Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.4/532
Título: Risk-prediction for postoperative major morbidity in coronary surgery
Autor: Antunes, PE
Oliveira, JF
Antunes, MJ
Palavras-chave: Cuidados Pós-operatórios
Procedimentos Cirúrgicos Cardíacos
Factores de Risco
Data: 2009
Editora: Elsevier
Citação: Eur J Cardiothorac Surg. 2009 May;35(5):760-6
Resumo: OBJECTIVE: Analysis of major perioperative morbidity has become an important factor in assessment of quality of patient care. We have conducted a prospective study of a large population of patients undergoing coronary artery bypass surgery (CABG), to identify preoperative risk factors and to develop and validate risk-prediction models for peri- and postoperative morbidity. METHODS: Data on 4567 patients who underwent isolated CABG surgery over a 10-year period were extracted from our clinical database. Five postoperative major morbidity complications (cerebrovascular accident, mediastinitis, acute renal failure, cardiovascular failure and respiratory failure) were analysed. A composite morbidity outcome (presence of two or more major morbidities) was also analysed. For each one of these endpoints a risk model was developed and validated by logistic regression and bootstrap analysis. Discrimination and calibration were assessed using the under the receiver operating characteristic (ROC) curve area and the Hosmer-Lemeshow (H-L) test, respectively. RESULTS: Hospital mortality and major composite morbidity were 1.0% and 9.0%, respectively. Specific major morbidity rates were: cerebrovascular accident (2.5%), mediastinitis (1.2%), acute renal failure (5.6%), cardiovascular failure (5.6%) and respiratory failure (0.9%). The risk models developed have acceptable discriminatory power (under the ROC curve area for cerebrovascular accident [0.715], mediastinitis [0.696], acute renal failure [0.778], cardiovascular failure [0.710], respiratory failure [0.787] and composite morbidity [0.701]). The results of the H-L test showed that these models predict accurately, both on average and across the ranges of patient deciles of risk. CONCLUSIONS: We developed a set of risk-prediction models that can be used as an instrument to provide information to clinicians and patients about the risk of postoperative major morbidity in our patient population undergoing isolated CABG.
URI: http://hdl.handle.net/10400.4/532
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