Admission Glucose Levels and Associated Risk for Heart Failure After Myocardial Infarction in Patients Without Diabetes

Determinants of 1-year mortality after acute myocardial infarction in patients with and without diabetes

The gap in excess mortality between patients with and without diabetes has not decreased over time. The aim of this study was to investigate the determinants of mortality after acute myocardial infarction (AMI) in patients with diabetes and without diabetes in a contemporary population. A retrospective analysis of a cohort of 266 patients with a diagnosis of AMI during 2022 was carried out. Patients living with diabetes had higher 1-year mortality, even after adjustment for covariates. Estimated glomerular filtration (eGFR) rate was independently associated with increased mortality in patients with diabetes. Plasma glucose was independently associated with peak troponin in patients both with and without diabetes. These data suggest that patients living with diabetes and with a low eGFR warrant more aggressive risk reduction and use of nephroprotective medications. Further studies are needed to assess whether early blood glucose control improves cardiovascular outcomes in all patients with AMI.

Metabolic signatures in post-myocardial infarction heart failure, including insights into prediction, intervention, and prognosis

Heart failure (HF) is a prevalent long-term complication of myocardial infarction (MI). The incidence of post-MI HF is high, and patients with the condition have a poor prognosis. Accurate identification of individuals at high risk for post-MI HF is crucial for implementation of a protective and ideally personalized strategy to prevent fatal events. Post-MI HF is characterized by adverse cardiac remodeling, which results from metabolic changes in response to long-term ischemia. Moreover, various risk factors, including genetics, diet, and obesity, can influence metabolic pathways in patients. This review focuses on the metabolic signatures of post-MI HF that could serve as non-invasive biomarkers for early identification in high-risk populations. We also explore how metabolism participates in the pathophysiology of post-MI HF. Furthermore, we discuss the potential of metabolites as novel targets for treatment of post-MI HF and as biomarkers for prognostic evaluation. It is expected to provide valuable suggestions for the clinical prevention and treatment of post-MI HF from a metabolic perspective.

Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction

AIMS

Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms.

METHODS

Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively.

RESULTS

The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits.

CONCLUSIONS

This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.

Elevated random glucose levels at admission are associated with all-cause mortality and cardiogenic shock during hospitalisation in patients with acute myocardial infarction and without diabetes: A retrospective cohort study

BACKGROUND

Elevated glucose levels at admission are associated with a worse prognosis in patients with acute myocardial infarction (AMI); additionally, such elevation has a higher prognostic value for patients without diabetes.

METHODS

We retrospectively recruited 2412 AMI patients without diabetes from 1 August 2011 to 10 January 2022. The primary outcome was all-cause mortality during hospitalisation, and the secondary outcomes were cardiogenic shock, ventricular tachycardia, ventricular fibrillation, atrioventricular block and new stroke.

RESULTS

The mean age of participants was 65 years and 78.6% were male. Of the 2412 patients, all-cause mortality occurred in 236 patients (9.8%) during hospitalisation. In multivariate-adjusted models that corrected for variable weights, the risk of all-cause mortality increased with an increase in random glucose levels at admission; specifically, the risk of all-cause mortality increased per 1 mg/dL (odds ratio [OR] 1.006, 95% confidence interval [CI]: 1.004-1.008), per 9 mg/dL (OR: 1.06, 95% CI: 1.04-1.08), and per 18 mg/dL (OR: 1.12, 95% CI: 1.07-1.16) increases in admission glucose levels. When admission glucose levels were expressed as a categorical variable, increased levels of glucose (relative to the reference glucose value <140 mg/dL) led to an increased risk of all-cause mortality; specifically, the OR of all-cause mortality for 140-200 mg/dL glucose was 1.55 (95% CI: 1.09-2.17) and the OR for glucose >200 mg/dL was 3.08 (95% CI: 2.00-4.62) (P for trend <0.001). The risk of cardiogenic shock also increased with glucose levels with an OR of 1.68 (95% CI: 1.21-2.31) for 140-200 mg/dL glucose and an OR of 3.72 (95% CI: 2.50-5.46) for >200 mg/dL, compared with that of glucose <140 mg/dL. In multivariate-adjusted spline regression models, an increased risk of all-cause mortality was observed in patients with glucose ≥122 mg/dL (OR: 1.81, 95% CI: 1.38-2.38, p < 0.001) compared with the reference cohort. Furthermore, patients with glucose ≥111 mg/dL (OR: 2.36, 95% CI: 1.80-3.12) had a higher risk of cardiogenic shock than patients with glucose <111 mg/dL.

CONCLUSIONS

Patients with AMI and without diabetes who had elevated random glucose levels at admission had a higher risk of all-cause mortality and cardiogenic shock during hospitalisation. In particular, patients with glucose ≥122 mg/dL had an increased risk of all-cause mortality, and those with glucose ≥111 mg/dL had an increased risk of cardiogenic shock.