Confirming the validity of the CONUT system for early detection and monitoring of clinical undernutrition: comparison with two logistic regression models developed using SGA as the gold standard.

AIM: To ratify previous validations of the CONUT nutritional screening tool by the development of two probabilistic models using the parameters included in the CONUT, to see if the CONUT´s effectiveness could be improved. METHODS: It is a two step prospective study. In Step 1, 101 patients were randomly selected, and SGA and CONUT was made. With data obtained an unconditional logistic regression model was developed, and two variants of CONUT were constructed: Model 1 was made by a method of logistic regression. Model 2 was made by dividing the probabilities of undernutrition obtained in model 1 in seven regular intervals. In step 2, 60 patients were selected and underwent the SGA, the original CONUT and the new models developed. The diagnostic efficacy of the original CONUT and the new models was tested by means of ROC curves. Both samples 1 and 2 were put together to measure the agreement degree between the original CONUT and SGA, and diagnostic efficacy parameters were calculated. RESULTS: No statistically significant differences were found between sample 1 and 2, regarding age, sex and medical/surgical distribution and undernutrition rates were similar (over 40%). The AUC for the ROC curves were 0.862 for the original CONUT, and 0.839 and 0.874, for model 1 and 2 respectively. The kappa index for the CONUT and SGA was 0.680. CONCLUSIONS: The CONUT, with the original scores assigned by the authors is equally good than mathematical models and thus is a valuable tool, highly useful and efficient for the purpose of Clinical Undernutrition screening.

Confirming the validity of the CONUT system for early detection and monitoring of clinical undernutrition; comparison with two logistic regression models developed using SGA as the gold standard / Confirmando la validez del sistema CONUT para la detección precoz de la desnutrición clínica. Comparación con dos modelos de regresión logística desarrollados usando el SGA como gold standard

Aim: To ratify previous validations of the CONUT nutritional screening tool by the development of two probabilistic models using the parameters included in the CONUT, to see if the CONUT's effectiveness could be improved. Methods: It is a two step prospective study. In Step 1, 101 patients were randomly selected, and SGA and CONUT was made. With data obtained an unconditional logistic regression model was developed, and two variants of CONUT were constructed: Model 1 was made by a method of logistic regression. Model 2 was made by dividing the probabilities of undernutrition obtained in model 1 in seven regular intervals. In step 2, 60 patients were selected and underwent the SGA, the original CONUT and the new models developed. The diagnostic efficacy of the original CONUT and the new models was tested by means of ROC curves. Both samples 1 and 2 were put together to measure the agreement degree between the original CONUT and SGA, and diagnostic efficacy parameters were calculated. Results: No statistically significant differences were found between sample 1 and 2, regarding age, sex and medical/surgical distribution and undernutrition rates were similar (over 40%). The AUC for the ROC curves were 0.862 for the original CONUT, and 0.839 and 0.874, for model 1 and 2 respectively. The kappa index for the CONUT and SGA was 0.680. Conclusions: The CONUT, with the original scores assigned by the authors is equally good than mathematical models and thus is a valuable tool, highly useful and efficient for the purpose of Clinical Undernutrition screening (AU)

Objetivo: Ratificar validaciones previas del sistema de cribado nutricional CONUT, mediante el desarrollo de dos modelos probabilísticos usando los parámetros incluidos en el CONUT, para ver si la efectividad del CONUT puede ser mejorada. Métodos: Estudio prospectivo en dos fases. En la fase I se seleccionaron 101 pacientes al azar, y se les hicieron SGA y CONUT. Con estos datos se fabricó un modelo de regresión logística incondicional, y se construyeron dos variantes del CONUT. El modelo 1 se hizo mediante regresión logística. El modelo 2 se hizo dividiendo las probabilidades de desnutrición obtenidas en el modelo 1 en siete intervalos regulares. En la fase 2, se seleccionaron 60 pacientes, y se les hizo el SGA, CONUT y los nuevos modelos desarrollados. La eficacia diagnóstica del CONUT original y de los nuevos modelos se estudió mediante curvas ROC. Se juntaron las muestras 1 y 2 para medir el grado de acuerdo entre el CONUT original y el SGA, y se calcularon los índices de eficacia. Resultados: No se encontraron diferencias significativas entre las muestras 1 y 2, en cuanto a la distribución de sexos y servicios, las tasas de desnutrición fueron similares (alrededor del 40%). El AUC para las curvas ROC fueron 0,862 para el CONUT original, y 0,839 y 0,874 para modelos 1 y 2 respectivamente. El índice kappa entre el CONUT y el SGA fue 0,680. Conclusión: El CONUT, con las puntuaciones asignadas originalmente por los autores, es tan bueno como los modelos matemáticos y por tanto, válido, muy útil y eficiente para el cribado de la desnutrición Clínica (AU)

The use of biochemical and immunological parameters in nutritional screening and assessment.

