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Antecedentes: El promedio móvil de pacientes (MA) es una estrategia de control de calidad que utiliza el resultado medio del paciente para monitorear continuamente el rendimiento del análisis. El desarrollo de protocolos de MA sensibles que detectan rápidamente errores sistemáticos (SE) es un reto. Se comparan los protocolos de MA establecidos utilizando un informe previamente publicado como guía y se demuestra el uso de un algoritmo de recocido simulado (SA) para optimizar el rendimiento del protocolo MA. Métodos: Usando 400 días de datos de pacientes, desarrollamos protocolos de MA para 23 ensayos. MA protocolos desarrollados utilizando un informe publicado anteriormente y nuestro algoritmo SA se compararon utilizando el número promedio de muestras de pacientes afectados hasta la detección de errores (ANP ed). Resultados: La comparación de las estrategias demostró que los protocolos desarrollados utilizando el algoritmo SA generalmente demostraron ser superiores. Algunos analitos como la proteína total mostraron una mejora considerable, con una SE positiva igual a 0,8 g / dL detectada con una ANP ed de 135 muestras utilizando el método publicado anteriormente mientras que el algoritmo SA detectó esta SE con una ANP ed de 18. No todos los analitos demostraron Mejora similar con el algoritmo SA. El fósforo, por ejemplo, demostró sólo mejoras menores, con un SE positivo de 0,9 mg / dL detectado con un ANP ed de 34 utilizando el método publicado anteriormente frente a un ANP ed de 29 utilizando el algoritmo SA. También demostrar un ejemplo de detección SE en un entorno en vivo utilizando el algoritmo SA derivados de los protocolos de MA. Conclusiones: El algoritmo de SA desarrollado desarrolla protocolos de MA en nuestro laboratorio y detectan rápidamente SE, reduciendo el número de muestras que requieren corrección y mejorando la seguridad del paciente. Recibido para publicación el 2 de marzo de 2016. Aceptado para su publicación el 6 de julio de 2016. 2016 Asociación Americana de Química ClínicaOptimización de un Programa de Medias Móviles Usando un Algoritmo de Recolección Simulado: El Objetivo es Monitorear el Proceso No los Pacientes (MA) es una estrategia de control de calidad utilizando el resultado medio del paciente para monitorear continuamente el rendimiento del ensayo. El desarrollo de protocolos de MA sensibles que detectan rápidamente errores sistemáticos (SE) es un reto. Se comparan los protocolos de MA establecidos utilizando un informe previamente publicado como guía y se demuestra el uso de un algoritmo de recocido simulado (SA) para optimizar el rendimiento del protocolo MA. Métodos: Usando 400 días de datos de pacientes, desarrollamos protocolos de MA para 23 ensayos. MA protocolos desarrollados utilizando un informe publicado anteriormente y nuestro algoritmo SA se compararon utilizando el número promedio de muestras de pacientes afectados hasta la detección de errores (ANP ed). RESULTADOS: La comparación de las estrategias demostró que los protocolos desarrollados utilizando el algoritmo SA generalmente demostraron ser superiores. Algunos analitos como la proteína total mostraron una mejora considerable, con una SE positiva igual a 0,8 g / dL detectada con una ANP ed de 135 muestras utilizando el método publicado anteriormente mientras que el algoritmo SA detectó esta SE con una ANP ed de 18. No todos los analitos demostraron Mejora similar con el algoritmo SA. El fósforo, por ejemplo, demostró sólo mejoras menores, con un SE positivo de 0,9 mg / dL detectado con un ANP ed de 34 utilizando el método publicado anteriormente frente a un ANP ed de 29 utilizando el algoritmo SA. También demostrar un ejemplo de detección SE en un entorno en vivo utilizando el algoritmo SA derivados de los protocolos de MA. CONCLUSIONES: Los algoritmos desarrollados por el SA en protocolos MA están actualmente en uso en nuestro laboratorio y detectan rápidamente SE, reduciendo el número de muestras que requieren corrección y mejorando la seguridad del paciente. Existen numerosas estrategias de QC que se basan en la alteración de la frecuencia del análisis de QC líquido para monitorear los ensayos de química (1. 2). A pesar de la conciencia de estas estrategias, muchos laboratorios de química clínica medir sólo 2 concentraciones de QC una vez por 24 h. Esta frecuencia, aunque suficiente para cumplir con las normas de los organismos reguladores, es insuficiente para detectar rápidamente el error sistemático (SE). Debido a que SE puede desarrollarse en cualquier momento entre los eventos de control de calidad, el SE puede pasar desapercibido durante horas, afectando a cientos de resultados de pacientes. El aumento de la frecuencia de control de calidad mejora la velocidad de detección de SE, pero también aumenta el costo por el consumo de material de QC, reactivos y tiempo técnico requerido para realizar y registrar los resultados de control de calidad. En resumen, aunque el análisis del material QC es valioso, proporciona sólo una instantánea del rendimiento del ensayo. Los materiales QC también pueden carecer de conmutabilidad al suero nativo porque se fabrican mediante la adición de componentes exógenos a una matriz de suero de base. Estos materiales QC pueden diferir sustancialmente del suero nativo y esta falta de conmutabilidad puede enmascarar la degradación en el rendimiento del ensayo (3, 5). Debido a estas deficiencias algunas publicaciones han propuesto supervisar el valor medio del paciente para los analitos de la química como una estrategia complementaria de control de calidad (6, 13]. Los promedios móviles (MA), o alternativamente el promedio de los normales, pueden detectar SE antes del siguiente evento QC, minimizando el número de resultados de pacientes no confiables informados. En 1965 se publicó el primer artículo que proponía MA para analitos químicos, y los artículos posteriores han refinado el concepto variando el número de muestras de pacientes promediado, aplicando límites de truncamiento (TL) para excluir los valores atípicos o utilizando cálculos exponencialmente ponderados MA (EWMA) 13). El establecimiento de protocolos MA requiere la selección de los límites de control (CL), o la magnitud SE tolerable, el número de resultados de los pacientes a la media (N) y las concentraciones en las que TL se colocan para minimizar el efecto de los valores atípicos. De las técnicas previamente publicadas para seleccionar N exploramos la estrategia publicada en 1984 por Cembrowski y colaboradores (7). En esta estrategia N se selecciona calculando la relación de la población de pacientes SD (Sp) con la imprecisión analítica SD del ensayo (Sa). La relación Sp / Sa cuando se aplica con un nomograma en la misma publicación guía la selección de N (7). Desarrollamos protocolos de MA utilizando esta estrategia para varios analitos químicos y además aprovechamos un algoritmo de recocido simulado (SA) para optimizar nuestros protocolos de MA. El rendimiento de los protocolos desarrollados utilizando ambas estrategias se evaluó mediante la simulación de las condiciones SE en los datos históricos del paciente y el cálculo del número medio de muestras de pacientes afectados hasta que se detectó la SE inducida (ANP ed). Materiales y Métodos DATOS DEL PACIENTE Y PRUEBAS EVALUADOS Cuatrocientos días de los resultados de los pacientes recogidos con el programa Data Innovations Middleware versión 8.10 (Data Innovations) fueron almacenados como archivos de valores separados por comas desde febrero de 2012 hasta marzo de 2013 y compilados a través de Matlab versión R2012a (Mathworks) . Se excluyeron las muestras de los pacientes de 18 a 18 años de edad, el servicio de urgencias y la clínica de hematología, debido a que los pacientes pediátricos tienen diferencias relacionadas con la edad en muchos pacientes de hematología clínica que frecuentemente presentan concentraciones séricas de proteínas y / o calcio anormalmente altas o bajas. Los pacientes con ED pueden presentar anomalías en bases ácidas y electrolitos que difieren sustancialmente de pacientes estables en hospitalización o ambulatorios. Después de la exclusión de la anterior y todos los QC y duplicar los valores de los pacientes, la matriz final contenía 1915999 resultados. Los ensayos que pasaron a través de middleware con volúmenes de prueba diarios de muestras gt100, así como proteína C reactiva de