Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.

OBJECTIVES: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. MATERIALS AND METHODS: Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists. RESULTS: The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations. CONCLUSION: The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

BACKGROUND: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS: Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes.

BACKGROUND: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG). OBJECTIVES: This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs. METHODS: We used electronic medical records from 5 hospitals and identified ECGs from adults with documented PVCs. Internal training and testing were performed at one hospital. External validation was performed with the others. The primary outcome was first diagnosis of LVEF ≤40% within 6 months. The dataset included 383,514 ECGs, of which 14,241 remained for analysis. We analyzed area under the receiver operating curves and explainability plots for representative patients, algorithm prediction, PVC burden, and demographics in a multivariable Cox model to assess independent predictors for cardiomyopathy. RESULTS: Among the 14,241-patient cohort (age 67.6 ± 14.8 years; female 43.8%; White 29.5%, Black 8.6%, Hispanic 6.5%, Asian 2.2%), 22.9% experienced reductions in LVEF to ≤40% within 6 months. The model predicted reductions in LVEF to ≤40% with area under the receiver operating curve of 0.79 (95% CI: 0.77-0.81). The gradient weighted class activation map explainability framework highlighted the sinus rhythm QRS complex-ST segment. In patients who underwent successful PVC ablation there was a post-ablation improvement in LVEF with resolution of cardiomyopathy in most (89%) patients. CONCLUSIONS: Deep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden. Model prediction performed well across sex and race, relying on the QRS complex/ST-segment in sinus rhythm, not PVC morphology.

A foundational vision transformer improves diagnostic performance for electrocardiograms.

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions.

Interatrial Block Association With Adverse Cardiovascular Outcomes in Patients Without a History of Atrial Fibrillation.

BACKGROUND: Interatrial block (IAB) is associated with thromboembolism and atrial arrhythmias. However, prior studies included small patient cohorts so it remains unclear whether IAB predicts adverse outcomes particularly in context of atrial fibrillation (AF)/atrial flutter (AFL). OBJECTIVES: This study sought to determine whether IAB portends increased stroke risk in a large cohort in the presence or absence of AFAF/AFL. METHODS: We performed a 5-center retrospective analysis of 4,837,989 electrocardiograms (ECGs) from 1,228,291 patients. IAB was defined as P-wave duration ≥120 ms in leads II, III, or aVF. Measurements were extracted as .XML files. After excluding patients with prior AF/AFL, 1,825,958 ECGs from 458,994 patients remained. Outcomes were analyzed using restricted mean survival time analysis and restricted mean time lost. RESULTS: There were 86,317 patients with IAB and 355,032 patients without IAB. IAB prevalence in the cohort was 19.6% and was most common in Black (26.1%), White (20.9%), and Hispanic (18.5%) patients and least prevalent in Native Americans (9.2%). IAB was independently associated with increased stroke probability (restricted mean time lost ratio coefficient [RMTLRC]: 1.43; 95% CI: 1.35-1.51; tau = 1,895), mortality (RMTLRC: 1.14; 95% CI: 1.07-1.21; tau = 1,924), heart failure (RMTLRC: 1.94; 95% CI: 1.83-2.04; tau = 1,921), systemic thromboembolism (RMTLRC: 1.62; 95% CI: 1.53-1.71; tau = 1,897), and incident AF/AFL (RMTLRC: 1.16; 95% CI: 1.10-1.22; tau = 1,888). IAB was not associated with stroke in patients with pre-existing AF/AFL. CONCLUSIONS: IAB is independently associated with stroke in patients with no history of AF/AFL even after adjustment for incident AF/AFL and CHA2DS2-VASc score. Patients are at increased risk of stroke even when AF/AFL is not identified.

Quantitative prediction of right ventricular and size and function from the electrocardiogram.

