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      Two-Hour Post-Load Plasma Glucose, a Biomarker to Improve the GRACE Score in Patients without Known Diabetes


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          Objective: To assess improvement in predictive performance of Global Registry of Acute Coronary Events risk score (GRS) by addition of a glucose matrix. Methods: 1,056 acute coronary syndrome (ACS) survivors without known diabetes had pre-discharge fasting (FPG) and 2-h post-load plasma glucose (2h-PG) measured. GRS was calculated. Major adverse cardiac events (MACE; death and non-fatal myocardial infarction) were recorded during follow-up. Cox proportional hazard regression predicted event-free survival. Likelihood ratio test, Akaike’s information criteria, continuous net reclassification index (NRI<sup>></sup><sup>0</sup>), and integrated discrimination improvement (IDI) were used to test the additional prognostic value of glycaemic indices over GRS. Results: During a median follow-up of 36.5 months, 211 MACEs (20.0%), 96 deaths (9.1%), and 115 non-fatal re-infarctions (10.9%), occurred. 2h-PG, but not FPG, independently predicted MACE-free survival at all time points (HR 1.08, 95% CI 1.03–1.13, p = 0.002, at 3 years). Risk of MACE increased by 8–11% with every 1 mmol/L rise in 2h-PG. 2h-PG significantly improved the prognostic models containing GRS. Models containing GRS and 2h-PG yielded lowest corrected Akaike’s information criteria compared to that with only GRS. 2h-PG, but not FPG, improved NRI<sup>></sup><sup>0</sup> (NRI<sup>></sup><sup>0</sup> 0.169, p = 0.028 at 3 years) and IDI (IDI of 0.66%, p = 0.018 at 3 years) significantly at all time points during the follow-up. Conclusions: 2h-PG, but not FPG, improves performance of GRS-containing models in predicting post-ACS prognosis in the short to medium term.

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          Most cited references 32

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          Predictors of hospital mortality in the global registry of acute coronary events.

          Management of acute coronary syndromes (ACS) should be guided by an estimate of patient risk. To develop a simple model to assess the risk for in-hospital mortality for the entire spectrum of ACS treated in general clinical practice. A multivariable logistic regression model was developed using 11 389 patients (including 509 in-hospital deaths) with ACS with and without ST-segment elevation enrolled in the Global Registry of Acute Coronary Events (GRACE) from April 1, 1999, through March 31, 2001. Validation data sets included a subsequent cohort of 3972 patients enrolled in GRACE and 12 142 in the Global Use of Strategies to Open Occluded Coronary Arteries IIb (GUSTO-IIb) trial. The following 8 independent risk factors accounted for 89.9% of the prognostic information: age (odds ratio [OR], 1.7 per 10 years), Killip class (OR, 2.0 per class), systolic blood pressure (OR, 1.4 per 20-mm Hg decrease), ST-segment deviation (OR, 2.4), cardiac arrest during presentation (OR, 4.3), serum creatinine level (OR, 1.2 per 1-mg/dL [88.4- micro mol/L] increase), positive initial cardiac enzyme findings (OR, 1.6), and heart rate (OR, 1.3 per 30-beat/min increase). The discrimination ability of the simplified model was excellent with c statistics of 0.83 in the derived database, 0.84 in the confirmation GRACE data set, and 0.79 in the GUSTO-IIb database. Across the entire spectrum of ACS and in general clinical practice, this model provides excellent ability to assess the risk for death and can be used as a simple nomogram to estimate risk in individual patients.
            • Record: found
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            • Article: not found

            Universal definition of myocardial infarction.

             S Alpert,  Brian White,   (2007)
              • Record: found
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              A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry.

              Accurate estimation of risk for untoward outcomes after patients have been hospitalized for an acute coronary syndrome (ACS) may help clinicians guide the type and intensity of therapy. To develop a simple decision tool for bedside risk estimation of 6-month mortality in patients surviving admission for an ACS. A multinational registry, involving 94 hospitals in 14 countries, that used data from the Global Registry of Acute Coronary Events (GRACE) to develop and validate a multivariable stepwise regression model for death during 6 months postdischarge. From 17,142 patients presenting with an ACS from April 1, 1999, to March 31, 2002, and discharged alive, 15,007 (87.5%) had complete 6-month follow-up and represented the development cohort for a model that was subsequently tested on a validation cohort of 7638 patients admitted from April 1, 2002, to December 31, 2003. All-cause mortality during 6 months postdischarge after admission for an ACS. The 6-month mortality rates were similar in the development (n = 717; 4.8%) and validation cohorts (n = 331; 4.7%). The risk-prediction tool for all forms of ACS identified 9 variables predictive of 6-month mortality: older age, history of myocardial infarction, history of heart failure, increased pulse rate at presentation, lower systolic blood pressure at presentation, elevated initial serum creatinine level, elevated initial serum cardiac biomarker levels, ST-segment depression on presenting electrocardiogram, and not having a percutaneous coronary intervention performed in hospital. The c statistics for the development and validation cohorts were 0.81 and 0.75, respectively. The GRACE 6-month postdischarge prediction model is a simple, robust tool for predicting mortality in patients with ACS. Clinicians may find it simple to use and applicable to clinical practice.

                Author and article information

                S. Karger AG
                September 2020
                12 August 2020
                : 145
                : 9
                : 553-561
                aDepartment of Cardiology, Milton Keynes University Hospital, Milton Keynes, United Kingdom
                bDepartment of Cardiology, Scunthorpe General Hospital, Scunthorpe, United Kingdom
                cDepartment of Cardiology, Castle Hill Hospital, Kingston upon Hull, United Kingdom
                dDepartment of Academic Endocrinology, Diabetes, and Metabolism, Hull York Medical School, University of Hull, Kingston upon Hull, United Kingdom
                Author notes
                *Sudipta Chattopadhyay, Department of Cardiology, Milton Keynes University Hospital, Standing Way, Milton Keynes MK6 5LD (UK), Sudipta.Chattopadhyay@nhs.net
                509180 Cardiology 2020;145:553–561
                © 2020 S. Karger AG, Basel

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                Page count
                Tables: 4, Pages: 9
                CAD and AMI: Research Article


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