alta sensibilidad (hsCRP), tiroxina libre, hierro y lipasa, que tenían volúmenes semanales promedio de aproximadamente 300, 230, 200 y 150 muestras, Respectivamente, fueron investigados para la adecuación MA (Tabla 1]. Los analitos con grandes diferencias sexuales como la creatina quinasa no fueron investigados debido a que las fluctuaciones en la distribución del sexo producirían cambios impredecibles en valores medios no relacionados con el rendimiento analítico. Después de la exclusión de la pediatría, la clínica de hematología y los pacientes con ED el menor número de resultados para los ensayos investigados fue para la lipasa con 4101 resultados y el mayor fue el cloruro con 139180 resultados. Comparación de guiado vs algoritmo optimizado protocolos. A DETERMINACIÓN MANUAL DE LOS PARÁMETROS DE MOVIMIENTO DE MEDIDAS Los CLM de MA se determinaron calculando el valor de cambio de referencia (RCV) utilizando la fórmula RCV. Donde Z 1,96 para 2 SD cambian en una distribución de 2 colas CV a 2 CV del ensayo CV i 2 dentro de la variación biológica individual. CL fueron, por tanto, el valor medio del paciente el valor de RCV. La CV analítica se determinó mediante la revisión de los datos históricos del material QC más cercano al valor medio del paciente para cada ensayo. La variación biológica intraindividual se obtuvo a partir de la base de datos de variación biológica deseada compilada en westgard (14). La longitud del filtro o N se determinó como se describe por Cembrowski et al. (7). Brevemente, los datos del paciente se representaron en una tabla de distribución de frecuencia y se calcularon los medios de ensayo individuales y Sp. Sa se determinó mediante la revisión de datos históricos de QC más cercanos al valor medio del paciente para cada ensayo. La relación Sp / Sa se calculó y se redondeó al número entero más próximo antes de su aplicación al nomograma (7). Las poblaciones internadas y ambulatorias se analizaron por separado para algunos analitos. Para esos ensayos, los datos se subdividieron en matrices de paciente internado y ambulatorio, trazadas en tablas separadas de distribución de frecuencia y medias ambulatorias y de pacientes hospitalizados y Sp determinadas antes del cálculo de la relación Sp / Sa. Las TL sirven para excluir valores extremos del cálculo de MA. Los valores que caen fuera de los TL no se incluyen en el valor medio calculado del paciente. Cuando la distribución de datos fue aproximadamente simétrica TLs se colocaron 4 SD de la media del paciente valor, una práctica influenciada por el middleware proveedor de formación. El valor TL en distribuciones sesgadas fue el punto de inflexión definido por el usuario para el cual la curva de distribución de frecuencia comenzó a nivelarse. Este valor se definió mediante la inspección visual de la distribución y no se intentó optimizar. El porcentaje de valores de los pacientes excluidos por los TL fue variable y osciló entre un mínimo de 0,3 de hierro a una concentración de 170 g / dL a un máximo de 58 para la albúmina a una concentración de 3,6 g / dL. Los porcentajes de muestras de pacientes excluidos para TLs determinados manualmente y algoritmo derivado de SA se enumeran en la Tabla 1 del Suplemento de Datos que acompaña la versión en línea de este artículo en www.clinchem.org/content/vol62/issue10. EVALUACIÓN DE LOS PROMEDIOS DE MOVIMIENTO RENDIMIENTO DEL PROTOCOLO Se calculó el ANP por inducción de un paso analítico en la matriz de datos del analito. SE se indujo después de las primeras 100 muestras de pacientes. El número de muestras de pacientes afectadas por SE se calculó como el número de muestras de pacientes individuales que caían entre el punto de inducción de error y el primer valor de MA que excede el CL. Se excluyeron valores de muestra que excedían los TLs del cálculo del valor medio del paciente, pero se incluyeron en el número de muestras afectadas por el SE. Este proceso se repitió con SE inducido desplazado a puntos de datos posteriores y el número de resultados de pacientes afectados promediados para calcular ANP ed. El número total de iteraciones para calcular el ANP dependía del tamaño del conjunto de datos para cada analito, el más pequeño de los cuales era 40 para la lipasa y el mayor de los cuales como 1390 para el cloruro. Por ensayo y error se encontró que el submuestreo por un intervalo de 100 resultados permitió una variación mínima de ANP ed vs el costo computacional de submuestreo más frecuente. Los pasos analíticos se indujeron como fracciones de CL y aumentaron hasta 4 veces CL. El ANP ed para SE igual a CL se utilizó para comparar el rendimiento a través de diferentes protocolos. Otros perfiles de error tales como una deriva gradual que reflejaría más estrechamente el desarrollo natural de SE no se intentaron. OPTIMIZACIÓN A TRAVÉS DEL ALGORITMO DE ANNEALIZACIÓN SIMULADA Para mejorar el rendimiento del protocolo inicial se implementó un algoritmo SA en MatLab que varió estocásticamente los valores de N y TLs superior e inferior (TL H y TL L respectivamente) manteniendo la constante de CL. El algoritmo SA fue diseñado para encontrar valores óptimos de N, TL H y TL L a través de la minimización de una función de coste dada como: (1) donde es un factor de ponderación definido por el usuario diseñado para forzar una baja tasa de error falso positivo (FP rate ). Para todos los analitos, se fijó arbitrariamente en 10000 para penalizar la tasa de FP a un costo de un ANP ed más alto. En este análisis la tasa de FP fue cero para todos los protocolos y ANP ed es típicamente un valor entre 0 y los 100s bajos. La selección de lt10000 puede haber resultado en una menor ANP ed, pero también habría probablemente han dado lugar a una mayor tasa de FP sin embargo, esto no se probó. La selección de un gt10000 también puede afectar a ANP ed, pero en nuestro conjunto de datos a de 10000 resultó en protocolos con una tasa de FP igual a cero y como tal un mayor probablemente no producir cambios sustanciales en el rendimiento. Subdividimos los 400 días de datos en diez conjuntos de 40 días y optimizado a través del algoritmo SA para una validación cruzada de 10 veces. Cada conjunto de condiciones optimizadas se probó con respecto al conjunto completo de datos. Para la sencillez de reporte, informamos solamente datos del bloque 10 a menos que las condiciones de optimización en el bloque 10 produjeran un protocolo que permitiera que la media excediera a CL dentro de las primeras 100 muestras de pacientes del conjunto de datos completo. Cuando esto ocurrió, informamos sobre el rendimiento del bloque 9. Este algoritmo SA típicamente logró convergencia a 20003000 iteraciones, mientras que una búsqueda completa de todas las combinaciones posibles de N, TL H. Y TL L requeriría al menos 50000iteraciones para enumerar el espacio de búsqueda típico de los parámetros. MA protocolos diseñados en este estudio se emplean en nuestro laboratorio utilizando Data Innovations Middleware Versión 8.13. RESULTADOS RENDIMIENTO DE LOS PROTOCOLOS GUIADOS DE SP / SA Para cada analito enumerado en la Tabla 1 se establecieron las siguientes características del protocolo: valor medio del paciente, CL, N y TL como se indica en Materiales y Métodos. Usando MatLab calculamos ANP ed para una SE igual a la CL para un analito dado. La ANP para estos protocolos iniciales varió desde un mínimo de 22 para el colesterol HDL hasta un máximo de 7249 para la bilirrubina total (Tabla 1. protocolos originales). Hubo 5 analitos, alanina aminotransferasa (ALT), nitrógeno ureico en sangre (BUN), calcio sérico, hsCRP, glucosa y hormona estimulante del tiroides (TSH), para los cuales nuestros protocolos originales no detectaron SE igual a CL. Se sospecha que el mal desempeño de algunos protocolos se atribuye a las diferencias en la distribución de analitos entre pacientes hospitalizados y ambulatorios, como los del calcio sérico (Fig. 1) con la concentración media de calcio en el hospital de 8,6 mg / dl y la media ambulatoria igual A 9,6 mg / dl. Estas poblaciones están ampliamente separadas por el tiempo, predominando las poblaciones de pacientes hospitalizados en las primeras horas de la mañana y las poblaciones ambulatorias que dominan el resto del día. La separación temporal de los pacientes hospitalizados y ambulatorios se demuestra en una figura animada disponible en línea. 