Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information.

Background: Atrial 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. Methods: Nine 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). Results: A 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. Conclusions: Using 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.

Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information.

Background: Heart 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. Methods: Standardized 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. Results: The 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. Conclusion: In 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.

Mid-wall striae fibrosis predicts heart failure admission, composite heart failure events, and life-threatening arrhythmias in dilated cardiomyopathy.

Heart failure (HF) admission is a dominant contributor to morbidity and healthcare costs in dilated cardiomyopathy (DCM). Mid-wall striae (MWS) fibrosis by late gadolinium enhancement (LGE) imaging has been associated with elevated arrhythmia risk. However, its capacity to predict HF-specific outcomes is poorly defined. We investigated its role to predict HF admission and relevant secondary outcomes in a large cohort of DCM patients. 719 patients referred for LGE MRI assessment of DCM were enrolled and followed for clinical events. Standardized image analyses and interpretations were conducted inclusive of coding the presence and patterns of fibrosis observed by LGE imaging. The primary clinical outcome was hospital admission for decompensated HF. Secondary heart failure and arrhythmic composite endpoints were also studied. Median age was 57 (IQR 47-65) years and median LVEF 40% (IQR 29-47%). Any fibrosis was observed in 228 patients (32%) with MWS fibrosis pattern present in 178 (25%). At a median follow up of 1044 days, 104 (15%) patients experienced the primary outcome, and 127 (18%) the secondary outcome. MWS was associated with a 2.14-fold risk of the primary outcome, 2.15-fold risk of the secondary HF outcome, and 2.23-fold risk of the secondary arrhythmic outcome. Multivariable analysis adjusting for all relevant covariates, inclusive of LVEF, showed patients with MWS fibrosis to experience a 1.65-fold increased risk (95% CI 1.11-2.47) of HF admission and 1-year event rate of 12% versus 7% without this phenotypic marker. Similar findings were observed for the secondary outcomes. Patients with LVEF > 35% plus MWS fibrosis experienced similar event rates to those with LVEF ≤ 35%. MWS fibrosis is a powerful and independent predictor of clinical outcomes in patients with DCM, identifying patients with LVEF > 35% who experience similar event rates to those with LVEF below this conventionally employed high-risk phenotype threshold.

Right ventricular insertion site fibrosis in a dilated cardiomyopathy referral population: phenotypic associations and value for the prediction of heart failure admission or death.

BACKGROUND: Dilated cardiomyopathy (DCM) is increasingly recognized as a heterogenous disease with distinct phenotypes on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging. While mid-wall striae (MWS) fibrosis is a widely recognized phenotypic risk marker, other fibrosis patterns are prevalent but poorly defined. Right ventricular (RV) insertion (RVI) site fibrosis is commonly seen, but without objective criteria has been considered a non-specific finding. In this study we developed objective criteria for RVI fibrosis and studied its clinical relevance in a large cohort of patients with DCM. METHODS: We prospectively enrolled 645 DCM patients referred for LGE-CMR. All underwent standardized imaging protocols and baseline health evaluations. LGE images were blindly scored using objective criteria, inclusive of RVI site and MWS fibrosis. Associations between LGE patterns and CMR-based markers of adverse chamber remodeling were evaluated. Independent associations of LGE fibrosis patterns with the primary composite clinical outcome of heart failure admission or death were determined by multivariable analysis. RESULTS: The mean age was 56 ± 14 (28% female) with a mean left ventricular (LV) ejection fraction (LVEF) of 37%. At a median of 1061 days, 129 patients (20%) experienced the primary outcome. Any abnormal LGE was present in 306 patients (47%), inclusive of 274 (42%) meeting criteria for RVI site fibrosis and 167 (26%) for MWS fibrosis. All with MWS fibrosis showed RVI site fibrosis. Solitary RVI site fibrosis was associated with higher bi-ventricular volumes [LV end-systolic volume index (78 ± 39 vs. 66 ± 33 ml/m2, p = 0.01), RV end-diastolic volume index (94 ± 28 vs. 84 ± 22 ml/m2 (p < 0.01), RV end-systolic volume index (56 ± 26 vs. 45 ± 17 ml/m2, p < 0.01)], lower bi-ventricular function [LVEF 35 ± 12 vs. 39 ± 10% (p < 0.01), RV ejection fraction (RVEF) 43 ± 12 vs. 48 ± 10% (p < 0.01)], and higher extracellular volume (ECV). Patient with solitary RVI site fibrosis experienced a non-significant 1.4-fold risk of the primary outcome, increasing to a significant 2.6-fold risk when accompanied by MWS fibrosis. CONCLUSIONS: RVI site fibrosis in the absence of MWS fibrosis is associated with bi-ventricular remodelling and intermediate risk of heart failure admission or death. Our study findings suggest RVI site fibrosis to be pre-requisite for the incremental development of MWS fibrosis, a more advanced phenotype associated with greater LV remodeling and risk of clinical events.

Right Ventricular Ejection Fraction for the Prediction of Major Adverse Cardiovascular and Heart Failure-Related Events: A Cardiac MRI Based Study of 7131 Patients With Known or Suspected Cardiovascular Disease.

