ES2525029T3 - Predicción del riesgo de sufrir episodios cardíacos adversos graves - Google Patents

Predicción del riesgo de sufrir episodios cardíacos adversos graves Download PDF

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ES2525029T3
ES2525029T3 ES13179055.2T ES13179055T ES2525029T3 ES 2525029 T3 ES2525029 T3 ES 2525029T3 ES 13179055 T ES13179055 T ES 13179055T ES 2525029 T3 ES2525029 T3 ES 2525029T3
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James V. Snider
Eugene R. Heyman
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Critical Care Diagnostics Inc
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Abstract

Un método para seleccionar un tratamiento para un sujeto, método que comprende: la determinación de una puntuación de riesgo de sufrir un episodio cardíaco adverso grave (MACERS) para un sujeto basándose, al menos en parte, en la relación de un segundo nivel de gen soluble 2 expresado por estimulación del crecimiento (ST2) en el sujeto en un segundo momento (ST2 T1) con un primer nivel de ST2 soluble en el sujeto en un primer momento (ST2 T0), junto con un logaritmo natural ponderado de un nivel de la prohormona N-terminal del péptido natriurético de tipo cerebral (NT-proBNP) en el sujeto en un segundo momento (NP T1) de acuerdo con la siguiente fórmula: MACERS >= (ST2 T1/ST2 T0 + αln(NP T1), donde α es un factor de ponderación; la comparación de la MACERS con una MACERS de referencia; y la elección de: (a) hospitalización inicial, tratamiento continuo sobre pacientes hospitalizados o cateterismo cardíaco para un sujeto con una MACERS elevada en comparación con la MACERS de referencia, (2) un tratamiento alternativo para un sujeto que recibe un tratamiento y que presenta una MACERS elevada en comparación con la MACERS de referencia, o (3) tratamiento continuo o interrupción del tratamiento, tal como con el alta del hospital, para un sujeto que recibe un tratamiento y que presenta una MACERS disminuida en comparación con la MACERS de referencia.

Description

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E13179055
26-11-2014
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[0074] Para definir mejor la utilidad funcional de la relación ST2 combinado con un valor NT-proBNP se desarrolló una fórmula:
[0075] Esta fórmula se desarrolló evaluando el resultado como una función ABC ROC para un intervalo de coeficientes asociados con el término NT-proBNP. El resultado de esta serie de cálculos se muestra en la Figura
1.
[0076] El valor ABC máximo se consiguió con un coeficiente para a de 0,33 resultando en que la ecuación final es:
[0077] Al utilizar este algoritmo en una serie de cálculos comparando la sensibilidad, especificidad y riesgo relativo (eje lateral derecho) obtenemos el gráfico de la Figura 2.
[0078] En este gráfico el valor de puntuación que resulta en el máximo valor de riesgo relativo es 3,2. El análisis ROC de estos datos confirma que el valor de umbral óptimo es 3,3, ilustrado en la Figura 3. Además cabe destacar que el valor ABC utilizando esta puntuación es de 0,80 según se compara con el 0,77 para la relación ST2 y 0,72 para la relación NT-proBNP, que generó los siguientes valores ABC más altos.
[0079] Cuando se utiliza esta puntuación, en el valor de umbral de 3,2, para estratificar a los pacientes en esta cohorte que están en riesgo de padecer un episodio: un ingreso, un trasplante o la mortalidad, se consigue una clara distinción entre los pacientes con riesgo bajo y los pacientes con riesgo alto. Estos resultados se ilustran en la Tabla 4.
Tabla 4: Resumen de la estratificación de pacientes por el riesgo de sufrir episodios adversos en 1 año utilizando un punto de corte de la puntuación de 3,2.
Puntuación mediana
media
<3,2 >3,2 3,55 3,71
N 17 31
Episodio N 3 23
% Episodio 17,6 %
74,2 %
PPV
74,2 % NPV 82,4 %
RR 4,2
[0080] Al comparar directamente estos resultados con los resultados que utilizan sólo la relación ST2, mostrados en la Tabla 2, se ilustra que combinando la relación ST2 con un valor NT-proBNP todos los parámetros relevantes que representan la evaluación de predicción del riesgo son más fuertes; PPV, NPV y RR.
[0081] Para comparar los resultados de estratificación para el siguiente valor más fuerte, la relación NT-proBNP se resume en la Tabla 5. Los valores que utilizan la relación NT-proBNP son mucho más bajos que cuando se utiliza la relación ST2 o desde la fórmula que combina ST2 con NT-proBNP
Tabla 5: Resumen de la estratificación de pacientes por el riesgo de sufrir episodios adversos en 1 año utilizando la relación NT-proBNP
Relación NT-proBNP mediana media
<0,75 >0,75 0,74 0,83
N
24 24
Episodio N
10 16
% Episodio
41,7 % 66,7 %
PPV
66,7 %
NPV
58,3 %
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E13179055
26-11-2014
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[0103] El valor óptimo ROC a partir de este análisis fue 3,52. Como observamos utilizando los datos de la cohorte de péptidos (véase el Ejemplo I), la ROC de la fórmula de puntuación de riesgo de MACE óptima también fue 3,5 pero la mejor precisión de pronóstico (mortalidad) se alcanzó con un valor 3,2 en esa cohorte. Un diagrama de caja y bigotes básico (Figura 19) muestra una resolución clara entre el grupo de supervivientes y de
20 fallecidos, p<0,0001. Para comparar, un análisis de gráfico de caja y bigotes de ST2 R L:F es similar, con un p=0,0001 (Figura 20). También se observó utilizando los datos de la cohorte de péptidos que un análisis de matriz básico y un cálculo de riesgo relativo confirman que la puntuación de riesgo de MACE proporciona la predicción de mortalidad más precisa.
Tabla 16: Análisis de matriz y riesgo relativo de las variables de predicción de mortalidad más fuertes
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ST2 R L:F
NTproBNP R L:F Puntuación de riesgo MACE
<0,85
≥0,85 <0,7 ≥0,7 <3,5 ≥3,5
N
76 31 49 58 65 42
N mortalidad
3 10 2 11 1 12
% mortalidad
3,9% 32,3% 4,1% 19,0% 1,5% 28,6%
PPV
32,3% 19,0% 28,6%
NPV
96,1 % 95,9% 98,5%
RR
8,2 4,6 18,6
[0104] Pese a que tanto la relación ST2 como la relación NTproBNP produjeron buenos valores de riesgo relativo, el riesgo relativo utilizando la puntuación de riesgo de MACE fue mucho mayor.
35 Conclusión
[0105] Según se determinó utilizando los datos de la cohorte de péptidos (Ejemplo 1) la fórmula de puntuación de riesgo de MACE descrita en el presente documento proporciona la mayor precisión de pronóstico, específicamente cuando el parámetro resultante es la mortalidad, según se determina por la ROC, la relación de riesgo y el cálculo de riesgo relativo. Existe una pequeña pero significativa diferencia entre los valores de umbral
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Claims (1)

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