1. La Fig. 1. Gráficos de distribución de frecuencia. Los gráficos de distribución de frecuencia para 400 días de datos de pacientes para (A), sodio, (B), potasio, (C), calcio, (D), bicarbonato, (E), AST, (F), creatinina, Proteína total, y (H) tiroxina libre. La distribución naranja representa todos los datos del paciente púrpura representa el ambulatorio, y verde el subgrupo de pacientes hospitalizados de todos los pacientes. Para mejorar la detección SE se crearon 2 protocolos MA adicionales para analitos seleccionados para el seguimiento ambulatorio y las poblaciones de pacientes hospitalizados por separado. Los protocolos separados produjeron reducciones dramáticas en ANP ed para calcio y proteína total (Tabla 2). La separación de las poblaciones no mejoró universalmente la ANP ed. Los analitos con distribuciones de población similares tuvieron una mejoría marginal de la ANP o se mantuvieron aproximadamente iguales. A la inversa, para algunos analitos como la creatinina, la ANP aumentó después de la separación de las poblaciones ambulatorias y de pacientes hospitalizados. Rendimiento del protocolo guiado: detección de errores sistemáticos. A PERFORMANCE DE PROTOCOLOS OPTIMIZADOS Para maximizar la detección SE de nuestros protocolos MA se empleó un algoritmo SA en MatLab para seleccionar nuevos N y TL para todas las pruebas de la Tabla 1. El algoritmo SA produjo protocolos con disminuciones considerables en ANP ed (Tabla 1. columna derecha ) Que se destacaron por los analitos con grandes diferencias en las poblaciones ambulatorias y de pacientes hospitalizados, como el calcio y la proteína total y aquellos con distribuciones asimétricas, como aspartato aminotransferasa (AST), creatinina y tiroxina libre. Como puede observarse en la Tabla 1, el valor medio del paciente para algunos analitos cambió después de la optimización un efecto atribuible a diferentes TLs seleccionados por el algoritmo SA, porque la inclusión o exclusión de valores atípicos influyó en el valor medio calculado. Para comparar la detección de SE en las estrategias de MA se informan los valores de ANP ed para SE igual a la CL de cada ensayo en nuestro estudio (Tabla 1]. También se ensayaron incrementos menores de SE por debajo de CL para cada ensayo. En la Fig. 2 la ANP de los ensayos seleccionados se traza contra el aumento de SE. En estos gráficos la ausencia de SE inducida se representa en la mitad del eje x y el aumento de SE positivo y negativo se representa como valores crecientes y decrecientes, respectivamente. Las interrupciones en las curvas ANP ed indican condiciones en las que el protocolo MA no detectó SE. Como era de esperar, ANP ed disminuyó a medida que la magnitud SE aumentó, lo que indica que los protocolos de MA detectado SE, incluso si la magnitud era menor que el CL (Fig. 2]. Para algunos analitos, la ANP comenzó a aumentar de nuevo a medida que el SE creció más que el CL. Este fenómeno se debió a un número creciente de resultados que se situaban fuera de la TL a medida que aumentaba la SE. Aunque este fenómeno fue más evidente en algunos protocolos originales (véase sodio, Fig. 2 A) también ocurrió para algunos protocolos siguiendo la optimización del algoritmo de SA (Figura 2). Higo. 2. Número medio de pacientes afectados hasta que se detectó el error en función de la magnitud del error sistemático inducido. Estos gráficos demuestran la ANP ed de (A), sodio, (B), potasio, (C), calcio, (D), bicarbonato, (E), AST, (F), creatinina, (G), proteína total, Y (H), tiroxina libre para aumentar la magnitud del error sistemático. Las curvas definidas por cuadrados rojos representan los protocolos MA originales y los diamantes azules representan los protocolos desarrollados utilizando el algoritmo SA. En cada gráfico, las CL del ensayo están representadas por un cuadrado abierto o un diamante, respectivamente. Consulte la versión en línea para ver una versión en color de esta figura. Aunque ANP ed disminuyó después de la optimización observamos que la detección de SE positivo y negativo no era igual. Para el calcio, se detectó una SE de 1,0 mg / dL con un ANP ed de 55 muestras y una SE negativa de 1,0 mg / dL produjo un ANP ed de 161 muestras (Tabla 3). Para minimizar esta asimetría, se separaron las poblaciones ambulatorias y de pacientes hospitalizados y se reoptimizó. TSH, tiroxina libre, hsCRP y colesterol HDL fueron excluidos del subanálisis debido a que el número de muestras de pacientes internados en nuestro conjunto de datos fue bajo. Funcionamiento optimizado del protocolo: separación de las poblaciones ambulatorias y de pacientes hospitalizados. A La optimización de las poblaciones ambulatorias y de pacientes hospitalizados redujo aún más las reducciones en la ANP para la mayoría de los analitos (Tabla 3). Al igual que con los protocolos iniciales de MA, se observaron mejoras notables en ANP ed para analitos con grandes diferencias de medias entre poblaciones. Después de la optimización todos los protocolos MA en nuestro laboratorio se actualizaron para que coincidieran con los parámetros seleccionados a través del algoritmo SA. DETECCIÓN DE SE EN UN ENTORNO VIVO Los protocolos de MA definidos anteriormente han estado en uso durante aproximadamente 2 años durante los cuales han detectado varios eventos SE. En la Fig. 3 compartimos un evento representativo. En esta imagen, obtenida del middleware Data Innovations, un electrodo selectivo de iones sodio (cuadrados marrones ISE) experimentó una condición SE positiva que aumentó la concentración media de sodio en gt4 mmol / L (flecha). Los otros ISEs de sodio que no experimentan SE se mantienen más cerca de la media. Después de la recalibración, la concentración media de sodio regresó a la media objetivo. En este ejemplo de la vida real, la condición SE se desarrolló poco después de la calibración de rutina y control de calidad. Debido a que la condición SE se detectó rápidamente sólo 14 muestras de pacientes requerían corrección. Si confiamos únicamente en los métodos tradicionales de QC, muchas más muestras de pacientes podrían haber sido afectadas. Higo. 3. Detección del error sistemático en un entorno vivo. Captura de pantalla de nuestro protocolo ISE de sodio demostrando la detección de errores sistemáticos (flecha) en 1 de 5 ISEs de sodio en nuestro laboratorio. Las combinaciones separadas de símbolos y colores representan diferentes ISE que realizan mediciones de sodio. Cada símbolo individual en este protocolo sólo de pacientes ambulatorios de sodio representa el promedio de 14 resultados de pacientes. La línea verde horizontal en el centro de la gráfica representa la concentración media de sodio en este protocolo. Las líneas rojas horizontales representan las CL del protocolo y las amarillas son iguales a la mitad de las CL. Consulte la versión en línea para ver una versión en color de esta figura. Discusión Hemos demostrado que un algoritmo de SA genera protocolos de MA que detectan rápidamente SE, minimizando el número de resultados de pacientes no confiables informados. Aunque el rendimiento de los algoritmos SA algoritmo optimizado variados que fueron capaces de mejorar la detección SE mediante el desarrollo de protocolos separados ambulatorio y paciente interno. El rendimiento de SA algoritmos derivados de los protocolos de MA fue sustancialmente mejor que la de nuestros protocolos originales. La estrategia de relación de Sp / Sa se puede utilizar para generar protocolos MA, sin embargo, esta estrategia es la más adecuada para analitos con distribuciones normales. Comparación de nuestro estudio con uno previamente publicado demuestra que nuestra estrategia de algoritmo SA detecta SE con un similar ANP ed (8). A diferencia de otras estrategias informadas, la nuestra no generó distribuciones normales de datos (6) o seleccionar aleatoriamente los valores de una distribución (8), sino que optimizó los protocolos en el contexto de las variaciones de los datos naturales del día a día. Para minimizar la tasa de FP, establecimos el factor de ponderación a 10000 en nuestra función de coste del algoritmo SA. Esto forzó la selección de parámetros que redujeron la tasa de FP a cero para todos los protocolos. Una tasa de PF de cero no es estadísticamente posible y es un artefacto de la optimización de nuestros protocolos para el conjunto de datos de 400 días. La aplicación de estos parámetros a otro conjunto de datos produciría una tasa cuantificable de PF. Anecdóticamente hemos experimentado eventos de error falso con estos protocolos en un entorno