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored. Methods: We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m2), RV dysfunction (RVEF≤40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938). We fine-tuned in a multi-center health system (MSHoriginal; n=3,019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance using area under the receiver operating curve (AUROC) for categorical and mean absolute error (MAE) for continuous measures overall and in key subgroups. We assessed association of RVEF prediction with transplant-free survival with Cox proportional hazards models. Results: Prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. Prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts 0.91/0.81/0.92, respectively. MSHoriginal MAE was RVEF=7.8% and RVEDV=17.6 ml/m2. Performance was similar in key subgroups including with and without left ventricular dysfunction. Over median follow-up of 2.3 years, predicted RVEF was independently associated with composite outcome (HR 1.37 for each 10% decrease, p=0.046). Conclusions: DL-ECG analysis can accurately identify significant RV dysfunction and dilation both overall and in key subgroups. Predicted RVEF is independently associated with clinical outcome.

Safely Shaping the Breast After Implant Removal and Total Intact Capsulectomy Using the Mammary Imbrication Lift and Fixation Technique.

Background: Implant-based breast augmentation is one of the most popular plastic surgery procedures performed worldwide. As the number of patients who have breast implants continues to rise, so does the number of those who request breast implant removal without replacement. There is little in the current scientific literature describing total intact capsulectomy and simultaneous mastopexy procedures. Objectives: Here, the authors present their current method using the mammary imbrication lift and fixation technique after explant and total capsulectomy. Methods: Between 2016 and 2021, a total of 64 patients (mean age: 42.95 years; range, 27-78 years) underwent the described mammary imbrication lift and fixation technique with bilateral breast implant removal and total capsulectomy. Results: Mean follow-up was 6.5 months (range, 1-36 months). Postoperative complications included minor cellulitis in 1 patient (1.6%), late onset hematoma with infection in 1 patient (1.6%), fat necrosis and pulmonary embolism in 1 patient with prior history of thromboembolic events (1.6%), and breast scar irregularity in 4 patients (6.2%) who required subsequent minor scar revision or steroid injections. Two patients (1.6%) underwent revision surgery with bilateral breast fat grafting to improve shape and add volume. Conclusions: The mammary imbrication lift and fixation technique described here can safely and simultaneously be performed with a total intact capsulectomy and explant procedure. This technique avoids wide undermining, intentionally opening the capsule, performing subtotal capsulectomy, and preserving blood supply to the breast tissue and nipple with low complication rates.

Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction.

BACKGROUND: Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS: In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS: We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS: Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.

The valves of the heart have flaps that open and close when the heart beats to maintain the flow of blood in the correct direction. Valvular disease, such as backflow or narrowing, puts additional strain upon heart muscles which can lead to heart failure. Usually, these conditions are diagnosed by doing an echocardiogram, an ultrasound scan of the heart and nearby blood vessels. The electrocardiogram (ECG) records the electrical signal generated by the heart and can be obtained more easily. We used deep learning neural networks, self-learning computer algorithms which excel at finding patterns within complex data. This enabled us to develop computer software able to diagnose valvular disease from ECGs. Earlier detection of such disease can help in improving overall outcome, while also reducing costs related to treatment.

Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation.

Background: Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties. Objective: For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach). Results: We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness. Conclusion: We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.

Cardiovascular Complications of Interatrial Conduction Block: JACC State-of-the-Art Review.

Interatrial block (IAB) is an electrocardiographic pattern describing the conduction delay between the right and left atria. IAB is classified into 3 degrees of block that correspond to decreasing conduction in the region of Bachmann's bundle. Although initially considered benign in nature, specific subsets of IAB have been associated with atrial arrhythmias, elevated thromboembolic stroke risk, cognitive impairment, and mortality. As the pathophysiologic relationships between IAB and stroke are reinforced, investigation has now turned to the potential benefit of early detection, atrial imaging, cardiovascular risk factor modification, antiarrhythmic pharmacotherapy, and stroke prevention with oral anticoagulation. This review provides a contemporary overview of the epidemiology, pathophysiology, diagnosis, and management of IAB, with a focus on future directions.

Prognostic Value of Electrocardiographic QRS Diminution in Patients Hospitalized With COVID-19 or Influenza.

