Estimating Myocardial Infarction Size With a Simple Electrocardiographic Marker Score

Sudden death in ischemic heart disease: looking for new predictors: polygenic risk

The phenomenon of sudden death (SD) occurs, in 70% of cases, in people who do not fall within the indications of the guidelines relating to the implantation of the defibrillator. There is a way of inheriting the risk condition by genetic means, the polygenic one, in which mutations are not found, but an increase in alleles of common variations called polymorphisms. The PRE-DETERMINE cohort study has the primary objective of determining whether biological markers, and electrocardiogram can be used to identify individuals more likely to experience SD. Within the study, we investigated the utility of the genome-wide polygenic score for coronary artery disease (GPSCAD) for SD risk stratification in an intermediate-risk population with stable coronary artery disease without severe systolic dysfunction and/or indication for an implantable cardioverter defibrillator in primary prevention. Over a mean follow-up period of 8.0 years, patients in the top decile of GPSCAD were at higher absolute (8.0% vs. 4.8%; P < 0.005) and relative (29% vs. 16%; P < 0.0003) risk of SD compared to the rest of the cohort. No association was found between the highest decile of GPSCAD and other forms of death, cardiac, and non-cardiac. The data on the increase in absolute and relative terms of SD can be used, at this stage, only for a theoretical estimate on the possible efficacy of the defibrillator in the population with chronic coronary artery disease and moderately depressed left ventricular function as number needed to treat and possible reduction of mortality in high-risk patients (those included in the top decile of GPSCAD).

Machine learning-based prediction of infarct size in patients with ST-segment elevation myocardial infarction: A multi-center study

BACKGROUND

Cardiac magnetic resonance imaging (CMR) is the gold standard for measuring infarct size (IS). However, this method is expensive and requires a specially trained technologist to administer. We therefore sought to quantify the IS using machine learning (ML) based analysis on clinical features, which is a convenient and cost-effective alternative to CMR.

METHODS AND RESULTS

We included 315 STEMI patients with CMR examined one week after morbidity in final analysis. After feature selection by XGBoost on fifty-six clinical features, we used five ML algorithms (random forest (RF), light gradient boosting decision machine, deep forest, deep neural network, and stacking) to predict IS with 26 (selected by XGBoost with information gain greater than average level of 56 features) and the top 10 features, during which 5-fold cross-validation were used to train and optimize models. We then evaluated the value of actual and ML-IS for the prediction of adverse remodeling. Our finding indicates that MLs outperform the linear regression in predicting IS. Specifically, the RF with five predictors identified by the exhaustive method performed better than linear regression (LR) with 10 indicators (R² of RF: 0.8; LR: 0). The finding also shows that both actual and ML-IS were independently associated with adverse remodeling. ML-IS ≥ 21% was associated with a twofold increase in the risk of LV remodeling (P < 0.01) compared with patients with reference IS (1st tertile).

CONCLUSION

ML-based methods can predict IS with widely available clinical features, which provide a proof-of-concept tool to quantitatively assess acute phase IS.

Assessment of a New Electrocardiographic Marker for the Diagnosis of Myocardial Infarction With Non-obstructive Coronary Arteries

Myocardial infarction with non-obstructive coronary arteries (MINOCA) is clinically defined as myocardial infarction in the absence of obstructive atherosclerosis on coronary angiography. Diagnosis may require multiple diagnostic tools in addition to standard coronary angiography, including cardiac imaging or provocative tests, according to clinical suspicion. We assessed the usefulness of the DETERMINE (Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation) score for distinguishing patients with MINOCA from those with non-ST segment elevation myocardial infarction with obstructive coronary artery disease (NSTEMI-CAD) in a single-center observational study. The patients were divided into two groups according to coronary angiography findings. The study included 277 patients: 227 with NSTEMI-CAD and 50 with MINOCA. The DETERMINE score (6.1 ± 3.7 vs 1.9 ± 2.1, P<.001) was significantly higher in the NSTEMI-CAD than MINOCA group. Logistic regression analysis revealed that the DETERMINE score (OR=0.591, P<0.001) was an independent predictor of MINOCA. The incidence of diabetes mellitus and glucose levels were significantly higher in the NSTEMI-CAD group; however, ejection fraction was significantly higher in the MINOCA group (P<0.05). Our findings suggest that the DETERMINE score constitutes a simple and inexpensive tool for the assessment of MINOCA.

Targeting Ferroptosis by Polydopamine Nanoparticles Protects Heart against Ischemia/Reperfusion Injury

Ferroptosis is a new form of regulated cell death depending on elevated iron (Fe²⁺) and lipid peroxidation levels. Myocardial ischemia/reperfusion (I/R) injury has been shown to be closely associated with ferroptosis. Therefore, antiferroptosis agents are considered to be a new strategy for managing myocardial I/R injury. Here, we developed polydopamine nanoparticles (PDA NPs) as a new type of ferroptosis inhibitor for cardioprotection. The PDA NPs features intriguing properties in inhibiting Fe²⁺ accumulation and restoring mitochondrial functions in H9c2 cells. Subsequently, we demonstrated that administration of PDA NPs effectively reduced Fe²⁺ deposition and lipid peroxidation in a myocardial I/R injury mouse model. In addition, the myocardial I/R injury in mice was alleviated by PDA NPs treatment, as demonstrated by reduced infarct size and improved cardiac functions. The present work indicates the therapeutic effects of PDA NPs against myocardial I/R injury via preventing ferroptosis.

Estimating the extent of myocardial damage in patients with STEMI using the DETERMINE score

Background

Recently, a simple ECG score (DETERMINE score) has been proposed for estimating myocardial scar in patients with ischaemic cardiomyopathy. We sought to evaluate the usefulness of the DETERMINE score for the assessment of myocardial infarct size (IS) as well as microvascular obstruction (MVO), in the setting of ST-elevation myocardial infarction (STEMI).