OBJECTIVE: To evaluate the relationship between serum albumin, total cholesterol and total lymphocyte count with two nutritional assessment methods, to verify if their use is justified in nutritional screening tools. METHODS: 101 patients admitted to medical/surgical wards underwent the SGA and the Full Nutritional Assessment (FNA). Blood test which included serum albumin, total cholesterol and total lymphocyte count (TLC), were made. Percentage of weight loss and BMI were calculated. An Anova test was done to measure the differences in the mean levels of the three parameters for the nutritional status evaluated by SGA and FNA. The probability of a patient being malnourished in the four ranges established for each parameter was calculated, as well as the relationship between the ranges and the percentage of weight loss and BMI. Sensitivity and specificity were calculated and the corresponding ROC curves, using SGA as gold standard. RESULTS: Prevalence of undernutrition is 43.6% and 44.6% for SGA and FNA respectively. Mean levels of the three parameters decrease as the undernutrition degree increases (p < 0.005 for all cases). The probability of a patient being malnourished gets higher as parameter lowers (p = 0.000 for all cases). Total cholesterol shows a relationship with BMI < 18.5 and presence/absence of weight loss (p = 0.074 and p = 0.002 respectively). The area under ROC curves are albumin (0.823), cholesterol (0.790) and TLC (0.758) respectively. CONCLUSIONS: The analytical parameters analyzed show a statistically significant relationship with the nutritional status. Therefore, they are suitable for use in nutritional screening.

The use of biochemical and immunological parameters in nutritional screening and assessment / Utilidad de los parámetros bioquímicos e inmunológicos en la evaluación y cribado nutricional

Objective: To evaluate the relationship between serum albumin, total cholesterol and total lymphocyte count with two nutritional assessment methods, to verify if their use is justified in nutritional screening tools. Methods: 101 patients admitted to medical/surgical wards underwent the SGA and the Full Nutritional Assessment(FNA). Blood test which included serum albumin, total cholesterol and total lymphocyte count (TLC), were made. Percentage of weight loss and BMI were calculated. An Anova test was done to measure the differences in the mean levels of the three parameters for the nutritional status evaluated by SGA and FNA. The probability of a patient being malnourished in the four ranges established for each parameter was calculated, as well as the relationship between the ranges and the percentage of weight loss and BMI. Sensitivity and specificity were calculated and the corresponding ROC curves, using SGA as gold standard. Results: Prevalence of under nutrition is 43.6% and 44.6% for SGA and FNA respectively. Mean levels of the three parameters decrease as the under nutrition degree increases (p < 0.005 for all cases). The probability of a patient being malnourished gets higher as parameter lowers(p = 0.000 for all cases). Total cholesterol shows a relationship with BMI ¡Ü 18.5 and presence/absence of weight l oss (p = 0.074 and p = 0.002 respectively). The area unde ROC curves are albumin (0.823), cholesterol (0.790) and TLC (0.758) respectively. Conclusions: The analytical parameters analyzed show a statistically significant relationship with the nutritional status. Therefore, they are suitable for use in nutritional screening (AU)

Objetivo: evaluar la relación entre albúmina sérica, colesterol total y linfocitos totales y dos métodos de evaluación nutricional, para verificar si su uso en las herramientas de cribado nutricional está justificado. Métodos: a 101 pacientes de servicios médicos y quirúrgicos se les realizó el SGA y la Valoración del Estado nutricional Completa (VEN). Se les realizaron análisis de albúmina sérica, colesterol total y linfocitos totales. Se calculó el porcentaje de pérdida de peso y el IMC. Las diferencias entre los niveles medios de los tres parámetros en los distintos niveles nutricionales evaluados por SGA y VEN se hizo mediante el test de ANOVA. Se calculó la probabilidad de estar desnutrido en los cuatros rangos establecidos para cada parámetro, así como la relaciones entre esos rangos y el porcentaje de pérdida de peso y el IMC. Se calculó la sensibilidad y especificidad y sus curvas ROC correspondientes, tomando el SGA como gold standard. Resultados: La prevalencia de desnutrición es 43,6% (SGA) y 44,6% (VEN). Los valores medios de los tres parámetros disminuyen según aumenta el grado de desnutrición (p < 0,005). La probabilidad de que un paciente esté desnutrido aumenta a medida que disminuyen los niveles de los parámetros (p = 0,000 para los tres). El colesterol total se relaciona con el IMC < 18,5 y con la presencia/ausencia de pérdida de peso (p = 0,790 y p = 0,002 respectivamente). Conclusiones: Los parámetros analíticos analizados muestran una relación significativa con el estado nutricional y por tanto son válidos para su uso en el cribado de desnutrición (AU)

CONUT: a tool for controlling nutritional status. First validation in a hospital population.

