Predictors of in-hospital mortality in diabetic patients with non-ST-elevation myocardial infarction

Assessment of the Impact of Comorbidities on Outcomes in Non-ST Elevation Myocardial Infarction (NSTEMI) Patients: A Narrative Review

Non-ST-segment elevation myocardial infarction (NSTEMI) is associated with significant morbidity and mortality, occurring when the heart's need for oxygen cannot be met. It is defined by elevated cardiac biomarkers without ST-segment elevation and often carries a poorer prognosis than most ST-segment elevation events. NSTEMI usually results from severe coronary artery narrowing, transient occlusion, or microembolization of thrombus/atheromatous material. Patients with NSTEMI often have multiple comorbidities, which can worsen their prognosis and complicate treatment. This study aims to investigate the impact of comorbidities such as hypertension (HTN), diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), obesity, dyslipidemia, and smoking on patients with NSTEMI. The prevalence of each comorbidity is examined individually within the NSTEMI population to provide a clearer picture of how frequently these conditions co-occur with NSTEMI and how they affect the established NSTEMI treatment protocols.  This paper sheds light on the interaction between NSTEMI and commonly associated comorbidities through a comprehensive literature review and data analysis. This is critical for optimizing clinical decision-making and enhancing patient care, ultimately improving outcomes in this high-risk patient population.

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

BACKGROUND AND OBJECTIVE

Despite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI).

METHODS

A single-center observational retrospective study was conducted, enrolling 4677 electronic medical records of patients with STEMI after PCI, which were analyzed using statistical analysis and machine learning methods. A pool of potential IHM predictors was identified, and prognostic models were developed and validated based on multivariate logistic regression, random forest, and stochastic gradient boosting methods at two stages of hospital treatment: during the initial physicians examination in the emergency department and immediately after PCI surgery. To explain the IHM prognosis, threshold values of IHM risk factors were determined using 3 grid search methods for optimal cut-off points, calculating centroids and SHapley Additive exPlanations (SHAP).

RESULTS

IHM prognostic models were developed using clinical and functional status data of STEMI patients during two stages of hospital treatment. The IHM prediction accuracy according to the first scenario was AUC = 0.85, and according to the second - AUC = 0.9. Predictors identified and validated in the models were converted into risk factors. Models whose parameters were risk factors demonstrated high forecast accuracy (AUC = 0.87), with the best model formed using the SHAP method.

CONCLUSIONS

For the forecast result interpretation risk factors obtained by categorizing continuous variables can be used by assessing the impact of the latter on the end point using the SHAP method.

C-reactive protein to albumin ratio may predict in-hospital mortality in non-ST elevation myocardial infarction

Objective: The authors investigated the value of novel inflammatory markers, systemic immune-inflammation index and C-reactive protein to albumin ratio (CAR), to predict in-hospital mortality in patients with non-ST elevation myocardial infarction (NSTEMI). Materials & methods: A total of 308 patients who underwent percutaneous coronary intervention because of NSTEMI were retrospectively included in the study. Killip classification, Thrombolysis in Myocardial Infarction score, SYNTAX score, and CAR and systemic immune-inflammation index values were calculated. Results: CAR (cutoff: 0.0864; sensitivity: 94.1%; specificity: 40.5%; p = 0.008) and Killip classification (cutoff: 2.5; sensitivity: 64.7%; specificity: 8.9%; p = 0.001) were found to be significantly higher in determining in-hospital mortality. Conclusion: This study revealed that CAR is an inexpensive and significant factor in predicting in-hospital mortality in patients undergoing percutaneous coronary intervention for NSTEMI.