Methods

This observational study enrolled 423 patients with STEMI (median age 56, 17% women), revascularised by primary percutaneous coronary intervention (PCI). For evaluation of the DETERMINE and Selvester scoring system (an established but complex ECG score for IS estimation), ECG was conducted before discharge (median: 4 (IQR 2–6) days). Cardiac magnetic resonance (CMR) was conducted within a week after infarction for determination of IS and MVO.

Results

Median DETERMINE score of the overall cohort was 8 points (IQR 5–11). A higher DETERMINE score was significantly associated with a larger IS (21% vs 11% of left ventricular myocardial mass (LVMM), p<0.001) as well as larger MVO (1.2% vs 0.0% of LVMM, p<0.001). In linear and binary multivariable logistic regression analysis, the DETERMINE score remained independently associated with IS (OR 1.09, 95% CI 1.02 to 1.17, p=0.014) and MVO (OR 1.12, 95% CI 1.04 to 1.21, p=0.003), after adjustment for Selvester score and clinical indicators of IS (high-sensitivity cardiac troponin T, high-sensitivity C reactive protein, N-terminal pro-B-type natriuretic peptide, TIMI flow pre-interventional and post-interventional PCI, anterior infarct localisation).

Conclusions

In patients undergoing PCI for STEMI, the DETERMINE score provides an easy and inexpensive tool for appropriate estimation of infarct severity as determined by CMR.

The study of border zone formation in ischemic heart using electro-chemical coupled computational model

Myocardial infarction (MI) is the most common cause of a heart failure, which occurs due to myocardial ischemia leading to left ventricular (LV) remodeling. LV remodeling particularly occurs at the ischemic area and the region surrounds it, known as the border zone. The role of the border zone in initiating LV remodeling process urges the investigation on the correlation between early border zone changes and remodeling outcome. Thus, this study aims to simulate a preliminary conceptual work of the border zone formation and evolution during onset of MI and its effect towards early LV remodeling processes by incorporating the oxygen concentration effect on the electrophysiology of an idealized three-dimensional LV through electro-chemical coupled mathematical model. The simulation result shows that the region of border zone, represented by the distribution of electrical conductivities, keeps expanding over time. Based on this result, the border zone is also proposed to consist of three sub-regions, namely mildly, moderately, and seriously impaired conductivity regions, which each region categorized depending on its electrical conductivities. This division could be used as a biomarker for classification of reversible and irreversible myocardial injury and will help to identify the different risks for the survival of patient. Larger ischemic size and complete occlusion of the coronary artery can be associated with an increased risk of developing irreversible injury, in particular if the reperfusion treatment is delayed. Increased irreversible injury area can be related with cardiovascular events and will further deteriorate the LV function over time.

The study of myocardial ischemia-reperfusion treatment through computational modelling

Reperfusion of the blood flow to ischemic myocardium is the standard treatment for patients suffering myocardial infarction. However, the reperfusion itself can also induce myocardial injury, in which the actual mechanism and its risk factors remain unclear. This work aims to study the mechanism of ischemia-reperfusion treatment using a three-dimensional (3D) oxygen diffusion model. An electrical model is then coupled to an oxygen model to identify the possible region of myocardial damage. Our findings show that the value of oxygen exceeds its optimum (>1.0) at the ischemic area during early reperfusion period. This complication was exacerbated in a longer ischemic period. While a longer reperfusion time causes a continuous excessive oxygen supply to the ischemic area throughout the reperfusion time. This work also suggests the use of less than 0.8 of initial oxygen concentration in the reperfusion treatment to prevent undesired upsurge at the early reperfusion period and further myocardial injury. We also found the region at risk for myocardial injury is confined in the ischemic vicinity revealed by its electrical conductivity impairment. Although there is a risk that reperfusion leads to myocardial injury for excessive oxygen accumulation, the reperfusion treatment is helpful in reducing the infarct size.

Estimating Myocardial Infarction Size With a Simple Electrocardiographic Marker Score

Background

Myocardial infarction (MI) size is a key predictor of prognosis in post‐MI patients. Cardiovascular magnetic resonance (CMR) is the gold standard test for MI quantification, but the ECG is less expensive and more widely available. We sought to quantify the relationship between ECG markers and cardiovascular magnetic resonance infarct size.

Methods and Results

Patients with prior MI enrolled in the DETERMINE (Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation) and PRE‐DETERMINE Trial and Registry were included. ECG leads were analyzed for markers of MI: Q waves, fragmented QRS, and T wave inversion. DETERMINE Score=number of leads with [Q waves×2]+[fragmented QRS]+[T wave inversion]. Left ventricular ejection fraction (LVEF) and infarct size as a percentage of left ventricular mass (MI%) were quantified by cardiovascular magnetic resonance. The Modified Selvester Score estimates MI size from 37 ECG criteria. In 551 patients (aged 62.1±10.9 years, 79% men, and LVEF=40.3±11.0%), MI% increased as the number of ECG markers increased (P<0.001). By univariable linear regression, the DETERMINE Score (range 0–26) estimated MI% (R²=0.18, P<0.001) with an accuracy approaching that of LVEF (R²=0.22, P<0.001) and higher than the Modified Selvester Score (R²=0.09, P<0.001). By multivariable linear regression, addition of the DETERMINE Score improved estimation of MI% over LVEF alone (P<0.001) and over Modified Selvester Score alone (P<0.001).

Conclusions

In patients with prior MI, a simple ECG score estimates infarct size and improves infarct size estimation over LVEF alone. Because infarct size is a powerful prognostic indicator, the DETERMINE Score holds promise as a simple and inexpensive risk assessment tool.