en vivo. No todas las pruebas de química son susceptibles de monitoreo de MA. SE detección de distorsionado distribuciones fue pobre (Fig. 2 y Tabla 3] y, aunque el algoritmo SA mejorado SE detección, existen oportunidades de mejora. El mal desempeño de la MA de glucosa podría deberse a nuestra incapacidad de separar los resultados de ayuno y no de adiestramiento. Para BUN la limitación probable fue nuestra meta CL estrecha de 2 mg / dL promedio derivado de la RCV. Si hubiéramos seleccionado métodos alternativos para determinar CL, como el Sp, podríamos haber logrado una mejor ANP ed. Pero para la consistencia del estudio esto no fue investigado. Otras técnicas que pueden mejorar la detección de SE como el análisis mediano móvil y EWMA (6.9.15), aunque están disponibles en el software Data Innovations v.8.13, no fueron investigados. Hemos elegido utilizar el algoritmo SA por 4 razones. Primero, dado un conjunto de datos suficientemente grande y un conjunto dado de N y TL, el CL podría ajustarse al valor medio máximo y mínimo del paciente en ausencia de SE inducido para minimizar ANP ed. Sin embargo, la inclusión de CL en la optimización SA aumentaría el tamaño del espacio de búsqueda en aproximadamente 2 órdenes de magnitud. Dadas las limitaciones técnicas de nuestro hardware informático no pudimos intentar este estudio. En segundo lugar, el truncamiento de datos es un proceso inherentemente no lineal, que no puede expresarse en una solución de forma cerrada. En tercer lugar, debido al método de muestreo utilizado, los resultados ANP ed mostrarán variación estocástica sobre la media como una función del punto de inicio y el intervalo de paso. En cuarto lugar, debido a que los datos de laboratorio se registran de forma discreta (por ejemplo, 1,6, 1,7, 1,8), nuestro espacio de búsqueda para TL es similarmente discreto (por ejemplo, 1,65, 1,75, 1,85), simplificando así el tamaño de la búsqueda potencial de valores óptimos. Una limitación del estudio es la exclusión de muestras de pediatría, ED, y la clínica de hematología. Esta decisión se basó en observaciones previas de que las concentraciones de muchos analitos de estos pacientes difieren sustancialmente de las de otras poblaciones. En nuestra experiencia los pacientes de la unidad de hemología de la hematología tienen menores valores de proteína total y calcio. Debido a que usamos personal de flebotomía hospitalaria, tendemos a recibir estas muestras de pacientes en masa y los valores medios de proteína total y de calcio pueden ser marcadamente influenciados por este pequeño grupo de pacientes. Este efecto es bastante pronunciado que unos cuantos pacientes analizados simultáneamente pueden desencadenar un evento de detección falsa. Para mitigar este efecto podría desarrollarse un protocolo de MA que utiliza una concentración media de analito por hora de día. Sin embargo, el software MA actual que empleamos no puede acomodar esta estrategia. Desde un punto de vista práctico, la fiabilidad y la sensibilidad de dicho protocolo dependerían de un esquema de flebotomía invariable. Los flebotomistas tendrían que dibujar cada unidad al mismo tiempo a diario porque la variación de la programación podría afectar el rendimiento del protocolo. Basándonos en estas limitaciones, nuestra estrategia de excluir ciertas poblaciones de pacientes sirve como una alternativa aceptable. Debido a que nuestra estrategia de optimización excluyó a estos pacientes, hemos utilizado similarmente una serie de filtros de exclusión para excluir estos valores de los pacientes de los cálculos de MA utilizados en nuestro middleware. Una limitación adicional a nuestra estrategia de uso de datos en formato nativo es que no podemos excluir la posibilidad de que hubiese eventos SE no detectados en nuestro conjunto de datos. El efecto probable de la inclusión de datos afectados por SE en nuestra optimización sería una menor sensibilidad del protocolo. También inducida SE utilizando una estrategia paso a paso en lugar de una degradación gradual en el rendimiento del ensayo como uno puede experimentar en un entorno del mundo real. Un paso representa el mejor escenario para la detección de SE y se podría suponer que la detección SE llevaría más tiempo si el rendimiento del ensayo degradado gradualmente, pero esta suposición no fue probado. Ambas limitaciones merecen un estudio futuro. Una publicación reciente ha propuesto una liberación de la estrategia de MA posterior en la que la primera muestra analizada no se libera hasta que el resto del bloque de MA se analiza y se encontró que no superen el CL (9). Esta estrategia debería disminuir la probabilidad de reportar resultados erróneos al mismo tiempo que se reducen los costos del análisis frecuente de QC (9). Sin embargo, la liberación de la estrategia posterior retrasaría inicialmente la presentación del primer resultado. Para un gran laboratorio de referencia, este retraso adicional sería inconsecuente debido a un alto volumen de ensayo diario. La liberación de la estrategia posterior, sin embargo, no sería práctica para el laboratorio del hospital porque el primer resultado podría estar pendiente varias horas mientras que el resto del bloque se recoge y analiza. La estrategia estándar de recalcular el valor medio a medida que se libera cada nuevo resultado puede producir una corrección más frecuente de los resultados de los pacientes que la liberación de la estrategia de retroceso, pero nuestros datos que demuestran bajos valores de ANP indican que SE puede detectarse rápidamente minimizando el número de muestras Requiriendo reanálisis y corrección. A nuestro entender este es el primer estudio para utilizar un algoritmo de SA para optimizar los protocolos de MA. Demostramos que el análisis separado de las poblaciones ambulatorias y de pacientes hospitalizados puede mejorar la detección de SE. Hemos utilizado esta estrategia para establecer con éxito un programa de maestría en un laboratorio basado en un hospital que atiende tanto a las poblaciones de pacientes hospitalizados como ambulatorios. Nuestros protocolos de MA monitorean continuamente el desempeño del análisis y permiten la detección de SE antes del próximo evento de control de calidad programado, minimizando el número de muestras de pacientes afectadas. Notas de pie de página 3 Abreviaturas no estándar: SE. Error sistemático MA. Promedio móvil TL. Límite de truncamiento EWMA. Exponencialmente ponderado MA CL. Límite de control N. número de resultados de los pacientes a Sp medio. Población de pacientes SD Sa. Imprecisión analítica SD SA. Recocido simulado ANP ed. Promedio de pacientes afectados hasta que el error detectó ED. Departamento de emergencias hsCRP. Proteína C reactiva de alta sensibilidad RCV. Valor de cambio de referencia TL H. Alto límite de truncamiento TH L. Bajo límite de truncamiento FP tasa. Tasa de falsos positivos BUN. Nitrógeno ureico en sangre TSH. Hormona estimulante de la tiroides AST. Aspartato aminotransferasa ISE. Electrodo selectivo de iones. Contribuciones de los autores: Todos los autores confirmaron haber contribuido al contenido intelectual de este documento y han cumplido con los siguientes 3 requisitos: (a) contribuciones significativas a la concepción y diseño, adquisición de datos o análisis e interpretación (B) redacción o revisión del artículo para contenido intelectual y (c) aprobación final del artículo publicado. Divulgación de los autores o posibles conflictos de interés: Al presentar el manuscrito, todos los autores completaron el formulario de divulgación del autor. Divulgaciones y / o potenciales conflictos de interés: Empleo o Liderazgo: Ninguno declarado. Consultor o Función Asesora: Ninguno declarado. Propiedad: Ninguno declarado. Honorarios: M.A. Cervinski, AACC y Lab RevolutionG2 Inteligencia. Financiamiento de la investigación: No declarado. Testimonio Experto: Ninguno declarado. Patentes: Ninguno declarado. Rol del Patrocinador: No se declaró ningún patrocinador. Recibido para publicación el 2 de marzo de 2016. Aceptado para publicación el 6 de julio de 2016. 2016 American Association for Clinical Chemistry Referencias Parvin CA. Evaluar el impacto de la frecuencia de las pruebas de control de calidad en la calidad de los resultados de los pacientes. Clin Chem 2008 54. 2049 54.Parvin CA, Gronowski AM. Efecto de la duración del análisis en el desempeño del control de calidad (QC) y el proceso de planificación del control de calidad. Clin Chem 1997 43. 