Search

BackgroundAtrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning.MethodsNine thousand four hundred forty-eight patients referred for CMR imaging were enrolled and followed over a 5-year period. Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival forests (RSFs) were used to predict new-onset AF and compared to Cox proportional-hazard (CPH) models. The best performing features were identified from 115 variables sourced from three data domains: (i) CMR-based disease phenotype, (ii) patient health questionnaire, and (iii) electronic health records. We evaluated discriminative performance of optimized models using C-index and time-dependent AUC (tAUC).ResultsA RSF-based model of 20 variables (CIROC-AF-20) delivered an overall C-index of 0.78 for the prediction of new-onset AF with respective tAUCs of 0.80, 0.79, and 0.78 at 1-, 2- and 3-years. This outperformed a novel CPH-based model and historic AF risk scores. At 1-year of follow-up, validation cohort patients classified as high-risk of future AF by CIROC-AF-20 went on to experience a 17.3% incidence of new-onset AF, being 24.7-fold higher risk than low risk patients.ConclusionsUsing phenotypic data available at time of CMR imaging we developed and validated the first described risk model for the prediction of new-onset AF in patients with cardiovascular disease. Complementary value was provided by variables from patient-reported measures of health and the electronic health record, illustrating the value of multi-domain phenotypic data for the prediction of AF.

BackgroundPulmonary vein isolation (PVI) is a commonly engaged therapy for symptomatic atrial fibrillation (AF). Prior studies have documented elevated AF recurrence rates among females vs. males. Sex-specific mechanisms underlying this phenomenon are poorly understood. This prospective cohort study aimed to evaluate the sex-based differences in cardiac phenotype and their influence on (AF) recurrence following first-time PVI.MethodsA total of 204 consecutive patients referred for first-time PVI and 101 healthy subjects were prospectively studied by cardiovascular magnetic resonance (CMR) imaging. Multi-chamber volumetric and functional measures were assessed by sex-corrected Z-score analyses vs. healthy subjects. Patients were followed for a median of 2.6 years for the primary outcome of clinical AF recurrence. Multivariable analyses adjusting for age and comorbidities were performed to identify independent predictors of AF recurrence.ResultsAF recurrence following first PVI occurred in 41% of males and 59% of females (p = 0.03). Females were older with higher prevalence of hypertension and thyroid disorders. Z-score-based analyses revealed significantly reduced ventricular volumes, greater left atrial (LA) volumes, and reduced LA contractility in females vs. males. Multivariable analysis revealed each of LA minimum and pre-systolic volumes and booster EF Z-scores to be independently associated with AF recurrence, providing respective hazard ratios of 1.10, 1.19, and 0.89 (p = 0.001, 0.03, and 0.01).ConclusionAmong patients referred for first time PVI, females were older and demonstrated significantly poorer LA contractile health vs. males, the latter independently associated with AF recurrence. Assessment of LA contractile health may therefore be of value to identify female patients at elevated risk of AF recurrence. Factors influencing female patient referral for PVI at more advanced stages of atrial disease warrant focused investigation.

BackgroundHeart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information.MethodsStandardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization.ResultsThe mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59–4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group.ConclusionIn this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.