Letter to the Editor

doi:10.1093/eurheartj/ehm231 The consensus is clearly needed for thedefinitionof stress hyperglycaemia in acute myocardial infarction We enjoyed reading the article of Bauters et al.1 about new relation of stress hypergly-caemia with left ventricular remodelling. Stress hyperglycaemia has been proved to be the independent prognostic marker of worse outcome in numerous critical ill-nesses,2 including acute myocardial infarc-tion (AMI).3,4 Interestingly, the admission hyperglycaemia has been studied very differ-ently in AMI even in last year or two: (i) as a blood glucose level,10 vs. 10 mmol/L (180 mg/dL)5; (ii) in comparison between tertiles of glycemia6; (iii) by comparing groups with the admis-sion glucose of,7.8 vs. 7.8 mmol/L and,11.1 vs. 11.1 mmol/L7; (iv) or groups with the admission glucose of,7.8 vs. 7.8–11.0 mmol/L vs. 11.1 mmol/L8; (v) or with/without first fasting glucose level after admission of.7.7 mmol/L9; (vi) or sextiles of glycaemia.10 Thus, AMI has been an important disease, glycaemia being the vital parameter, stress hyperglycaemia has prognostic value, and consensus is clearly needed for the defi-nition of stress hyperglycaemia. For studying pathophysiological processes, it may not be so useful only to choose a gly-caemic value above which ‘stress hypergly-caemia begins’, if it depends on diabetes mellitus definition, because the definition of diabetes mellitus has been changed and may be expected to change. It may be more rational for scientific purposes to analyse tertiles–sextiles (depending on the number of patients studied). On the contrary, using, for example, quar-tiles may not be fully applicable for prac-titioner and another method probably should be employed in addition: authors may determine the best cut-off value of glycaemia for mortality in their own AMI patients. Using meta-analysis, this may allow the cardiological community to find a concentration of glucose that is good to introduce in routine risk stratification in AMI patients all over the world, as well as more precise value for risk stratification according to age, gender, race, and so on. We propose that authors should analyse their database in two ways: both by using quartiles and best cut-off value of glycaemia for mortality in AMI patients