How to standardize the measurement of left ventricular ejection fraction

Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction

This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary intervention(PCI) or optimal medical therapy(OMT). This study included 329 LVSD patients, who were randomly assigned to the training or validation cohort. Least absolute shrinkage and selection operator(LASSO) regression was used to identify variables associated with MACE. Subsequently, various machine learning models were established. Model performance was compared using receiver operating characteristic(ROC) curves, the Brier score(BS), and the concordance index(C-index). A total of 329 LVSD patients were retrospectively enrolled between January 2016 and December 2021. Utilizing LASSO regression analysis, five factors were selected. Based on these factors, RSF, GBM, XGBoost, Cox, and DeepSurv models were constructed. In the development and validation cohorts, the C-indices were 0.888 vs. 0.955 (RSF). The RSF model (0.991 vs. 0.982 vs. 0.980) had the highest area under the ROC curve (AUC) compared with the other models. The BS (0.077 vs. 0.095vs. 0.077) of RSF model were less than 0.25 at 12, 18, and 24 months. This study developed a novel predictive model based on RSF to predict MACE in LVSD patients who underwent either PCI or OMT.

Development of a Novel Nomogram to Predict Major Adverse Cardiac Events in Patients with Chronic Total Occlusion

Objectives: To create a nomogram using single photon emission computed tomography (SPECT) myocardial perfusion imaging and 18F-FDG positron emissions tomography (PET) gated myocardial metabolism imaging to forecast major adverse cardiovascular events (MACE) in chronic total occlusion (CTO) patients treated with optimal medical therapy (OMT). Methods: A total of 257 patients who received OMT between January 2016 and December 2021 were included in this retrospective study. Patients were randomly divided into development (n=179) and validation (n=78) cohorts. A thorough evaluation was conducted, encompassing clinical features and imaging analysis, which involved assessing myocardial perfusion and metabolism. Independent risk factors were identified using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Calibration curves and decision curve analysis (DCA) were used to evaluate the clinical usefulness. Results: In the development cohort, 53 patients (29.6%) experienced MACE out of 179 patients, while in the validation cohort, MACE occurred in 23 (29.5%) patients out of 78. The PET-left ventricular end-systolic volume (P-ESV) (HR 1.01; 95% CI 1.003-1.017; p=0.003), hibernating myocardium / total perfusion defect (HM/TPD) (HR 1.053; 95% CI 1.038-1.069; p<0.001), PET-left ventricular ejection fraction (P-LVEF) (HR 0.862; 95% CI 0.788-0.943; p=0.001), and left anterior descending branch (LAD) (HR 2.303; 95% CI 1.086-4.884; p=0.03) were significantly associated with MACE and were used to develop the nomogram. The nomogram demonstrated excellent discrimination with C-indexes of 0.931 and 0.911 in the development and validation cohorts. DCA determined that the model exhibited a considerably superior net advantage in predicting MACE. Conclusion: A new nomogram integrating clinical factors and imaging features was created to predict the risk of MACE in patients with CTO.

Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction

BACKGROUND

Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high.

METHODS AND RESULTS

We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later.

CONCLUSIONS

LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography

The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

Can prolonged P-R interval predict clinical outcomes in non-ST elevation acute coronary syndrome patients?

BACKGROUND

The present study aimed to respond to clinical question, can prolonged P-R interval predict clinical outcomes in non-ST elevation acute coronary syndrome patients?

METHODS

This descriptive-analytical study was conducted on cardiac patients. All of the non-ST elevation acute coronary syndrome (NSTEACS) including non-ST elevation myocardial infarction (NSTEMI) and unstable angina patients included in the study. Then they divided into two groups: prolonged P-R interval and normal P-R interval. The patients who had a history of digoxin and calcium channel blocker use, using antiarrhythmic drugs, known valvular or congenital heart disease and connective tissue, unreadable P-R interval and cardiac block were excluded. Data were collected using the questionnaire consisted demographic data and clinical outcomes and a follow-up part was completed by one of the researchers.

