecánica

Introduction: 'tight calorie control' concept arose to avoid over- and under-feeding of patients. Objective: to describe and validate a simplified predictive equation of total energy expenditure (TEE) in mechanically ventilated critically ill patients. Methods: this was a secondary analysis of measurements of TEE by indirect calorimetry in critically ill patients. Patients were allocated in a 2:1 form by a computer package to develop the new predictive equation TEE (prediction cohort) and the validation cohort. Indirect calorimetry was performed with three different calorimeters: the Douglas-bag, a metabolic computer and the Calorimet®. We developed a new TEE predictive equation using measured TEE (in kcal/kg/d) as dependent variable and as independent variables different factors known to influence energy expenditure: age, gender, body mass index (BMI) and type of injury. Results: prediction cohort: 179 patients. Validation cohort: 91 patients. The equation was: TEEPE (kcal/Kg/d) = 33 - (3 x A) - (3 x BMI) - (1 x G). Where: A (age in years): ≤ 50 = 0; > 50 = 1. BMI (Kg/m2 ): 18.5 - 24.9 = 0; 25 - 29.9 = 1; 30 - 34.9 = 2;35 - 39.9 = 3. G (gender): male = 0; female = 1. The bias (95% CI) was -0.1 (-1.0 - 0.7) kcal/kg/d and the limits of agreement (± 2SD) were -8.0 to 7.8 kcal/kg/d. Predicted TEE was accurate (within 85% to 115%) in 73.6% of patients. Conclusion: the new predictive equation was acceptable to predict TEE in clinical practice for most mechanically ventilated critically ill patients (AU)

Introducción: el concepto de 'control calórico estricto' surgió para evitar la excesiva y la deficiente nutrición de los pacientes. Objetivo: describir y validar una ecuación simplificada para el cálculo del gasto energético total (GET) en pacientes críticos con ventilación mecánica. Métodos: análisis secundario de las mediciones de GET por calorimetría indirecta en pacientes críticos. Los pacientes fueron asignados de forma 2:1 por un paquete estadístico; el primer grupo se empleó para desarrollar la nueva ecuación predictiva del GET (grupo predictivo) y el segundo para validarla (grupo validación). La calorimetría indirecta se realizó con tres calorímetros diferentes: la bolsa de Douglas, un computador metabólico y el equipo Calorimet®. Hemos desarrollado la nueva ecuación predictiva del GET utilizando el GET medido (en kcal/kg/d), como variable dependiente, y como variables independientes los diferentes factores que influyen en el gasto energético: edad, género, índice de masa corporal (IMC) y tipo de lesión. Resultados: el grupo de predicción incluyó 179 pacientes y el de validación 91 pacientes. La ecuación predictiva fue: GETEP = 33 - (3 x E) - (3 x IMC) - (1 x G). Donde: E (edad en años): ≤ 50 = 0; > 50 = 1. IMC (kg / m2): 18,5- 24,9 = 0; 25-29,9 = 1; 30-34,9 = 2; 35-39,9 = 3. G (género): hombre = 0; mujer = 1. El sesgo (IC del 95%) entre el GET medido y el predicho fue de -0,1 (-1,0 a 0,7) kcal/ kg/día y los límites de acuerdo (± 2SD) fueron -8,0 a 7,8 kcal/kg/d. El GET por la ecuación predictiva fue preciso (entre el 85% y el 115%) en el 73,6% de los pacientes. Conclusiones: La nueva ecuación predictiva fue aceptable para predecir el GET de la mayoría de pacientes críticos con ventilación mecánica en la práctica clínica (AU)