Combination of EuroSCORE and Cardiac Troponin I Improves the Prediction of Adverse Outcome after Cardiac Surgery
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BACKGROUND: Reclassification tables have never been used to compare concentrations of cardiac troponin I (cTnI) with predictive models of risk in the perioperative setting. The current study aimed to evaluate the prognostic value of pre- and/or postoperative serum cTnI when combined with The European System for Cardiac Operative Risk Evaluation (EuroSCORE) in predicting adverse outcome after cardiac surgery. METHODS: Nine hundred five consecutive patients were included. Standard EuroSCORE as well as preoperative and 24-h postoperative cTnI were measured in all patients. Major adverse cardiac events and in-hospital mortality were chosen as study endpoints. The performance of EuroSCORE with and without pre- and/or postoperative cTnI were assessed by means of receiver operating characteristic curves, net reclassification index, and integrated discrimination improvement analyses. Data are expressed as ±SD. RESULTS: Death occurred in 28 of 905 (3%) patients and major adverse cardiac events in 202/905 (22%) patients. Models including EuroSCORE alone were characterized by a low discriminative power (c-index = 0.60 ± 0.05) in predicting major adverse cardiac events. The c-index increased to 0.61 ± 0.05 (P = 0.46), 0.70 ± 0.04 (P < 0.001), and 0.71 ± 0.04 (P < 0.001) when preoperative, postoperative, and pre/postoperative cTnI were included, respectively. The better predictive ability was confirmed by net reclassification index (0.41 ± 0.08, P < 0.001; 0.67 ± 0.08, P < 0.001; and 0.68 ± 0.08, P < 0.001, respectively) and integrated discrimination improvement (0.003 ± 0.002, P = 0.12; 0.099 ± 0.015, P < 0.001; and 0.094 ± 0.016, P < 0.001, respectively). Similar results were observed for in-hospital mortality. CONCLUSIONS: The combination of EuroSCORE and postoperative cTnI provides the best discriminative power and performance in predicting adverse outcome after cardiac surgery and is suggested as being an effective model that improves early identification of high-risk patients.
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