RESULTS

Finally, 248 patients completed the study. The results showed both of the two groups had significant differences in terms of the history of myocardial infarction (MI) (p = 0.018), the level of high-density lipoprotein (HDL) (p = 0.004), heart rate (p = 0.042), inverted T wave (p = 0.017), anterior ST- segment depression (p = 0.008), normal report of coronary angiography (CAG) (p = 0.003), three vessels disease (p = 0.043), left main lesion (p = 0.045) and SYNTAX score (p = 0.032) based on the CAG report. The results of six-month follow-up showed although, the frequency of ischemic stroke, coronary artery disease (CAD) and cardiovascular death were higher in prolonged P-R interval groups. The chi-square test showed this difference was statistically non-significant (p > 0.05). The multivariate logistic regression model revealed non-significant relationships between prolonged P-R interval and SYNTAX score, significant CAD, three-vessel disease, inverted T wave, anterior ST depression, heart rate and HDL.

CONCLUSIONS

Based on the results of our study the six-month follow-up showed non-significant outcomes. Further studies are recommended to assess the long-term outcomes.

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Background

With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis.

Objective

This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots.

Methods

The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results

A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound".

Conclusions

AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.

Prognostic Value of Left Ventricular Global Longitudinal Strain for Major Adverse Cardiovascular Events in Patients with Aortic Valve Disease: A Meta-Analysis

INTRODUCTION

Valvular heart disease is one of the most common heart diseases. It is characterized by abnormal function or structure of the heart valves. There may be no clinical symptoms in the early stages. Clinical symptoms of arrhythmia, heart failure, or thromboembolic events may occur in the late stages of the disease, such as palpitation after activities, breathing difficulties, fatigue, and so on. Aortic valve disease is a major part of valvular heart disease. The main treatment for aortic valve disease is valve replacement or repair surgery, but it is extremely risky. Therefore, a rigorous prognostic assessment is extremely important for patients with aortic valve disease. The global longitudinal strain is an index that describes the deformation capacity of myocardium. There is evidence that it provides a test for systolic dysfunction other than LVEF (left ventricular ejection fraction) and provides additional prognostic information.

METHOD

Search literature published between 2010 and 2023 on relevant platforms and contain the following keywords: "Aortic valve disease," "Aortic stenosis," "Aortic regurgitation," and "longitudinal strain" or "strain." The data is then extracted and collated for analysis.

RESULTS

A total of 15 articles were included. The total population involved in this study was 3,678 individuals. The absolute value of LVGLS was higher in the no-MACE group than in the MACE group in patients with aortic stenosis (Z = 8.10, p < 0.00001), and impaired LVGLS was a risk factor for MACE in patients with aortic stenosis (HR = 1.14, p < 0.00001, 95% CI: 1.08-1.20). There was also a correlation between impaired LVGLS and aortic valve surgery in patients with aortic valve disease (HR = 1.16, p < 0.0001, 95% CI: 1.08-1.25) or patients with aortic valve regurgitation (HR = 1.21, p = 0.0004, 95% CI: 1.09-1.34). We also found that impaired LVGLS had no significant association between LVGLS and mortality during the period of follow-up in patients with aortic valve stenosis (HR = 1.08, 95% CI: 0.94-1.25, p = 0.28), but it was associated with mortality in studies of prospective analyses (HR = 1.34, 95% CI: 1.02-1.75, p = 0.04).

CONCLUSIONS

Impaired LVGLS correlates with major adverse cardiovascular events in patients with aortic valve disease, and it has predictive value for the prognosis of patients with aortic valve disease.

From ejection fraction, to myocardial strain, and myocardial work in echocardiography: Clinical impact and controversies

Enhancing an echocardiographic tool, aimed to detect even subtle left ventricular (LV) systolic function abnormalities, capable of obtaining both early diagnosis and risk prediction of heart disease, represents an ambitious, attractive, and arduous purpose in the modern era of cardiovascular imaging. Ideally, that tool should be simple, reliable, and reproducible, in order to be concretely applied in routine clinical practice. Importantly, that technique should be physiologically plausible and useful both at the population-level, as well as in the individual subject. For a long time, LV ejection fraction (EF) has been considered the first-line parameter for assessing LV global systolic function, strictly related to the prognosis, at least in some settings. However, LV EF limitations are well-known, even though frequently overemphasized, including its load-dependency. Therefore, myocardial strain techniques have been proposed, deemed able to disclose even subtle early LV function anomalies. Nevertheless, many disadvantages of myocardial strain have been reported as well. More recently, myocardial work (MW) analysis has been introduced as a new echocardiographic tool for the evaluation of LV global systolic function, attempting to overcome EF and strain disadvantages. However, MW has shown many limits as well. Notwithstanding, LV EF still remains a landmark functional classification marker for heart failure and cardiac oncology, allowing reliable fast reassessment of LV function changes during patient management, in order to guide treatment in individual cases as well. Notably, global longitudinal strain and MW parameters seem to show better meaningful results at the population-level, but controversial clinical impact, major limitations, wide cut-offs spread and overlap, when the single value needs to be applied to the single case. Taking into account the recent literature-based evidence, the scope of the present narrative critical review is trying to delineate the different types of information given by the described LV global systolic function parameters, both at the population-level and in the individual case, in order to trace a comparative analysis of advantages and limitations in clinical practice.

