E/e' in relation to outcomes in ST-elevation myocardial infarction

Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients

BACKGROUND

Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units.

METHODS

This historical cohort study included consecutive adults admitted to Mayo Clinic cardiac intensive care unit from 2007 to 2018 with an admission AIECG. The AIECG assigned the LVDD grade (0-3). Medial mitral E/e' ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. In-hospital and 1-year mortality was evaluated, before and after multivariable adjustment.

RESULTS

We included 11 868 patients (median age 69.5 years, 37.7% female); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grade was 0 (normal), 33%; 1, 7%; 2, 39%; and 3, 21%. In-hospital and 1-year mortality increased in each higher AIECG LVDD grade. After adjustment, each higher AIECG LVDD grade was associated with higher in-hospital (adjusted odds ratio [OR], 1.22 [95% CI, 1.13-1.32]) and 1-year mortality (adjusted hazard ratio [HR], 1.23 [95% CI, 1.19-1.29]); this persisted after adjustment for TTE measurements. Patients with grade 2 or 3 LVDD by AIECG and medial mitral E/e' ratio >15 by TTE had the highest in-hospital (adjusted OR, 2.54 [95% CI, 1.69-3.88]) and 1-year (adjusted HR, 2.03 [95% CI, 1.65-2.48]) mortality, whereas patients meeting either of these criteria had similar, elevated mortality.

CONCLUSIONS

The AIECG LVDD grade was strongly associated with in-hospital and 1-year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures were at highest risk.

Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit

Aims

Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.

Methods and results

In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 ± 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (n = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision-recall AUC = 0.57, F1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all P < 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88).

Conclusion

This new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables.

Trial Registration

ClinicalTrials.gov Identifier: NCT05063097.

Prognostic Value of Advanced Echocardiography in Patients with Ischemic Heart Disease: A Comprehensive Review

Cardiovascular (CV) diseases caused 20.5 million deaths in 2021, making up nearly one-third of global mortality. This highlights the need for practical prognostic markers to better classify patients and guide treatment, especially in ischemic heart disease (IHD), which represents one of the leading causes of CV mortality. Transthoracic echocardiography (TTE) is a key, non-invasive imaging tool widely used in cardiology for diagnosing and managing a range of CV conditions. It is the first choice for diagnosing and monitoring patients with acute coronary syndrome (ACS). Alongside well-established echocardiographic measures, new techniques have proven useful for predicting adverse events in IHD patients, such as three-dimensional (3D) and tissue Doppler imaging (TDI), and speckle tracking technology. This review aims to explore the latest echocardiographic tools that could provide new prognostic markers for patients in the acute phase and during follow-up after an acute myocardial infarction (AMI). We focus on new imaging methods like TDI, myocardial work index (MWI), speckle-tracking strain, and 3D technologies using TTE, which are easy to use and widely available at all stages of coronary artery disease (CAD).

Left atrial phasic volumes and functions changes in asymptomatic patients with sarcoidosis: evaluation by three-dimensional echocardiography

BACKGROUND

Cardiac involvement is the leading cause of morbidity and death in patients with sarcoidosis. However, many patients remain asymptomatic until the late-stage. In this study, we investigated the left atrial (LA) phasic volumes and functions changes by three-dimensional (3D) echocardiography measurements in asymptomatic patients with sarcoidosis, which has good correlation with cardiac magnetic resonance imaging.

METHODS

In this cross-sectional study, 44 asymptomatic patients with sarcoidosis and 40 age, sex and BMI-matched healthy volunteers underwent two-dimensional (2D) and 3D-echocardiograpy. Standard echocardiographic and tissue Doppler imaging parameters were obtained. LA phasic volumes were assessed by 3D-echocardiography. From the 3D-echocardiography derived values, LA active, passive, and total emptying fraction (EF) were calculated.

RESULTS

All left ventricular ejection fractions (LVEF) obtained by 2D and 3D-echocardiography were normal (≥50%). While LA diameters (33.36 ± 4.23 vs. 30.57 ± 5.43) and E/e' septal annulus ratios (10.82 ± 1.79 vs. 9.27 ± 1.81) were significantly higher, A-wave (70.80 ± 5.81 vs. 74.51 ± 5.41) and e'septal annular velocities (6.48 ± 1.58 vs. 9.03 ± 1.63) were significantly lower in the sarcoidosis group as compared with control group, respectively. While 3D-echocardiography derived LA-minimum volume indices (LAVImin) (13.89 ± 2.75 vs. 12.23 ± 1.73) were significantly higher, 3D-echocardiography derived LA active EFs (AAEF) (30.78 ± 3.52 vs. 38.52 ± 4.75) and LA total EFs (TAEF) (47.71 ± 7.47 vs. 53.32 ± 5.81) were found to be significantly lower in the sarcoidosis group as compared with control group, respectively.

CONCLUSION

LAVImin, AAEF and TAEF calculated based on LA phasic volumes obtained by 3D-echocardiography may be promising indicators of subclinical cardiac involvement in asymptomatic patients with sarcoidosis.

The long-term prognostic value of E/e' in patients with ST segment elevation myocardial infarction

Objectives

This study aimed to evaluate the long-term prognostic value of E/e’ ratio in patients with ST-segment elevation myocardial infarction (STEMI).

Methods

We retrospectively assessed 314 patients who underwent primary coronary interventions between January 2010 and December 2015. The included patients were classified into two groups according to the E/e’ ratios: E/e’<15 (n = 245) and E/e’≥15 (n = 69). We investigated the incidence of major adverse cardiac events (MACEs) from the event to the final follow-up period of at least three years.

Results

A total of 55 cases of MACEs occurred during the follow-up. The E/e’≥15 group showed a significantly higher rate of MACEs than the E/e’<15 group (34.8% vs. 12.7%, p < 0.001). Among the MACE, the percentage of cardiac deaths (17.4% vs. 0.4%, p < 0.001) was higher in the E/e’≥15 group than in the E/e’<15 group. In the multivariable model, E/e’≥15 was demonstrated as the strongest prognostic factor for MACEs (hazard ratio [HR], 2.597; 95% confidence interval [CI], 1.294–5.211; p = 0.007) and cardiac death (HR, 27.537; 95% CI, 3.287–230.689; p = 0.002), while left ventricular ejection fraction (LVEF) was not. Neither the discrepancy of systolic nor diastolic function between initial and follow-up echocardiography affected the overall prevalence of MACEs. A disparity was observed between the two groups, with a significant increase in the rate of MACEs in the E/e’≥15 group (log-rank test, p < 0.001).

Conclusion

The baseline E/e’≥15 in patients with STEMI after successful reperfusion is the strongest predictor of poor long-term clinical outcomes among those analyzed.