Atrial Fibrillation and Stroke Risk After Coronary Artery Bypass Grafting Surgery. [artículo]
Por: Pérez de la Sota, Enrique [Cirugía Cardiovascular]
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Colaborador(es): Servicio de Cirugía Cardiovascular.
Editor: Journal of atrial fibrillation, 2013Descripción: 6(3):909.Recursos en línea: Acceso libre Resumen: OBJECTIVES: To develop a multivariate predictive risk score of perioperative in-hospital stroke after coronary artery bypass grafting (CABG) surgery. METHOD: A total of 26 347 patients were enrolled from 21 Spanish hospital databases. Logistic regression analysis was used to predict the risk of perioperative stroke (ictus or transient ischaemic attack). The predictive scale was developed from a training set of data and validated by an independent test set, both selected randomly. The assessment of the accuracy of prediction was related to the area under the ROC curve. The variables considered were: preoperative (age, gender, diabetes mellitus, arterial hypertension, previous stroke, cardiac failure and/or left ventricular ejection fraction <40%, non-elective priority of surgery, extracardiac arteriopathy, chronic kidney failure and/or creatininemia >= 2 mg/dl and atrial fibrillation) and intraoperative (on/off-pump). RESULTS: Global perioperative stroke incidence was 1.38%. Non-elective priority of surgery (priority; OR = 2.32), vascular disease (arteriopathy; OR = 1.37), cardiac failure (cardiac; OR = 3.64) and chronic kidney failure (kidney; OR = 6.78) were found to be independent risk factors for perioperative stroke in uni- and multivariate models in the training set of data; P < 0.0001; AUC = 0.77, 95% CI 0.73-0.82. The PACK(2) stroke CABG score was established with 1 point for each item, except for chronic kidney failure with 2 points (range 0-5 points); AUC = 0.76, 95% CI 0.72-0.80. In patients with PACK(2) score = 2 points, off-pump reduced perioperative stoke incidence by 2.3% when compared with on-pump CABG. CONCLUSIONS: PACK(2) risk scale shows good predictive accuracy in the data analysed and could be useful in clinical practice for decision making and patient selection.
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Artículo
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Formato Vancouver:
Hornero F, Martín E, Mena-Durán AV, Rodríguez R, Castellà M, Porras C et al. Atrial Fibrillation and Stroke Risk After Coronary Artery Bypass Grafting Surgery. J Atr Fibrillation. 2013 Oct 31;6(3):909.
PMID: 28496894
Contiene 16 referencias
OBJECTIVES: To develop a multivariate predictive risk score of perioperative in-hospital stroke after coronary artery bypass grafting (CABG) surgery. METHOD: A total of 26 347 patients were enrolled from 21 Spanish hospital databases. Logistic regression analysis was used to predict the risk of perioperative stroke (ictus or transient ischaemic attack). The predictive scale was developed from a training set of data and validated by an independent test set, both selected randomly. The assessment of the accuracy of prediction was related to the area under the ROC curve. The variables considered were: preoperative (age, gender, diabetes mellitus, arterial hypertension, previous stroke, cardiac failure and/or left ventricular ejection fraction <40%, non-elective priority of surgery, extracardiac arteriopathy, chronic kidney failure and/or creatininemia >= 2 mg/dl and atrial fibrillation) and intraoperative (on/off-pump). RESULTS: Global perioperative stroke incidence was 1.38%. Non-elective priority of surgery (priority; OR = 2.32), vascular disease (arteriopathy; OR = 1.37), cardiac failure (cardiac; OR = 3.64) and chronic kidney failure (kidney; OR = 6.78) were found to be independent risk factors for perioperative stroke in uni- and multivariate models in the training set of data; P < 0.0001; AUC = 0.77, 95% CI 0.73-0.82. The PACK(2) stroke CABG score was established with 1 point for each item, except for chronic kidney failure with 2 points (range 0-5 points); AUC = 0.76, 95% CI 0.72-0.80. In patients with PACK(2) score = 2 points, off-pump reduced perioperative stoke incidence by 2.3% when compared with on-pump CABG. CONCLUSIONS: PACK(2) risk scale shows good predictive accuracy in the data analysed and could be useful in clinical practice for decision making and patient selection.
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