BACKGROUND: The serious problem of hospital undernutrition is still being underestimated, despite its impact on clinical evolution and costs. The screening methods developed so far are not useful for daily clinical practice due to their low effectiveness/cost ratio. OBJECTIVE: We present an screening tool for CONtrolling NUTritional status (CONUT) that allows an automatic daily assessment of nutritional status of all inpatients that undergo routine analysis. DESIGN: The system is based on a computer application that compiles daily all useful patient information available in hospital databases, through the internal network. It automatically assesses the nutritional status taking into account laboratory information including serum albumin, total cholesterol level and total lymphocyte count. We have studied the association between the results of the Subjective Global Assessment (SGA) and Full Nutritional Assessment (FNA) with those from CONUT, in a sample of 53 individuals. RESULTS: The agreement degree between CONUT and FNA as measured by kappa index is 0.669 (p = 0.003), and between CONUT and SGA is 0.488 (p = 0.034). Considering FNA as "gold standard" we obtain a sensitivity of 92.3 and a specificity of 85.0. CONCLUSIONS: CONUT seems to be an efficient tool for early detection and continuous control of hospital undernutrition, with the suitable characteristics for these screening functions.

CONUT: a tool for Controlling Nutritional Status. First validation in a hospital population

Antecedentes: El grave problema de la desnutrición hospitalaria sigue siendo infravalorado, pese a sus repercusiones sobre la evolución clínica y los costes de la hospitalización.Los procedimientos de filtro desarrollados hasta ahora no son útiles para la práctica diaria por su baja relación efectividad/costo.Objetivo: Presentamos un sistema de cribado para el CONtrol NUTricional que permite valorar a diario, de manera automática, la situación nutricional de la totalidad de los pacientes ingresados a los que se practica análisis de rutina.Diseño: El sistema se basa en una aplicación informática que recopila a diario, a través de la red interna, aquellos datos de los pacientes ingresados que se consideran útiles para evaluar su estado nutricional y que están disponibles en bases de datos del hospital. Automáticamente determina la situación nutricional de los pacientes considerando los datos de laboratorio: albúmina, colesterol y linfocitos totales. Hemos estudiado la asociación entre los resultados del Subjective Global Assessment (SGA) y delFull Nutritional Assessment (FNA) con aquellos del CONUT,en una muestra de 53 individuos.Resultados: El grado de concordancia entre el CONUTy el FNA, medido por el índice kappa es de 0,699 (p= 0,003), y entre el CONUT y el SGA es de 0,488(p = 0,034). Si consideramos que el FNA es la “prueba de referencia”, obtenemos una sensibilidad del 92,3 y una especificidad del 85,0.Conclusiones: Parece que CONUT es una herramienta eficaz para la detección precoz y el control continuo de la desnutrición hospitalaria, con las características adecuadas a las funciones de cribado (AU)

Background: The serious problem of hospital undernutrition is still being underestimated, despite its impact on clinical evolution and costs. The screening methods developed so far are not useful for daily clinical practice due to their low effectiveness/cost ratio.Objective: We present an screening tool for CONtrolling NUTritional status (CONUT) that allows an automatic daily assessment of nutritional status of all inpatients that undergo routine analysis. Design: The system is based on a computer application that compiles daily all useful patient information available in hospital databases, through the internal network.It automatically assesses the nutritional status taking into account laboratory information including serum albumin, total cholesterol level and total lymphocyte count. We have studied the association between the results of the Subjective Global Assessment (SGA)and Full Nutritional Assessment (FNA) with those from CONUT, in a sample of 53 individuals. Results: The agreement degree between CONUT andFNA as measured by kappa index is 0.669 (p = 0.003),and between CONUT and SGA is 0.488 (p = 0.034). ConsideringFNA as “gold standard” we obtain a sensitivity of 92.3 and a specificity of 85.0. Conclusions: CONUT seems to be an efficient tool for early detection and continuous control of hospital undernutrition,with the suitable characteristics for these screening functions (AU)