The hemodynamic effect of simulated atrial fibrillation on left ventricular function

Stojadinovic, P; Deshraju, Aslesha; Wichterle, Dan; Fukunaga, Masato; Peichl, Petr; Kautzner, Josef; Šramko, Marek

doi:10.1111/jce.15669

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…The irregular contractions during AF impair the atrium’s pumping function, leading to decreased blood flow speed and increased risk of blood stasis and thrombus formation in patients with NVAF 30 . Prior research has demonstrated a strong link between LAT/SEC and left atrial hemodynamic parameters 31 . Fang et al discovered that these parameters can help assess stroke risk in NVAF patients by indicating the association between thrombosis risk and blood flow status in the left atrium and left atrial appendage 18 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Zeng,

Chang,

Zhang

et al. 2024

Preprint

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BACKGROUND: This study sought to develop a robust machine learning (ML)-based predictive model that synthesizes multimodal echocardiographic data and clinical risk factors to assess thrombosis risk in patients with non-valvular atrial fibrillation (NVAF). METHODS AND RESULTS: A total of 402 NVAF patients scheduled for AF radiofrequency ablation and/or left atrial appendage closure at the First Affiliated Hospital of Guangxi Medical University from January 2020 to December 2023 were prospectively collected. Among them, there were 289 males (71.9%) and 113 females (28.1%), with a mean age of 59.7 years. There were 142 patients (35.3%) with left atrial thrombus/spontaneous echocardiographic contrast (LAT/SEC) and 260 patients (64.7%) without LAT/SEC. We constructed seven ML models from the collected clinical data, biochemical markers, and multimodal echocardiographic parameters. Models were evaluated via ROC curves; SHAP values were employed for feature importance analysis.The integration of multimodal echocardiographic parameters with clinical risk factors based on sophisticated ML algorithms significantly improved the precision of thrombus risk prediction in patients with NVAF. Specifically, the XGBoost model (AUC 0.959, 95% CI 0.925?0.993) slightly outperformed the traditional Logistic regression model (AUC 0.949, 95% CI: 0.911-0.987) in predicting thrombus formation risk in NVAF patients, and showed superior predictive ability compared to other ML algorithms. Additionally, XGBoost offered greater clinical net benefit within a threshold probability range of 0.1 to 1.0. SHAP analysis revealed that left atrial structure (left atrial volume index, three-dimensional sphericity index), hemodynamic parameters (left atrial acceleration factor and S/D ratio), and functional parameters (peak atrial longitudinal strain) were important features in predicting the risk of thrombus formation in NVAF patients, with reduced peak atrial longitudinal strain being the most important risk factor for predicting thrombus. Conclusion: Developing a predictive model utilizing ML techniques that incorporate multimodal echocardiographic parameters in conjunction with clinical risk factors has the potential to enhance the predictive accuracy of the thrombosis risk in individuals with NVAF. The XGBoost model shows that decreased PALS, hemodynamic abnormalities and left atrium spherical remodeling are significant factors correlated with increased risk of thrombus in NVAF.

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Section: Discussionmentioning

confidence: 99%

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Zeng,

Chang,

Zhang

et al. 2024

Preprint

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“…Thus new onset AF may be a marker of risk factors, which are known to affect EF recovery and increase heart failure risk. However direct hemodynamic effects of AF caused by the loss of atrial contraction, heart rate irregularity and increased heart rate may negatively influence EF recovery ( 40 ). Whether targeting patients with new onset AF can decrease mortality risk and improve EF after MI needs to be further evaluated.…”

Section: Discussionmentioning

confidence: 99%

Trajectories and determinants of left ventricular ejection fraction after the first myocardial infarction in the current era of primary coronary interventions

Wohlfahrt

Jenča

Melenovský

et al. 2022

Front. Cardiovasc. Med.

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BackgroundLeft ventricular ejection fraction (EF) is an independent predictor of adverse outcomes after myocardial infarction (MI). However, current data on trajectories and determinants of EF are scarce. The present study aimed to describe the epidemiology of EF after MI.MethodsData from a single-center prospectively-designed registry of consecutive patients hospitalized at a large tertiary cardiology center were utilized.ResultsOut of 1,593 patients in the registry, 1,065 were hospitalized for MI type I (65.4% STEMI) and had no previous history of heart failure or MI. At discharge, EF < 40% was present in 238 (22.3%), EF 40–50% in 326 (30.6%) and EF > 50% in 501 (47.0%). Patients with EF < 40% were often those who suffered subacute and anterior STEMI, had higher heart rate at admission and higher maximal troponin level, and had more often HF signs requiring intravenous diuretics. Among subjects with EF < 40%, the follow-up EF was available in 166 (80% of eligible). Systolic function recovered to EF > 50% in 39 (23.1%), slightly improved to EF 40–50% in 44 (26.0%) and remained below 40% in 86 (50.9%). Systolic function improvement to EF > 40% was predicted by lower severity of coronary atherosclerosis, lower leukocyte count, and the absence of atrial fibrillation.ConclusionsDespite recent improvements in in-hospital MI care, one in five patients has systolic dysfunction at hospital discharge. Out of these, EF improves in 51%, and full recovery is observed in 23%. The severity of coronary atherosclerosis, inflammatory response to MI, and atrial fibrillation may affect EF recovery.

