Admission glucose and risk of early death in non-diabetic patients with ST-segment elevation myocardial infarction: a meta-analysis

Acute Hyperglycemia-Induced Injury in Myocardial Infarction

Acute hyperglycemia is a transient increase in plasma glucose level (PGL) frequently observed in patients with ST-elevation myocardial infarction (STEMI). The aim of this review is to clarify the molecular mechanisms whereby acute hyperglycemia impacts coronary flow and myocardial perfusion in patients with acute myocardial infarction (AMI) and to discuss the consequent clinical and prognostic implications. We conducted a comprehensive literature review on the molecular causes of myocardial damage driven by acute hyperglycemia in the context of AMI. The negative impact of high PGL on admission recognizes a multifactorial etiology involving endothelial function, oxidative stress, production of leukocyte adhesion molecules, platelet aggregation, and activation of the coagulation cascade. The current evidence suggests that all these pathophysiological mechanisms compromise myocardial perfusion as a whole and not only in the culprit coronary artery. Acute hyperglycemia on admission, regardless of whether or not in the context of a diabetes mellitus history, could be, thus, identified as a predictor of worse myocardial reperfusion and poorer prognosis in patients with AMI. In order to reduce hyperglycemia-related complications, it seems rational to pursue in these patients an adequate and quick control of PGL, despite the best pharmacological treatment for acute hyperglycemia still remaining a matter of debate.

Admission glucose level and short-term mortality in older patients with acute myocardial infarction: results from the KORA Myocardial Infarction Registry

STUDY OBJECTIVES

To investigate the association between admission blood glucose levels and 28-day mortality as well as in-hospital complications in older patients with incident acute myocardial infarction (AMI) undergoing modern treatment.

METHODS

From a German population-based regional MI registry, 5530 patients (2016 women), aged 65-84 years, hospitalised with an incident AMI between 1 January 2009 and 31 December 2016 were included in the study. Multivariable logistic regression models were used to assess the associations between admission blood glucose and 28-day mortality as well as in-hospital complications after AMI. Analyses stratified according to age, diabetes and type of infarction (ST-elevation MI (STEMI)/non-STEMI) were conducted.

RESULTS

The adjusted ORs for the association between admission blood glucose and 28-day mortality in young-old (65-74 years) and old (75-84 years) patients with AMI were 1.40 (95% CI: 1.21 to 1.62) and 1.21 (95% CI: 0.98 to 1.50) per 1 SD increase in admission blood glucose, respectively. Furthermore, higher admission blood glucose was related to case fatality irrespective of the diabetes status and type of infarction only in the under-75 group. For the patients aged 75-84 years, it was only true for those without diabetes and STEMI. Admission blood glucose was also associated with major cardiac complications in both age groups.

CONCLUSION

Admission blood glucose was significantly associated with 28-day case fatality in patients with AMI aged 65-74 years but not 75-84 years; furthermore, in both age groups there was an increased risk of major complications. It seems that admission glucose may play a rather minor role in terms of case fatality in higher aged patients with AMI.

Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning

Risk stratification at hospital discharge could be instrumental in guiding postdischarge care. In this study, the risk models for 1-year mortality using machine learning (ML) were evaluated for guiding management of acute myocardial infarction (AMI) patients. From the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, 22,182 AMI patients were selected. The 1-year all-cause mortality was recorded at 12-month follow-up periods. Anomaly detection was conducted for removing outliers; principal component analysis for dimensionality reduction, recursive feature elimination algorithm for feature selection. Model selection and training were conducted with 70% of the dataset after the creation and cross-validation of hundreds of models with decision trees, ensembles, logistic regressions, and deepnets algorithms. The rest of the dataset (30%) was used for comparison between the ML and KAMIR score-based models. The mean age of the AMI patients was 64 years, 71.8% were male, and 56.7% were eventually diagnosed with ST-elevation myocardial infarction. There were 1,332 patients suffering from all-cause mortality (6%) during a median 338 days of follow-up. The ML models for 1-year mortality were well-calibrated (Hosmer-Lemeshow p >0.05) and showed good discrimination (area under the curve for test cohort: 0.918). Compared with the performance of the KAMIR score model, the ML model had a higher area under the curve, net reclassification improvement, and integrated discrimination improvement. The ML model for 1-year mortality was well-calibrated and had excellent discriminatory ability and higher performance. In a comprehensive clinical evaluation process, this model could support risk stratification and management in postdischarge AMI patients.

[Prognostic value of HbA1c and plasma glucose on one-year mortality in non-diabetic patients after myocardial infarction]

BACKGROUND

The usefulness of the combined assessment of HbA1c and plasma glucose (PG) in acute myocardial infarction (AMI) in non-diabetic patients remains unclear.

PURPOSE

In a large observational study, we aimed to identify the prognostic values of these biomarkers regarding one-year all-cause mortality in non-diabetic patients after AMI.

METHODS

From the "obseRvatoire des Infarctus de Côte d'Or" (RICO) survey database, we included all consecutive non-diabetic patients with AMI (n=6617) from May 2001 to December 2016. Exclusion criteria were: admission known or unknown diabetes, in-hospital death. The primary endpoint was all-cause one-year mortality. The secondary endpoints were: MACE, infarct size, LVEF<40% and GRACE risk score. Cut-off levels (high/low) were determined by ROC curve analysis for the prediction of one-year death (HbA1c 5.9% and PG 131mg/dL) to set up 4 groups: low HbA1c/low glucose (n=3158), low HbA1c/high glucose (n=1264), high HbA1c/low glucose (n=1378) and high HbA1c/high glucose (n=817).