BACKGROUND: There is increasing evidence that right ventricular ejection fraction (RVEF) may provide incremental value to left ventricular (LV) ejection fraction for the prediction of major adverse cardiovascular events. To date, generalizable utility for RVEF quantification in patients with cardiovascular disease has not been established. Using a large prospective clinical outcomes registry, we investigated the prognostic value of RVEF for the prediction of major adverse cardiovascular events- and heart failure-related outcomes. METHODS: Seven thousand one hundred thirty-one consecutive patients with known or suspected cardiovascular disease undergoing cardiovascular magnetic resonance imaging were prospectively enrolled. Multichamber volumetric quantification was performed by standardized operational procedures. Patients were followed for the primary composite outcome of all-cause death, survived cardiac arrest, admission for heart failure, need for transplantation or LV assist device, acute coronary syndrome, need for revascularization, stroke, or transient ischemic attack. A secondary, heart failure focused outcome of heart failure admission, need for transplantation/LV assist device or death was also studied. RESULTS: Mean age was 54±15 years. The mean LV ejection fraction was 55±14% (range 6%-90%) with a mean RVEF of 54±10% (range 9%-87%). At a median follow-up of 908 days, 870 (12%) patients experienced the primary composite outcome and 524 (7%) the secondary outcome. Each 10% drop in RVEF was associated with a 1.3-fold increased risk of the primary outcome (P<0.001) and 1.5-fold increased risk of the secondary outcome (P<0.001). RVEF was an independent predictor following comprehensive covariate adjustment, inclusive of LV ejection fraction. Patients with an RVEF<40% experienced a 3.1-fold risk of the primary outcome (P<0.001) with a 1-year cumulative event rate of 22% versus 7% above this cutoff. CONCLUSIONS: RVEF is a powerful and independent predictor of major adverse cardiac events with broad generalizability across patients with known or suspected cardiovascular disease. These findings support migration towards biventricular phenotyping for the classification of risk in clinical practice. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04367220.

Left Atrial Function Using Cardiovascular Magnetic Resonance Imaging Independently Predicts Life-Threatening Arrhythmias in Patients Referred to Receive a Primary Prevention Implantable Cardioverter Defibrillator.

BACKGROUND: In this study we aimed to investigate left atrial (LA) function, measured from routine cine cardiovascular magnetic resonance imaging, to determine its value for the prediction of sudden cardiac death (SCD) or appropriate implantable cardioverter defibrillator (ICD) shock in patients who received primary prevention ICD implantation. METHODS: We studied 203 patients with ischemic or idiopathic nonischemic dilated cardiomyopathy who underwent cardiovascular magnetic resonance imaging before primary prevention ICD implantation. LA volumes were measured at end-diastole and end-systole from 4- and 2-chamber cine images, and LA emptying function (LAEF) calculated. Patients were followed for the primary composite end point of SCD or appropriate ICD shock. RESULTS: Mean age was 61 ± 12 years with a mean left ventricular ejection fraction of 24 ± 7%. The mean LAEF was 27 ± 15% (range, 0.9%-73%). At a median follow-up of 1639 days, 35 patients (17%) experienced the primary composite outcome. LAEF was strongly associated with the primary outcome (P = 0.001); patients with an LAEF ≤ 30% experienced a cumulative event rate of 26.1% vs 5.7% (hazard ratio, 5.5; P < 0.001) in patients above this cutoff. This finding was maintained in multivariable analysis (hazard ratio, 4.7; P = 0.002) and was consistently shown in the ischemic and nonischemic dilated cardiomyopathy subgroups. CONCLUSIONS: LAEF is a simple, powerful, and independent predictor of SCD in patients being referred for primary prevention ICD implantation.

Objective criteria for septal fibrosis in non-ischemic dilated cardiomyopathy: validation for the prediction of future cardiovascular events.

BACKGROUND: Expert subjective reporting of mid-wall septal fibrosis on late gadolinium enhancement (LGE) images has been shown to predict major cardiovascular outcomes in patients with non-ischemic dilated cardiomyopathy (NIDCM). This study aims to establish objective criteria for non-experts to report clinically relevant septal fibrosis and compare its performance by such readers versus experts for the prediction of cardiovascular events. METHODS: LGE cardiovascular magnetic resonance (CMR) was performed in 118 consecutive patients with NIDCM (mean age 57 ± 14, 42 % female) and the presence of septal fibrosis scored by expert readers. CMR-naive readers performed signal threshold-based LGE quantification by referencing mean values of remote tissue and applying these to a pre-defined anatomic region to measure septal fibrosis. All patients were followed for the primary composite outcome of cardiac mortality or appropriate implantable cardioverter-defibrillator (ICD) therapy. RESULTS: The mean LVEF was 32 ± 12 %. At a median follow-up of 1.9 years, 20 patients (17 %) experienced a primary composite outcome. Expert visual scoring identified 55 patients with septal fibrosis. Non-expert septal fibrosis quantification was highly reproducible and identified mean septal fibrosis burden for three measured thresholds as follows; 5SD: 2.9 ± 3.6 %, 3SD: 6.9 ± 6.3 %, and 2SD: 11.1 ± 7.5 % of the left ventricular (LV) mass, respectively. By ROC analysis, optimal thresholds for prediction of the primary outcome were; 5SD: 2.74 % (HR 8.7, p < 0.001), 3SD: 6.63 % (HR 5.7, p = 0.001) and 2SD: 10.15 % (HR 6.1, p = 0.001). By comparison, expert visual scoring provided a HR of 5.3 (p = 0.001). In adjusted analysis, objective quantification by a novice reader (>5SD threshold) was the strongest independent predictor of the primary outcome (HR 8.7) and provided improved risk reclassification beyond LVEF alone (NRI 0.54, 95 % CI 0.16-0.92, p = 0.005). CONCLUSIONS: Novice readers were able to achieve superior risk prediction for future cardiovascular events versus experts using objective criteria for septal fibrosis in patients with NIDCM. Patients with a septal fibrosis burden >2.74 % of the LV mass (>5SD threshold) were at a 9-fold higher risk of cardiac death or appropriate ICD therapy versus those not meeting this criteria. As such, this study validates reproducible criteria applicable to all levels of expertise to identify NIDCM patients at high risk of future cardiovascular events.