The association between fibrinogen levels and severity of coronary artery disease and long-term prognosis following percutaneous coronary intervention in patients with type 2 diabetes mellitus

Background

Fibrinogen is a potential risk factor for the prognosis of CAD and is associated with the complexity of CAD. There is limited research specifically investigating the predictive role of fibrinogen in determining the severity of CAD among patients with T2DM, as well as its impact on the prognosis following PCI.

Methods

The study included 675 T2DM patients who underwent PCI at the Third People's Hospital of Chengdu between April 27, 2018, and February 5, 2021, with 540 of them remaining after exclusions. The complexity of CAD was assessed using the SYNTAX score. The primary endpoint of the study was the incidence of MACCEs.

Results

After adjusting for multiple confounding factors, fibrinogen remained a significant independent risk factor for mid/high SYNTAX scores (SYNTAX score > 22, OR 1.184, 95% CI 1.022-1.373, P = 0.025). Additionally, a dose-response relationship between fibrinogen and the risk of complicated CAD was observed (SYNTAX score > 22; nonlinear P = 0.0043). The area under the receiver operating characteristic curve(AUROC) of fibrinogen for predicting mid/high SYNTAX score was 0.610 (95% CI 0.567-0.651, P = 0.0002). The high fibrinogen group (fibrinogen > 3.79 g/L) had a higher incidence of calcified lesions and an elevated trend of more multivessel disease and chronic total occlusion. A total of 116 patients (21.5%) experienced MACCEs during the median follow-up time of 18.5 months. After adjustment, multivariate Cox regression analysis confirmed that fibrinogen (HR, 1.138; 95% CI 1.010-1.284, P = 0.034) remained a significant independent risk factor for MACCEs. The AUROC of fibrinogen for predicting MACCEs was 0.609 (95% CI 0.566-0.650, P = 0.0002). Individuals with high fibrinogen levels (fibrinogen > 4.28 g/L) had a higher incidence of acute myocardial infarction (P < 0.001), MACCEs (P < 0.001), all-cause death (P < 0.001), stroke (P = 0.030), and cardiac death (P = 0.002). Kaplan-Meier analysis revealed a higher incidence of MACCEs in the high fibrinogen group (Log-Rank test: P < 0.001).

Conclusions

Elevated fibrinogen levels were associated with increased coronary anatomical complexity (as quantified by the SYNTAX score) and a higher incidence of MACCEs after PCI in patients with T2DM.

Metoprolol or Verapamil in the Management of Patients With Hypertrophic Cardiomyopathy: A Systematic Review

Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease and is a prevalent cause of sudden cardiac death (SCD). This study aims to establish the benefits and therapeutic value metoprolol or verapamil offer to patients who suffer from symptoms caused by HCM, with regard to resolving left ventricular outflow tract obstruction (LVOTO), as well as improving a patient's quality of life and reducing symptoms. We conducted a systematic review to find clinical studies that described the use of metoprolol or verapamil in the management of HCM. Three databases were analyzed for studies, PubMed, Google Scholar, and ScienceDirect. We discovered 6,260 potentially eligible records across all the databases. According to our eligibility criteria, we included four studies in this review. Metoprolol showed median left ventricular outflow tract (LVOT) gradients of 25 mm Hg versus 72 mm Hg (P = 0.007) at rest, 28 mm Hg versus 62 mm Hg (P < 0.001) at peak exercise, and 45 mm Hg versus 115 mm Hg (P < 0.001) post-exercise. Verapamil also showed a statistically significant increase in exercise capacity. Both drugs have been shown to be safe to use with a good side effect profile; however, metoprolol was better tolerated in the patient population that was tested in the studies collected. In this study, metoprolol was effective in reducing LVOT and improving the quality of life in patients, while verapamil showed variable effects on both exercise capacity and baseline hemodynamics.

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

BACKGROUND

Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.

AIM

To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.

METHODS

A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.

RESULTS

The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.

CONCLUSION

This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.