Predictors of post-operative cardiovascular events, focused on atrial fibrillation, after valve surgery for primary mitral regurgitation

Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF.

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

With the rapid development of artificial intelligence (AI) and machine learning (ML), as well as the arrival of the big data era, technological innovations have occurred in the field of cardiovascular medicine. First, the diagnosis of cardiovascular diseases (CVDs) is highly dependent on assistive examinations, the interpretation of which is time consuming and often limited by the knowledge level and clinical experience of doctors; however, AI could be used to automatically interpret the images obtained in auxiliary examinations. Second, some of the predictions of the incidence and prognosis of CVDs are limited in clinical practice by the use of traditional prediction models, but there may be occasions when AI-based prediction models perform well by using ML algorithms. Third, AI has been used to assist precise classification of CVDs by integrating a variety of medical data from patients, which helps better characterize the subgroups of heterogeneous diseases. To help clinicians better understand the applications of AI in CVDs, this review summarizes studies relating to AI-based diagnosis, prediction, and classification of CVDs. Finally, we discuss the challenges of applying AI to cardiovascular medicine.

Noninvasive biomarker-based risk stratification for development of new onset atrial fibrillation after coronary artery bypass surgery

BACKGROUND

Postoperative atrial fibrillation (PoAF) is a common complication after cardiac surgery. A pre-existing atrial substrate appears to be important in postoperative development of dysrhythmia, but its preoperative estimation is challenging. We tested the hypothesis that a combination of clinical predictors, noninvasive surrogate markers for atrial fibrosis defining abnormal left atrial (LA) mechanics, and biomarkers of collagen turnover is superior to clinical predictors alone in identifying patients at-risk for PoAF.

METHODS

In patients without prior AF undergoing coronary artery bypass grafting, concentrations of biomarkers reflecting collagen synthesis and degradation, extracellular matrix, and regulatory microRNA-29s were determined in serum from preoperative blood samples and correlated to atrial fibrosis extent, alteration in atrial deformation properties determined by 3D speckle-tracking echocardiography, and AF development.

RESULTS

Of 90 patients without prior AF, 34 who developed PoAF were older than non-PoAF patients (72.04 ± 10.7 y; P = 0.043) with no significant difference in baseline comorbidities, LA size, or ventricular function. Global (P = 0.007) and regional longitudinal LA strain and ejection fraction (P = 0.01) were reduced in PoAF vs. non-PoAF patients. Preoperative amino-terminal-procollagen-III-peptide (PIIINP) (103.1 ± 39.7 vs. 35.1 ± 19.3; P = 0.041) and carboxy-terminal-procollagen-I-peptide levels were elevated in PoAF vs. non-PoAF patients with a reduction in miR-29 levels and correlated with atrial fibrosis extent. Combining age as the only significant clinical predictor with PIIINP and miR-29a provided a model that identified PoAF patients with higher predictive accuracy.

CONCLUSIONS

In patients without a previous history of AF, using age and biomarkers of collagen synthesis and regulation, a noninvasive tool was developed to identify those at risk for new-onset PoAF.