RESUMEN
OBJECTIVE: Primary percutaneous coronary intervention (P-PCI) has demonstrated its efficacy in patients with ST segment elevation myocardial infarction (STEMI). However, patients with STEMI ≥75 years receive less P-PCI than younger patients despite their higher in-hospital morbimortality. The objective of this analysis was to determine the effectiveness of P-PCI in patients with STEMI ≥75 years. METHODS: We included 979 patients with STEMI ≥75 years, from the ATención HOspitalaria del Síndrome coronario study, a registry of 8142 consecutive patients with acute coronary syndrome admitted at 31 Spanish hospitals in 2014-2016. We calculated a propensity score (PS) for the indication of P-PCI. Patients that received or not P-PCI were matched by PS. Using logistic regression, we compared the effectiveness of performing P-PCI versus non-performance for the composite primary event, which included death, reinfarction, acute pulmonary oedema or cardiogenic shock during hospitalisation. RESULTS: Of the included patients, 81.5 % received P-PCI. The matching provided two groups of 169 patients with and without P-PCI. Compared with its non-performance, P-PCI presented a composite event OR adjusted by PS of 0.55 (95% CI 0.34 to 0.89). CONCLUSIONS: Receiving a P-PCI was significantly associated with a reduced risk of major intrahospital complications in patients with STEMI aged 75 years or older.
Asunto(s)
Intervención Coronaria Percutánea , Infarto del Miocardio con Elevación del ST/terapia , Factores de Edad , Anciano , Anciano de 80 o más Años , Comorbilidad , Femenino , Humanos , Masculino , Intervención Coronaria Percutánea/efectos adversos , Intervención Coronaria Percutánea/mortalidad , Edema Pulmonar/mortalidad , Edema Pulmonar/prevención & control , Recurrencia , Sistema de Registros , Medición de Riesgo , Factores de Riesgo , Infarto del Miocardio con Elevación del ST/diagnóstico por imagen , Infarto del Miocardio con Elevación del ST/mortalidad , Choque Cardiogénico/mortalidad , Choque Cardiogénico/prevención & control , España , Factores de Tiempo , Resultado del TratamientoRESUMEN
Antecedentes y objetivo: El objetivo del estudio fue comprobar la validez de la clasificación de riesgo KDIGO 2012 para predecir mortalidad total (MT) y cardiovascular (MCV) en diabetes mellitus tipo 2 (DM2). Materiales y métodos: Estudio de cohortes prospectivo incluyendo pacientes con DM2. Los puntos finales clínicos fueron MT y MCV. La principal variable predictora fue la clasificación KDIGO, una variable que recoge 4 niveles de riesgo en dependencia de una combinación de la tasa de filtración glomerular y la excreción de albúmina urinaria. La evaluación del poder predictivo se realizó con el índice de mejora de discriminación integrada (IDI). Resultados: Se incluyeron 453 pacientes (39,3% varones, edad 64,9 [DE 9,3] años y evolución de DM2 de 10,4 [DE 7,5] años). Durante una mediana de 13 años de seguimiento, hubo incremento significativo de la tasa/1000 pacientes-año de MT (26,5 vs. 45,1 vs. 79,2 vs. 109,8; p<0,001) y de MCV (8,1 vs. 17,4 vs. 24,7 vs. 57,5; p<0,001) en las sucesivas categorías de riesgo KDIGO. En análisis multivariante también hubo incremento de riesgo de MT (HR[riesgo moderado]=1,29; HR[riesgo alto]=1,83; HR[riesgo muy alto]=2,15; p=0,016) y MCV (HR[riesgo moderado]=1,73; HR[riesgo alto]=2,27; HR[riesgo muy alto]=4,22; p=0,007) en las sucesivas categorías. La clasificación KDIGO mejoró la predicción de MT (IDI=0,00888; p=0,047) y MCV (IDI=0,01813; p=0,035). Conclusiones: La clasificación de riesgo según guías KDIGO 2012 puede estratificar eficazmente el riesgo de MT y MCV en pacientes con DM2
Background and aims: Our aim was to assess the usefulness of KDIGO 2012 risk classification to predict total and cardiovascular mortality in type 2 diabetes mellitus (DM2). Material and methods: Prospective cohort study that included DM2 patients. Clinical end-points were total and cardiovascular mortality. The main predictive variable was KDIGO risk classification, which is a combination of urinary albumin excretion and glomerular filtration rate. The predictive value was evaluated by the integrated discrimination improvement (IDI) index. Results: 453 patients (39.3% males, aged 64.9 [SD 9.3] and with a mean diabetes duration of 10.4 [SD 7.5] years) were included. During a median follow-up of 13 years, mortality rates per 1000 patients/year (26.5 vs. 45.1 vs. 79,2 vs. 109,8; p<0,001) and cardiovascular mortality (8.1 vs. 17.4 vs. 24.7 vs. 57.5; p<0,001) were progressively increased in successive KDIGO categories. In the multivariate analysis, there was also a progressive increase of mortality risk (HR[moderate risk]=1.29; HR[high risk])=1.83; HR[very high risk]=2.15; p=.016) and cardiovascular mortality risk (HR[moderate risk]=1.73; HR[high risk]=2.27; HR[very high risk]=4.22; p=.007) in the successive categories. KDIGO classification was able to improve the mortality risk prediction (IDI=0.00888; p=.047) and cardiovascular mortality risk prediction (IDI=0.01813; p=.035). Conclusions: KDIGO risk classification can effectively stratify total and cardiovascular mortality risk in DM2 patients
Asunto(s)
Humanos , Masculino , Femenino , Persona de Mediana Edad , Guías de Práctica Clínica como Asunto , Diabetes Mellitus Tipo 2/complicaciones , Tasa de Filtración Glomerular , Albuminuria , Medición de Riesgo , Pronóstico , Estudios de Cohortes , Estudios Prospectivos , Análisis Multivariante , Diabetes Mellitus Tipo 2/mortalidadRESUMEN
BACKGROUND AND AIMS: Our aim was to assess the usefulness of KDIGO 2012 risk classification to predict total and cardiovascular mortality in type 2 diabetes mellitus (DM2). MATERIAL AND METHODS: Prospective cohort study that included DM2 patients. Clinical end-points were total and cardiovascular mortality. The main predictive variable was KDIGO risk classification, which is a combination of urinary albumin excretion and glomerular filtration rate. The predictive value was evaluated by the integrated discrimination improvement (IDI) index. RESULTS: 453 patients (39.3% males, aged 64.9 [SD 9.3] and with a mean diabetes duration of 10.4 [SD 7.5] years) were included. During a median follow-up of 13 years, mortality rates per 1000 patients/year (26.5 vs. 45.1 vs. 79,2 vs. 109,8; p<0,001) and cardiovascular mortality (8.1 vs. 17.4 vs. 24.7 vs. 57.5; p<0,001) were progressively increased in successive KDIGO categories. In the multivariate analysis, there was also a progressive increase of mortality risk (HR[moderate risk]=1.29; HR[high risk])=1.83; HR[very high risk]=2.15; p=.016) and cardiovascular mortality risk (HR[moderate risk]=1.73; HR[high risk]=2.27; HR[very high risk]=4.22; p=.007) in the successive categories. KDIGO classification was able to improve the mortality risk prediction (IDI=0.00888; p=.047) and cardiovascular mortality risk prediction (IDI=0.01813; p=.035). CONCLUSIONS: KDIGO risk classification can effectively stratify total and cardiovascular mortality risk in DM2 patients.
