Evaluation of the NICE mini-GRACE risk scores for acute myocardial infarction using the Myocardial Ischaemia National Audit Project (MINAP) 2003–2009: National Institute for Cardiovascular Outcomes Research (NICOR). Issue 1 (22nd September 2012) - Simms, Alexander D; Reynolds, Stephanie; Pieper, Karen; Baxter, Paul D; Cattle, Brian A; Batin, Phillip D; Wilson, John I; Deanfield, John E; West, Robert M; Fox, Keith A A; Hall, Alistair S; Gale, Christopher P
Abstract : Objective: To evaluate the performance of the National Institute for Health and Clinical Excellence (NICE) mini-Global Registry of Acute Coronary Events (GRACE) (MG) and adjusted mini-GRACE (AMG) risk scores. Design: Retrospective observational study. Setting: 215 acute hospitals in England and Wales. Patients: 137 084 patients discharged from hospital with a diagnosis of acute myocardial infarction (AMI) between 2003 and 2009, as recorded in the Myocardial Ischaemia National Audit Project (MINAP). Main outcome measures: Model performance indices of calibration accuracy, discriminative and explanatory performance, including net reclassification index (NRI) and integrated discrimination improvement. Results: Of 495 263 index patients hospitalised with AMI, there were 53 196 ST elevation myocardial infarction and 83 888 non-ST elevation myocardial infarction (NSTEMI) (27.7%) cases with complete data for all AMG variables. For AMI, AMG calibration was better than MG calibration (Hosmer–Lemeshow goodness of fit test: p=0.33 vs p<0.05). MG and AMG predictive accuracy and discriminative ability were good (Brier score: 0.10 vs 0.09; C statistic: 0.82 and 0.84, respectively). The NRI of AMG over MG was 8.1% (p<0.05). Model performance was reduced in patients with NSTEMI, chronic heart failure, chronic renal failure and in patients aged ≥85 years. Conclusions: The AMG and MG risk scores, utilised by NICE, demonstrated good performance across a range of indices using MINAP data, but performed less well in higher risk subgroups. Although indices were better for AMG, its application may be constrained by missing predictors.