2149 54. Rej R, Jenny RW, Bretaudiere JP. Control de calidad en química clínica: caracterización de materiales de referencia. Talanta 1984 31. 851 62.Miller WG, Erek A, Cunningham TD, Oladipo O, Scott MG, Johnson RE. Las limitaciones de conmutabilidad influyen en los resultados del control de calidad con diferentes lotes de reactivos. Clin Chem 2011 57. 76 83.Carobeno A, Guerra E, Ceriotti F. Una forma basada en mecanismos para evaluar la conmutabilidad de los materiales de control para las mediciones enzimáticas. El ejemplo de gamma-glutamiltransferasa. Clin Chim Acta 2013 424. 153 8.Linnet K. La regla de media móvil ponderada exponencialmente (EWMA) comparada con las reglas de control de calidad usadas tradicionalmente. Clin Chem Lab Med 2006 44. 396 9.Cembrowski GS, Chandler EP, Westgard JO. Evaluación del promedio de los procedimientos normalizados de control de calidad y directrices para su implementación. Am J Clin Pathol 1984 81. 492 9. Sí JJ, Ingels SC, Parvin CA. Evaluación del desempeño y planificación de procedimientos de control de calidad basados ​​en el paciente. Am J Clin Pathol 2000 113. 240 8.Fleming JK, Katayev A. Changing the paradigm of laboratory quality control through implementation of real-time test results monitoring: for patients by patients. Clin Biochem 2015 48. 508 13.Hoffmann RG , Waid ME . The average of normals method of quality control. Am J Clin Pathol 1965 43. 134 41.Reed AH . Use of patient data for quality control of clinical laboratory tests. Clin Chem 1970 16. 129 34.Woo J , LeFever D , Winkelman JW . Use of average of normals quality control procedure in the detection and resolution of assay discrepancies. Am J Clin Pathol 1988 89. 125 9.Westgard JO , Smith FA , Mountain PJ , Boss S . Design and assessment of average of normals (AON) patient data algorithms to maximize run lengths for automatic process control. Clin Chem 1996 42. 1683 8.Westgard QC . Desirable biological variation database specifications. www.westgard/biodatabase1.htm ( Accessed March 2016 ).Wilson A , Roberts WL , Pavlov I , Fontenot J , Jackson B . Patient result median monitoring for clinical laboratory quality control. Clin Chim Acta 2011 412. 1441 6.Reforming Laboratory Reimbursement: Issues, Impact and Innovations. A Summary of the Clinical Chemistry Forum Held on November 15, 2001 (Meeting Report) Clinical Chemistry 2002, May, 48, 5 Clinical Chemistry This book is available for download with iBooks on your Mac or iOS device, and with iTunes on your computer. Los libros se pueden leer con iBooks en tu Mac o dispositivo iOS. Description The focus of the American Association for Clinical Chemistrys 11th Clinical Chemistry Forum, held in Alexandria, VA, on November 15, 2001, was on current and future direction of Medicare reimbursement. Reimbursement issues involve coverage policies, particularly as they relate to new tests, and coding system changes, with the continued influence of the Office of the Inspector General (OIG) (1) in subtly defining laboratory policies. The speakers included representatives from the Centers for Medicare and Medicaid Services (CMS), the profession of clinical chemistry, and experts on new test approval and coding issues as well as members of the legal profession with interests in laboratory-related fraud and trends in reimbursement. Appropriately, the lead-off speaker was Jeffrey Kang, Chief Clinical Officer and Director of Office of Clinical Standards and Quality, CMS. Kang, who envisages an expanded role for clinical laboratories in providing physician performance monitoring, discussed CMSs priorities for clinical laboratories. These include coverage for tests, coding and reimbursement, medical review, enforcement of CLIA, and using laboratory data to measure physician performance. Coverage defines which items or services are eligible for reimbursement. Medical review defines when covered services are deemed medically necessary. CMS is moving toward an evidential approach to coverage and, therefore, expects evidence from those seeking coverage that use of a new technology leads to a health outcome at least as good as, if not better than the outcome with existing technology. Available on iPhone, iPad, iPod touch, and Mac. Category: Chemistry Published: May 01, 2002 Publisher: American Association for Clinical Chemistry, Inc. Seller: The Gale Group, Inc. Print Length: 13 Pages Language: English Requirements: To view this book, you must have an iOS device with iBooks 1.3.1 or later and iOS 4.3.3 or later, or a Mac with iBooks 1.0 or later and OS X 10.9 or later. Calificaciones de los clientes No hemos recibido suficientes calificaciones para mostrar un promedio de este libro. More by Clinical Chemistry Reforming Laboratory Reimbursement: Issues, Impact and Innovations. A Summary of the Clinical Chemistry Forum Held on November 15, 2001 (Meeting Report) is available for download from iBooks. Reforming Laboratory Reimbursement: Issues, Impact and Innovations. A Summary of the Clinical Chemistry Forum Held on November 15, 2001 (Meeting Report) is available for download from iBooks.articulo arwma - Clinical Chemistry 43:4 594 601 (1997). The EWMA control chart: properties and comparison with other quality-control procedures by computer simulation Aljoscha Steffen Neubauer A quality-control chart based on exponentially weighted moving averages (EWMA) has, in the past few years, become a popular tool for controlling inaccuracy in industrial quality control. In this paper, I explain the principles of this technique, present some numerical examples, and by computer simulation compare EWMA with other control charts currently used in clinical chemistry. The EWMA chart offers a flexible instrument for visualizing imprecision and inaccuracy and is a good alternative to other charts for detecting inaccuracy, es- pecially where small shifts are of interest. Detection of imprecision with EWMA charts, however, requires spe- cial modification. INDEXING TERMS. statistics bull exponentially weighted mov- ing average bull Shewhart chart, Westgard algorithm com- pared A control chart based on the exponentially weighted moving average (EWMA) was first described by Roberts in 1959 1. 1 Whereas the Shewhart chart takes only the immediate control into consideration for statistical test- ing, the EWMA chart uses the previous values also. In brief, after multiplication by a weighting factor w. the current measurement is added to the sum of all former measurements, which is weighted with (1 2 w ). Thus, at each time t ( t 5 1,2. ), the test statistic z t 5 w x t 1 (1 2 w ) z t 2 1 , with w 01, can be obtained. 2 The com- puted z t values are displayed on a control chart over the course of time. Because the mean x macr t 5 x 1 t 1 x 2 t 1. 1 x nt. / n of the n control observations per run is used, this control chart is called the EWMA- x macr chart. Another way of expressing this is: z t 5 w O i 5 0 t 2 1 1 2 w. i x macr t 2 i 1 1 2 w. t z 0 with the first value z 0 in this sum generally being set to the mean of former observations. This smoothing process means that the contribution of a value to the test statistic decays exponentially by time or by the number of new observations, with the speed of decay being adjustable by the weighting factor. The limits for warning and action of the EWMA chart differ from those of a Shewhart chart and have to be computed separately, as shown later. The EWMA control chart differs from the similar Cusum chart by using the additional weighting factor, which allows the adjustment of shift sensitivity. (Setting the EWMA weighting factor w 5 1 yields a Shewhart control chart.) Because of this flexibility, the EWMA chart has drawn increasing atten- tion in industrial quality-control practice during the past few years, as shown by the number of publications in the Journal of Quality Technology since 1989. Exponential smoothing was first proposed for use in clinical chemistry as early as 1975 2. Cembrowski et al. Esta vista previa tiene secciones borrosas intencionalmente. Sign up to view the full version. Este es el final de la vista previa. Sign up to access the rest of the document.Clinical Research to Clinical Practice Lost in Translation Practice is science touched with emotion. Stephen Paget, 1909 During the 20th century, enormous progress was made in improving the health and therefore the life span of all Americans. The average life