Could fragmented QRS predict mortality in aortic stenosis patients after transcatheter aortic valve replacement?

Predictive potential of pre-procedural cardiac and inflammatory biomarkers regarding mortality following transcatheter aortic valve implantation: A systematic review and meta-analysis

BACKGROUND

Aortic stenosis (AS) is a common heart valve disease, especially in aging populations. While surgical aortic valve replacement (SAVR) is the standard treatment, many patients are ineligible. Transcatheter aortic valve implantation (TAVI) offers an alternative, especially for high-risk patients, but is not without complications. Identifying biomarkers that predict post-TAVI mortality is essential for optimizing outcomes.

OBJECTIVES

The purpose of this systematic review and meta-analysis is to evaluate the role of cardiac and inflammatory biomarkers in predicting short-term and mid to long-term mortality following TAVI.

METHODS

We searched PubMed, Scopus, Embase, and Web of Science for studies examining the impact of inflammatory and cardiac biomarkers on mortality following TAVI. Mean differences (MDs) and 95 % confidence interval (CI) were calculated using a random-effect model.

RESULTS

Twenty-eight studies involving 10,560 patients were included, with 1867 in the mortality group. Mortality was significantly associated with higher pre-procedural levels of creatinine (0.41; 95 % CI: [0.35, 0.48]), brain natriuretic peptide (0.58; 95 % CI: [0.43, 0.73]), C-reactive protein (0.55; 95 % CI: [0.45, 0.64]), and white blood cell count (0.18; 95 % CI: [0.06, 0.31]), and lower pre-procedural levels of hemoglobin (-0.49; 95 % CI: [-0.60, -0.38]) and albumin (-0.18; 95 % CI: [-0.24, -0.13]). These associations remained statistically significant in subgroup analyses for both mid to long-term mortality and short-term mortality, except for WBC levels, which were not significantly associated with short-term mortality, and Hb, for which short-term data were insufficient. Platelet count showed no significant difference.

CONCLUSION

These findings highlight the importance of inflammatory and cardiac biomarkers in risk stratification and patient management in TAVI procedures.

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review

BACKGROUND

Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility. Electrocardiograms (ECGs) are widely available and cost-effective for monitoring cardiac electrical activity. While ECG-based methods for inferring fibrosis exist, they are not commonly used due to accuracy limitations and the need for cardiac expertise. However, the ECG shows promise as a target for machine learning (ML) applications in fibrosis detection.

OBJECTIVE

This study aims to synthesize and critically evaluate the current state of ECG-based ML approaches for cardiac fibrosis detection.

METHODS

We conducted a scoping review of research in ECG-based ML applications to identify cardiac fibrosis. Comprehensive searches were performed in PubMed, IEEE Xplore, Scopus, Web of Science, and DBLP databases, including publications up to October 2024. Studies were included if they applied ML techniques to detect cardiac fibrosis using ECG or vectorcardiogram data and provided sufficient methodological details and outcome metrics. Two reviewers independently assessed eligibility and extracted data on the ML models used, their performance metrics, study designs, and limitations.

RESULTS

We identified 11 studies evaluating ML approaches for detecting cardiac fibrosis using ECG data. These studies used various ML techniques, including classical (8/11, 73%), ensemble (3/11, 27%), and deep learning models (4/11, 36%). Support vector machines were the most used classical model (6/11, 55%), with the best-performing models of each study achieving accuracies of 77% to 93%. Among deep learning approaches, convolutional neural networks showed promising results, with one study reporting an area under the receiver operating characteristic curve (AUC) of 0.89 when combined with clinical features. Notably, a large-scale convolutional neural network study (n=14,052) achieved an AUC of 0.84 for detecting cardiac fibrosis, outperforming cardiologists (AUC 0.63-0.66). However, many studies had limited sample sizes and lacked external validation, potentially impacting the generalizability of the findings. Variability in reporting methods may affect the reproducibility and applicability of these ML-based approaches.

CONCLUSIONS

ML-augmented ECG analysis shows promise for accessible and cost-effective detection of cardiac fibrosis. However, there are common limitations with respect to study design and insufficient external validation, raising concerns about the generalizability and clinical applicability of the findings. Inconsistencies in methodologies and incomplete reporting further impede cross-study comparisons. Future work may benefit from using prospective study designs, larger and more clinically and demographically diverse datasets, advanced ML models, and rigorous external validation. Addressing these challenges could pave the way for the clinical implementation of ML-based ECG detection of cardiac fibrosis to improve patient outcomes and health care resource allocation.

Fragmented QRS as a predictor of hypertensive crisis in patients with newly diagnosed essential hypertension: 4-year follow-up data

BACKGROUND

Hypertensive crisis (HC) is a life-threatening condition in patients with hypertension (HT). However, there is no electrocardiography (ECG) marker that can predict which hypertensive patient may develop HC. The fragmented QRS (fQRS) complex is an important prognostic marker in ECG that might be predict cardiovascular events and mortality. Our study aimed to investigate whether fQRS can predict the development of HC in patients with HT, within 4 years of follow-up.

METHODS

Newly diagnosed patients with essential HT were recruited for the study from an outpatient clinic. The patients were divided into two groups according to the presence of HC: HC (+) and HC (-). During follow-up, the relationship between fQRS and HC was evaluated.

RESULTS

The study group included 504 patients with newly diagnosed HT. During the follow-up period, HC occurred in 98 of the patients. In 57 (11.30%) patients, fQRS was observed on ECG; fQRS was detected in the ECG of 19 (19.38%) of the HC (+) patients (p = 0.008). The results of multivariate logistic regression analysis showed that fQRS (p < 0.001) was as independent predictor for HC development. Kaplan-Meier analysis further demonstrated that the presence of fQRS affects the development of hypertensive urgency in hypertensive patients (log-rank p < 0.001).