RESULTS

Elevation of PG was associated with elevated rate of LVEF<40%, STEMI, anterior wall location, DFG<60mL/min/m² and higher troponin Ic pic (all P<0.001); HbA1c>5.9% was associated with elevated rate of CRP>3mg/L (P<0.001); high HbA1c and high PG together were associated with higher rate of MACE (P<0.001). By multivariate logistic regression analysis, elevated admission PG remained a strong predictor of one-year all-cause [OR (95%CI): 1.64 (1.31-2.05)] mortality and cardiovascular mortality [OR (95%CI): 1.75 (1.33-2.31)], beyond GRACE score [OR (95%CI): 1.03 (1.03-1.04)], as well as elevated HbA1c [OR (95%CI): 1.43 (1.15-1.78) and OR (95%CI): 1.83 (1.39-2.41) respectively].

CONCLUSIONS

Admission PG and HbA1c had strong independent predictive value regarding one-year all-cause mortality in our non-diabetic patients with AMI. These biomarkers could be useful to identify the most-at-risk patients after AMI in order to reduce residual risk in this target population.

Role of plasma glucose level on myocardial perfusion in ST-segment elevation myocardial infarction patients

AIMS

Hyperglycemia is frequent in patients with ST elevation myocardial infarction (STEMI) and is associated with adverse outcome. Aim of our study was to evaluate the correlation between admission plasma glucose level (PGL) and coronary arteries flow velocity.

METHODS

We enrolled 149 STEMI patients successfully treated with primary percutaneous coronary intervention (pPCI). The study population was divided into two groups based on PGL (< or >140 mg/dl) and on history of diabetes, and the groups compared in terms of corrected TIMI frame count (cTFC).

RESULTS

Hyperglycemic patients had a significantly higher cTFC in both the culprit (p < 0.0001) and non-culprit vessel (p: 0.0002); diabetes history impairs as well cTFC of the culprit (p < 0.0001) and non-culprit vessel (p: 0.0001). Within the subpopulation of diabetic patients hyperglycemic ones showed higher cTFC in both the culprit (p 0.0013) and non-culprit vessel (p: 0.0006). Moreover in the whole population cTFC values of both arteries increase linearly with the increment of admission PGL.

CONCLUSIONS

Admission PGL affects coronary flow of both culprit and non-culprit vessel. The impairment of coronary flow is also demonstrated in known diabetic patients, suggesting to consider hyperglycemia an additional risk factor. We finally demonstrated for the first time a positive linear relationship between PGL and cTFC.

Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: An Acute Coronary Syndrome Israeli Survey data mining study

BACKGROUND

Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach.

OBJECTIVE

To evaluate an array of machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores.

METHODS

This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered. Prediction models for overall mortality 30days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores.

RESULTS

Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p<0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age.

CONCLUSIONS

We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings.

Glycosylated hemoglobin levels and clinical outcomes in nondiabetic patients with coronary artery disease: A meta-analysis

A number of studies assessed the prognostic value of HbA1c level in nondiabetic patients with coronary artery disease (CAD). The purpose of this meta-analysis was to assess the association between the HbA1c level and clinical outcomes.We searched PubMed, EMBASE, MEDLINE, and the Cochrane Library from their inception to 10 April 2016. Studies evaluated the outcomes according to HbA1c levels in CAD patients without diabetes mellitus were eligible.Twenty studies involving 22,428 patients were included. In nondiabetic patients with CAD, a high HbA1c level was associated with a higher rate of long-term death (odds ratio 1.76, 95% confidence interval 1.44-2.16, P < .001), and myocardial infarction (MI, odds ratio 1.69, 95% confidence interval 1.07-2.67, P = .026), but not a higher rate of early deaths (odds ratio 1.08, 95% confidence interval 0.92-1.27, P = .359). These findings for death remained the same after sensitivity analyses and the trim and fill method, but the risk difference for MI became nonsignificant after adjustment for potential publication bias.Elevated HbA1c level increased the risks of long-term mortality and MI, but not the risk for early deaths in nondiabetic patients with CAD. High-quality large-scale studies with less bias are needed to confirm these findings.

A single-centre cohort study of National Early Warning Score (NEWS) and near patient testing in acute medical admissions

INTRODUCTION

The utility of an early warning score may be improved when used with near patient testing. However, this has not yet been investigated for National Early Warning Score (NEWS). We hypothesised that the combination of NEWS and blood gas variables (lactate, glucose or base-excess) was more strongly associated with clinical outcome compared to NEWS alone.

METHODS

This was a prospective cohort study of adult medical admissions to a single-centre over 20days. Blood gas results and physiological observations were recorded at admission. NEWS was calculated retrospectively and combined with the biomarkers in multivariable logistic regression models. The primary outcome was a composite of mortality or critical care escalation within 2days of hospital admission. The secondary outcome was hospital length of stay.

RESULTS

After accounting for missing data, 15 patients out of 322 (4.7%) died or were escalated to the critical care unit. The median length of stay was 4 (IQR 7) days. When combined with lactate or base excess, NEWS was associated with the primary outcome (OR 1.18, p=0.01 and OR 1.13, p=0.03). However, NEWS alone was more strongly associated with the primary outcome measure (OR 1.46, p<0.01). The combination of NEWS with glucose was not associated with the primary outcome. Neither NEWS nor any combination of NEWS and a biomarker were associated with hospital length of stay.

CONCLUSION

Admission NEWS is more strongly associated with death or critical care unit admission within 2days of hospital admission, compared to combinations of NEWS and blood-gas derived biomarkers.