expectancy at birth increased by nearly 30 years between 1900 and 2000. Although the largest gains were made in the early part of the century, we still managed to add another 1.5 years between 1990 and 2000. Much of our continued success in extending life expectancy over the past several decades is almost certainly due to research supported by the National Institutes of Health (NIH) and generously funded by the American public. NIH-supported research has not only made possible the development of new and improved treatments for a wide range of human diseases it has also provided the knowledge of disease risk factors needed to formulate effective approaches to prevent them. For example, research supported by the National Heart, Lung, and Blood Institute has identified important cardiovascular risk factors, has established the effectiveness of approaches to prevent or control them, and has assessed the effectiveness of treatment interventions for established disease. As director of the National Heart, Lung, and Blood Institute, I am especially gratified to be able to point out that the lions share of our recent gains in life expectancy in the United States has come from reductions in rates of death from heart disease and stroke. According to data provided by the National Center for Health Statistics, life expectancy increased by six years between 1970 and 2000, and nearly two thirds of that increase can be attributed to reductions in mortality due to cardiovascular disease (Figure 1 Figure 1 Change in U.S. Life Expectancy between 1970 and 2000. ). And although primary prevention has played an important part in the reductions, it appears, at least for coronary heart disease, that secondary prevention and other treatments have had a significantly greater effect. According to one analysis of the decline in mortality due to coronary heart disease that occurred between 1980 and 1990, the reduction was due largely to secondary prevention and other improvements in treatment, with primary prevention accounting for only one quarter of the decline. 1 Still, one might question whether we have enjoyed the maximal return on the more than 250 billion that this country has invested in the NIH since 1950. Consider that in 2000 the life expectancy at birth for men and women in the United States lagged behind that of 22 other countries, ranging from Japan to Israel and including Canada and most of western Europe. If we view the longevity of citizens in our sister nations as an indication of what is possible in the modern world, then we must question why our reality is falling short. Some may believe that the difference between life expectancy in the United States and that in other economically developed countries is largely a manifestation of societal differences. I, however, believe the answer is this: we in the United States, both health providers and members of the public, are not applying what we know. Indeed, medical researchers and public and political leaders are increasingly talking about the lack of success we have had in translating research findings into medical practice and personal behavior. Regardless of the reasons cited for this phenomenon structural, economic, or motivational the result is the same: we are not reaping the full public health benefits of our investment in research. Given the ever-growing sophistication of our scientific knowledge and the additional new discoveries that are likely in the future, many of us harbor an uneasy, but quite realistic, suspicion that this gap between what we know about diseases and what we do to prevent and treat them will become ever wider. And it is not just recent research results that are not finding their way into clinical practice and public health behaviors there is plenty of evidence that old research outcomes have been lost in translation as well. In part 1 of Harrisons Principles of Internal Medicine, entitled Introduction to Clinical Medicine, the editors express their view that the practice of medicine combines both science and art. The role of science in medicine is clear. What may be less clear is the art part of medicine. To the editors of Harrisons, the art of medicine is the combination of medical knowledge, intuition and judgment. Today, everyone recognizes that a great deal of the knowledge element of this combination is there for the taking libraries cannot be built fast enough to keep up with modern scientific output. But moving this knowledge off the shelves and into practice, making it relevant and accessible to practitioners and patients, achieving a true marriage of knowledge with intuition and judgment all this requires translation. And that is, indeed, a delicate and elusive art. Robert Frost, possibly one of the greatest American poets, contended that, Poetry is what is lost in translation. I think that we have to ask ourselves whether much of the output of biomedical science is also getting lost in translation and if it is, why it is, and what we can do about it. Where is the Problem During the past few years, a number of publications have commented on how health might be improved if only we did a better job of applying what we have learned through research. 2,3 The paradigm for the translation of new information from research bench to bedside has been conceptualized by some as a highway, a translational highway. 4 A recent article identified some of its roadblocks and detours and offered challenging solutions. 5 However, that analysis and most of the others that have attempted to address the issue have focused only on the broad, high-speed part of the highway that is, the part that is concerned with taking the findings of basic research and translating them into clinical investigations, and not on the last and perhaps most important segment, the segment that is concerned with taking the findings of clinical investigations and translating them into the practice of medicine at the community level. In reality, most medical care is delivered in local health systems, including the private and group offices of general practitioners or specialists. This is where the highway reaches its end and divides into a number of smaller avenues and lanes, and it is also where vehicles and concepts may get lost. In our efforts to take a systematic and careful look at how we translate research results into clinical medicine and public health, we must consider the environment or neighborhood in which healing and disease prevention take place. In this context, there are effectively several levels of activity at which translation occurs, including the behavior of physicians and that of patients. Is translation at these levels successful The following examples seem to cast some doubt. The Physician and Translation Let us first consider the extent to which physicians behavior reflects research findings. In 1981, the results of the -Blocker Heart Attack Trial were reported by way of a clinical alert published in the Journal of the American Medical Association . 6 Actually, this announcement was made six months before the originally scheduled end of the trial, because by that time the findings had already conclusively established the benefits of beta-blockers for patients who were recovering from myocardial infarction. Subsequently, other reports corroborated the findings unequivocally. Yet in 1996, 15 years after the results of the -Blocker Heart Attack Trial had been made known, beta-blockers were being prescribed for only 62.5 percent of patients who had had a myocardial infarction. 7 Likewise, since the late 1980s, numerous studies have demonstrated the benefits of cholesterol lowering in patients with coronary heart disease. However, data from a national sample show that only half to three quarters of patients who have had a myocardial infarction are even being screened for elevated serum cholesterol levels, much less being prescribed cholesterol-lowering regimens. 8 A third example is the use of aspirin as a cardiovascular drug. Research has shown that aspirin is highly effective when given as short-term therapy for acute myocardial infarction, as therapy for unstable angina, and as long-term, secondary preventive therapy in a wide range of patients with established cardiovascular disease. 