CONCLUSION

In patients with newly diagnosed HT, the presence of fQRS was found to be an independent predictor of HC.

Fragmented QRS complex may predict long-term mortality after isolated surgical aortic valve replacement in patients with severe aortic stenosis

OBJECTIVES

Fragmented QRS (fQRS), related to myocardial fibrosis, is an important prognostic marker of cardiovascular events and mortality. Aortic stenosis (AS), the most frequent valvular heart disease in developed countries, causes myocardial fibrosis due to ventricular pressure overload. The current study aimed to investigate whether fQRS is associated with long-term mortality after isolated surgical aortic valve replacement (SAVR) in patients with severe AS.

METHODS

A total of 289 patients who underwent SAVR for severe AS between May 2009 and January 2020 with interpretable electrocardiogram were included. Patients were divided into 2 groups according to the presence of fQRS. Kaplan-Meier survival analyses were used to detect cumulative survival rates. Univariable and multivariable Cox proportional hazards models were used to determine the predictors of all-cause mortality.

RESULTS

fQRS occurred in 126 (43.5%) patients. A total of 59 (20.4%) patients died over a follow-up period of 54 ± 32 months. All-cause mortality was higher in the fQRS group (23 [14.1%] vs 36 [28.6], log-rank test P = 0.002) in the long term. The presence of fQRS [hazard ratio (HR): 1.802, confidence interval (CI): 1.035-3.135, P = 0.037], electrocardiographic left ventricular strain (HR: 1.836, CI: 1.036-3.254, P = 0.038) and history of stroke or transient ischaemic attack (HR: 3.130, CI: 1.528-6.412, P = 0.002) were independent predictors of all-cause mortality in the multivariable Cox regression model.

CONCLUSIONS

fQRS is associated with a 1.8-fold increase in long-term mortality in patients undergoing isolated SAVR for severe AS. Detecting fQRS in electrocardiograms may provide prognostic information about the long-term outcomes.

The relationship between galectin-3 levels and fragmented QRS (fQRS) in patients with heart failure with reduced left ventricular ejection fraction

BACKGROUND

Fragmented QRS (fQRS) complex is an electrocardiographic pattern which reflects myocardial scarring. We aimed to investigate the relationship between the presence of fragmented QRS (fQRS) on electrocardiogram (ECG) and plasma galectin-3 levels in patients with heart failure (HF) and severely decreased left ventricular ejection fraction (LVEF ≤ 35%).

METHODS

We prospectively enrolled 125 symptomatic HF patients (NYHA class II-III) with severely reduced LVEF (≤35%). fQRS was identified in ECG. Galectin-3 and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels were measured. Patients were divided into two groups based on the presence (n = 40) or absence (n = 85) of a fQRS on ECG.

RESULTS

Majority of patients were male (87.70%), and mean age was 65.1 ± 11.6. Galectin-3 and NT-proBNP levels were found to be significantly higher in the fQRS (+) group compared with the fQRS (-) group (NT-proBNP 5,362 ± 701 pg/ml vs. 4,452 ± 698 pg/ml; p < 0.001, galectin-3 607 ± 89.8 pg/ml vs. 509.4 ± 63.5 pg/ml; p < 0.001). Multivariate analyses revealed galectin-3 and NT-proBNP levels are the presence of fQRS on ECG (p < 0.001 and p < 0.001, respectively). The area under the curve using the galectin-3 level for fQRS was 0.819.

CONCLUSIONS

fQRS and serum galectin-3 levels are associated with myocardial fibrosis and are associated with poor prognosis in heart failure. In our study, a positive correlation was found between serum galectin-3 levels and fQRS on ECG.

Could fragmented QRS predict mortality in aortic stenosis patients after transcatheter aortic valve replacement?

BACKGROUND

Fragmented QRS evaluated in 12-derivation electrocardiography has widely been accepted as a sign of myocardial fibrosis. The prognostic value of that marker has been demonstrated, particularly, in cardiac diseases that accompany myocardial scar and fibrosis. Myocardial fibrosis is also an issue in patients with aortic stenosis. In this study, we wanted to determine whether fragmented QRS could predict all-cause mortality in aortic stenosis patients after transcatheter aortic valve replacement (TAVR).

METHOD

In this study, we evaluated a total of 116 eligible patients on whom we performed TAVR between 2014 and 2018. Patients' demographic and clinical findings, echocardiography results, in-hospital and 30-day mortality, long-term survival statuses were noted. Patient's ECGs before the procedure were evaluated in regard to the occurrence of fragmented QRS. Predictors of mortality were evaluated using univariable and multivariable Cox regression analysis.

RESULTS

The study population consisted of 116 patients of median age 79 (IQR 75-83), 64 females (55.2%). Mortality occurred in 27 (23%) patients; median follow-up time was 319 (IQR 122-719) days. Fragmented QRS was observed in 44 out of 116 (37.9%) patients. The presence of a fragmented QRS (HR = 2.178, 95% CI 0.999-4.847, p = 0.050), a history of stroke (HR = 3.463, 95% CI 1.276-9.398, p = 0.015), and the creatinine levels at admission (HR = 2.198, 95% CI 1.068-4.520, p = 0.030) were associated with the long-term mortality in multivariable Cox regression analysis.

CONCLUSION

Like in the case of the other diseases associated with myocardial fibrosis, fragmented QRS could also predict mortality in aortic stenosis patients after TAVR procedure.