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“…AF is known to have a negative prognostic impact on CAD patients; 15 yet, it remains unclear if the 7 increased mortality is due to increased coronary ischemic events or from unfavorable hemodynamic effects, such as loss of atrial kick, high ventricular rate, and decreased cardiac output. 16 Several studies have attempted to understand the impact of both diseases. While previous studies have explored the associated poor outcomes, no study has directly examined the pattern of diagnostic workup, intervention, and cardiac outcomes in this sub-population.…”

Section: What Are the Clinical Implicationsmentioning

confidence: 99%

“…AF is known to have a negative prognostic impact on CAD patients; 15 yet, it remains unclear if the increased mortality is due to increased coronary ischemic events or from unfavorable hemodynamic effects, such as loss of atrial kick, high ventricular rate, and decreased cardiac output. 16…”

Section: Introductionmentioning

confidence: 99%

The Trend of Ischemic Evaluation, Intervention, and In-Hospital Mortality and Outcomes among Patients with Acute Myocardial Infarction and Atrial Fibrillation

Baig

2023

Preprint

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BackgroundAtrial fibrillation (AF) is associated with increased cardiovascular mortality. Data regarding the relationship between coronary artery disease (CAD) and AF is mixed. It is uncertain if AF directly increases the risk for future coronary events and if such patients are appropriately evaluated for CAD.MethodsThis is a cross-sectional study performed on hospitalized patients with AMI and concurrent AF in 2019 using National Inpatient Sample from HCUP. Patients with missing information and type II non-ST elevation myocardial infarction (NSTEMI) were excluded. Using STATA 18, In-hospital mortality, ischemic evaluation, percutaneous treatment, rates of ventricular tachycardiac (VT), ventricular fibrillation (VF), cardiogenic shock, cardiac arrest, average length of stay (LOS), and total hospitalization charges were studied. Regression models were used for data analyses.ResultsA total of 600,645 patients met inclusion criteria (219,660 females [36.5%], 428,755 Caucasian [71%], 65,870 African American [10.9%], 51,155 Hispanic [8.5%]; mean [SD] age, 66.7 [0.5] years), including 166,680 (28%) STEMI and 433,965 (72%) NSTEMI patients. 109,520 (18%) patients with AMI had AF. For patients with AMI and AF, the adjusted odds of mortality increased by 23% (adjusted Odds ratio [aOR], 1.23; CI, 1.15-1.32; p<0.001). AF patients were less likely to undergo ischemic evaluation (aOR, 0.77; CI, 0.74-0.80; p<0.001) and ischemic intervention (aOR, 0.64; CI, 0.62-0.66; p<0.001). AF patients had higher odds of VT (aOR, 1.41; CI, 1.33-1.49; p<0.001), VF (aOR, 1.44; CI, 1.33-1.57; p<0.001), cardiogenic shock (aOR, 1.43; CI, 1.35-1.52; p<0.001), and cardiac arrest (aOR, 1.35; CI, 1.24-1.47; p<0.001). AF patients had longer LOS (mean, 1.39; SCD, 1.29-1.48; p<0.001) and higher total hospital charges (mean $22,188; 19,311-25,064, p<0.001).ConclusionAF was independently associated with increased mortality in patients admitted with AMI. AF was associated with higher rates of cardiac complications. Patients with AF were less likely to receive ischemic evaluation or percutaneous intervention and had overall higher healthcare resource utilization. This study encourages AF to be viewed as an independent risk factor for CAD and suggests more efforts to diagnose CAD in such patients.Clinical PerspectiveWhat is newPatients with acute myocardial infarction and atrial fibrillation have higher odds of mortality.AMI patients with AF are subjected to lesser odds of undergoing ischemic evaluation and intervention.Healthcare resource utilization is higher in this cohort.What are the clinical implicationsAF should be considered as an independent risk factor for increased mortality in AMI.Early ischemic evaluation should be considered to diagnose coronary artery disease in this cohort.Increased awareness to recognize all risk factors of coronary artery disease.

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The hemodynamic effect of simulated atrial fibrillation on left ventricular function

Cited by 16 publications

References 17 publications

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients based on multimodal echocardiographic parameters

Trajectories and determinants of left ventricular ejection fraction after the first myocardial infarction in the current era of primary coronary interventions

The Trend of Ischemic Evaluation, Intervention, and In-Hospital Mortality and Outcomes among Patients with Acute Myocardial Infarction and Atrial Fibrillation

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