9 Nonetheless, according to data from two independent representative samples of visits to office-based physicians in the United States, it appears that as late as 2000, aspirin was being prescribed for at most one third of patients with coronary artery disease for whom there were no contraindications to its use. 10 These three examples of relatively simple, and certainly not prohibitively expensive, practices show that we have a problem in getting providers to apply knowledge gained through research. In this case, it is truly an issue that develops at the end of the highway, not at the level where research outcomes are produced. Admittedly, it is hard to know and evaluate the reasons for this situation. Let us move on to an example of a more complex treatment: percutaneous transluminal coronary angioplasty, which is of interest because it demonstrates both the underuse and possible overuse of a therapeutic strategy. It is a more difficult situation to evaluate because the procedure is performed by specialists, albeit not all of them in academic centers or tertiary hospitals. Research results have consistently shown marked benefits of early intervention to achieve reperfusion in patients with acute myocardial infarction. With the use of these research data, treatment guidelines were developed and promulgated jointly by the American College of Cardiology and the American Heart Association in 1996 11 and were reissued in revised form in 1999. 12 The guidelines advocate rapid treatment with percutaneous transluminal coronary angioplasty or fibrinolysis, with the former being preferentially recommended. Yet recent data from the National Registry of Myocardial Infarction show that in the last quarter of 2002, nearly one third of all patients nationwide who presented with an acute myocardial infarction and who were eligible for reperfusion received neither of these therapies. 13 By itself, this failure to take advantage of a beneficial treatment is unsettling, but it is even more disturbing when one notes that the current rate of undertreatment is actually identical to the rate in 1994. 14 These observations suggest that despite widespread agreement that early reperfusion is imperative and despite the dissemination of guidelines for its use, patients were no better off in 2002 than they had been eight years earlier. On the flip side of the coin, a recent review of Medicare records in five geographically distributed states found that percutaneous transluminal coronary angioplasty was performed inappropriately, on average, in 14 percent of patients (range, 4 to 24 percent). 15 Similarly, a study of coronary-artery bypass grafting in four states showed inappropriate use in 10 to 14 percent of patients. 15 Quite a lot, it seems, is being lost in translation. Patients Behavior From the viewpoint of public health, whatever conclusions one can draw from the examples cited above must be balanced by an understanding of the extent to which patients assume (or fail to assume) their share of responsibility for their health. In fact, in a wide range of situations, patients themselves are the indispensable players. At one extreme is the case of adherence to a prescribed medication regimen. Returning to aspirin as an example, let us consider the findings of a study on its use as reported by patients who had undergone coronary angiography at a major medical center, had documented coronary artery disease, and had been routinely followed up by means of a mailed questionnaire. 16 Even in this population of presumably highly motivated patients with access to state-of-the-art medical care, reported daily aspirin use was only about 60 percent in 1995 and 80 percent in 1999. And this is a therapeutic regimen that is easy, painless, and cheap. An analogous situation, but one with a major difference, is that of antihypertensive medications: these medications are used to treat a condition that may not yet have caused any illness, rather than to prevent the recurrence of a life-threatening event. During the past two or three decades, new classes of antihypertensive drugs have been developed, and several specific brands in each class are now on the market. As a group, antihypertensive medications are among the most frequently taken prescription drugs in the United States today. 17 Yet the rates of blood-pressure control in the general population of patients with hypertension are shockingly poor, as reported by successive National Health and Nutrition Examination Surveys. Is the reason for this that doctors are not prescribing the appropriate antihypertensive medications or that patients are not taking them In truth, it is probably both, but lack of patient compliance appears to be a substantial part of the problem. A 1996 study reported that 47 percent of patients failed to take their antihypertensive medications as prescribed. 18 A 1998 study of a cohort of 21,723 patients showed that between 29 and 56 percent of them, depending on the class of drug used, discontinued their antihypertensive therapy at 12 months and that only 6 to 9 percent switched to another class of drug. 19 Intervention by Physicians to Keep Patients on the Road It is tempting to throw up our hands in despair when patients fail to follow doctors orders heaven, as they say, helps those who help themselves but health care providers can certainly do much to correct this situation if they work at it. Indeed, although it admittedly reflects a special circumstance, one study showed that with very careful monitoring and appropriate adjustment of the treatment regimen, physicians practicing in communities, not in academic centers, were able to increase the rate of blood-pressure control in a cohort of more than 42,000 patients from 27 percent to 66 percent (Figure 2 Figure 2 Control of High Blood Pressure in the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ). 20 If there is a conclusion to be drawn from this observation regarding hypertension treatment, it is that applying known precedents or just using common good practice can make an enormous difference in patient compliance. Admittedly, the challenges associated with modifying patients behavior are enormous and extend far beyond persuading them to take their pills. But difficulties do not constitute a dispensation. As physicians, we still have to try. Consider obesity, for example a modern epidemic that threatens to undo decades of progress against coronary heart disease. Extensive public health research has established some very straightforward measures to address this problem, and these research outcomes have been widely publicized for more than a decade. Yet, astonishingly enough, in 1999 one study reported that only 42 percent of 12,835 obese adults had been advised by their physician to lose weight. 21 Similarly, the value of regular exercise has been the subject of considerable research. The health benefits of regular exercise for most people have been documented, as have been the debilitating effects of being sedentary. Yet a study of 9299 people, also published in 1999, reported that only 34 percent had been counseled about exercise during a regular visit to their physician. 22 Closing the Loop What all the aforementioned examples have in common should be clear. The practices no longer require research to demonstrate efficacy and effectiveness as a practical matter, everything that needs to be known is already known. Furthermore, translation of these research findings into actions that can be used in practice is very simple. In addition, because the actions are not prohibitively expensive, cost is not an obstacle, and in fact, cost effectiveness could be advanced as one more reason for their widespread adoption. Yet their application in the real world is not what it should be, and we need to find out why and to try new approaches to change this situation. In fact, there is evidence that substantial improvements can be achieved simply by increasing the level of accountability of medical practice. For example, the National Committee for Quality Assurance (NCQA) provides a model of action with its Health Plan Employer Data and Information Set (HEDIS) measures. These tools are currently used by the majority of U.S. health plans to assess the quality of care and service. Of course, the value of these tools depends largely on the quality and acceptability of the benchmarks that are chosen to measure performance. NCQA benchmarks are based on widely accepted research findings that are analyzed and presented as recommendations or guidelines by relevant professional organizations such as the American College of Cardiology and the American Heart Association or by other authoritative sources, such as the various components of the NIH. In the latter case, the institutes themselves do not produce the recommendations, but they sponsor evidence-based consensus-development activities involving the participation of many interested professional groups. During the past few years, significant improvements in the day-to-day use of research outcomes have been reported by the NCQA and its members. 23 For example, the rate of beta-blocker treatment among patients who had had a myocardial infarction rose from 62.5 percent in 1996 to 92.5 percent in 2001 (Figure 3 Figure 3 Beta-Blocker Treatment after Myocardial Infarction in Managed-Care Plans. ). Likewise, the rate of appropriate blood-pressure control among patients with hypertension increased from 39.0 percent in 1999 to 55.4 percent in 2001 still not good, but better. Among patients with asthma, the use of appropriate medications increased from 57.7 percent in 1999 to 65.6 percent in 2001. It seems quite likely that the public availability of information on the performance of health plans has been a major contributor to the success of HEDIS measures. However, it is also likely that their success reflects the commitment of relevant professional societies to work together to reach consensus on the guidelines that underlie the measures. Thanks to their efforts, practitioners are less frequently faced with the need to select from or to reconcile potentially conflicting recommendations. This is not to overlook or undermine the role of independent judgment recommendations are, after all, recommendations, even though they are often misnamed guidelines. The art of medicine will always be central to its responsible practice. I believe, however, that professional organizations must also assume a greater role, if not the leading role, in our collective efforts to realize the full public health benefits of research by minimizing what gets lost in translation. Developing and publishing practice recommendations is an extremely valuable first step. As important, or more important, is ensuring that these recommendations actually influence the way medicine is practiced. Fortunately, there are encouraging signs that professional societies are moving beyond mere promulgation of practice guidelines toward direct involvement in efforts to see that they are implemented. For example, the American Heart Association and the American Stroke Association recently announced that they have joined with the NCQA to recognize physician excellence in ambulatory cardiovascular and stroke care. The voluntary program, which is designed to assess physicians performance on the basis of accepted clinical guidelines, is modeled on a successful collaborative effort initiated in 1997 by the NCQA and the American Diabetes Association, the Diabetes Physician Recognition Program. This program allows individual physicians and medical groups to submit performance data through the Internet for comparison with threshold values on a set of selected HEDIS measures that were carefully defined and tested for their relation to improved care for people with diabetes. Physicians and groups found to meet or exceed the threshold values are recognized on a dedicated page of the American Diabetes Association Web site, and callers to the associations toll-free number are referred to the list of recognized physicians. Although there is no way to distinguish the effect of the Diabetes Physician Recognition Program from that of the overall HEDIS effort, clear improvements in diabetes care as reported by the NCQA were seen between 1999 and 2001. During those three years, the percentage of patients with diabetes in whom glycosylated hemoglobin was measured increased from 75.0 percent to 81.4 percent the percentage in whom glycosylated hemoglobin was poorly controlled decreased from 44.9 percent to 36.9 percent those in whom the complete lipid profile was determined increased from 69.0 percent to 81.4 percent those in whom acceptable lipid control was achieved increased from 44.3 percent to 50.9 percent those who underwent nephropathy screening increased from 36.0 percent to 46.3 percent and those who underwent comprehensive eye examinations increased from 45.4 percent to 52.0 percent. These increases are all reflections of progress. Of course, the focus on translation at the level of medical practice must not come at the expense of improving the entire translational highway. As has been widely recognized, we are today on the threshold of a new era in which gene-centered medicine will almost certainly be the star player. Although several basic-science pursuits such as immunology, cell biology, and molecular biology have been highly productive, genomic research clearly dominates current interests and expectations. From it, a new type of medicine is being born: genetic medicine. The expectations are many, and already studies are reporting personalized prevention and treatment of specific conditions on the basis of individual gene variations. Moreover, specific polymorphisms have been found that may help to predict how fast some conditions will progress. 24-27 However, the excitement is warranted only if these discoveries are used in clinical medicine. Many scientific and academic leaders deplore our inertia (or is it our imperfect vision) in moving from basic to clinical research in this area, and they despair of our readiness to bridge this gap. Not all, however, agree about the steps that should be taken to remedy the situation. This debate is cogently described and analyzed in a 2002 issue of Science focused on The Puzzle of Complex Diseases. Its main message is that basic science, clinical discovery, and patient-oriented research are interdependent and not necessarily successive steps. 28,29 Using the translational-highway metaphor, we need to widen the interstate and increase the number of its lanes. Enthusiasm for gene-centered medicine is contagious, and I am certainly not immune to it. In my view, however, the fundamental issue remains the same. Enormous amounts of new knowledge are barreling down the information highway, but they are not arriving at the doorsteps of our patients. Given that so much is lost at the real-world level, as has been demonstrated time and time again with regard to relatively simple research outcomes, I believe that our approach to all patient-oriented research should be reconsidered. Often, investigators appear to design and implement clinical trials and demonstration-research projects without first asking what should always be the threshold question namely, Are the results likely to be applied in a clinical setting Answering this question means considering not only the cost of a proposed intervention but also the degree of risk to patients that is entailed in implementing it. This issue of who will really benefit from research results is especially critical as we look toward applications of genomic research. And we must direct our attention, our commitment, our energies, our creativity, and our resourcefulness to making sure that the beneficiaries will be the patients all the patients. Lets be realistic: If we didnt do it with aspirin, how can we expect to do it with DNA We must keep our focus on the public health, as have all good medical researchers through the ages. Thomas King Chambers expressed this goal in the Goulstonian Lecture in 1850: To us, knowledge, how good and lovely soever it be for its own sake, must always be a by-end, a step merely towards the still better and lovelier goal of good-will towards men. Our object, then, in reviewing these researchers, and in adding to them such observations as our own sphere of action supplies, should be to deduce from them rules of practice, to gather from the tree of knowledge fruit for the solace and refreshment of mankind. Presented as the 113th Shattuck Lecture at the Annual Meeting of the Massachusetts Medical Society, Boston, May 3, 2003. The views expressed in this article are solely those of the author and do not necessarily represent the opinions of the National Institutes of Health or the Department of Health and Human Services. I am indebted to Ms. Barbara Liu and Dr. Carl Roth for their invaluable assistance in the preparation of the lecture and manuscript. 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