During the clinical care of hospitalized patients with COVID-19, diminished QRS amplitude on the surface electrocardiogram (ECG) was observed to precede clinical decompensation, culminating in death. This prompted investigation into the prognostic utility and specificity of low QRS complex amplitude (LoQRS) in COVID-19. We retrospectively analyzed consecutive adults admitted to a telemetry service with SARS-CoV-2 (n = 140) or influenza (n = 281) infection with a final disposition-death or discharge. LoQRS was defined as a composite of QRS amplitude <5 mm or <10 mm in the limb or precordial leads, respectively, or a ≥50% decrease in QRS amplitude on follow-up ECG during hospitalization. LoQRS was more prevalent in patients with COVID-19 than influenza (24.3% vs 11.7%, p = 0.001), and in patients who died than survived with either COVID-19 (48.1% vs 10.2%, p <0.001) or influenza (38.9% vs 9.9%, p <0.001). LoQRS was independently associated with mortality in patients with COVID-19 when adjusted for baseline clinical variables (odds ratio [OR] 11.5, 95% confidence interval [CI] 3.9 to 33.8, p <0.001), presenting and peak troponin, D-dimer, C-reactive protein, albumin, intubation, and vasopressor requirement (OR 13.8, 95% CI 1.3 to 145.5, p = 0.029). The median time to death in COVID-19 from the first ECG with LoQRS was 52 hours (interquartile range 18 to 130). Dynamic QRS amplitude diminution is a strong independent predictor of death over not only the course of COVID-19 infection, but also influenza infection. In conclusion, this finding may serve as a pragmatic prognostication tool reflecting evolving clinical changes during hospitalization, over a potentially actionable time interval for clinical reassessment.

Malignant Arrhythmias in Patients With COVID-19: Incidence, Mechanisms, and Outcomes.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) who develop cardiac injury are reported to experience higher rates of malignant cardiac arrhythmias. However, little is known about these arrhythmias-their frequency, the underlying mechanisms, and their impact on mortality. METHODS: We extracted data from a registry (NCT04358029) regarding consecutive inpatients with confirmed COVID-19 who were receiving continuous telemetric ECG monitoring and had a definitive disposition of hospital discharge or death. Between patients who died versus discharged, we compared a primary composite end point of cardiac arrest from ventricular tachycardia/fibrillation or bradyarrhythmias such as atrioventricular block. RESULTS: Among 800 patients with COVID-19 at Mount Sinai Hospital with definitive dispositions, 140 patients had telemetric monitoring, and either died (52) or were discharged (88). The median (interquartile range) age was 61 years (48-74); 73% men; and ethnicity was White in 34%. Comorbidities included hypertension in 61%, coronary artery disease in 25%, ventricular arrhythmia history in 1.4%, and no significant comorbidities in 16%. Compared with discharged patients, those who died had elevated peak troponin I levels (0.27 versus 0.02 ng/mL) and more primary end point events (17% versus 4%, P=0.01)-a difference driven by tachyarrhythmias. Fatal tachyarrhythmias invariably occurred in the presence of severe metabolic imbalance, while atrioventricular block was largely an independent primary event. CONCLUSIONS: Hospitalized patients with COVID-19 who die experience malignant cardiac arrhythmias more often than those surviving to discharge. However, these events represent a minority of cardiovascular deaths, and ventricular tachyarrhythmias are mainly associated with severe metabolic derangement. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04358029.

Syncope and presyncope in patients with COVID-19.

INTRODUCTION: Recent studies have described several cardiovascular manifestations of COVID-19 including myocardial ischemia, myocarditis, thromboembolism, and malignant arrhythmias. However, to our knowledge, syncope in COVID-19 patients has not been systematically evaluated. We sought to characterize syncope and/or presyncope in COVID-19. METHODS: This is a retrospective analysis of consecutive patients hospitalized with laboratory-confirmed COVID-19 with either syncope or presyncope. This "study" group (n = 37) was compared with an age and gender-matched cohort of patients without syncope ("control") (n = 40). Syncope was attributed to various categories. We compared telemetry data, treatments received, and clinical outcomes between the two groups. RESULTS: Among 1000 COVID-19 patients admitted to the Mount Sinai Hospital, the incidence of syncope/presyncope was 3.7%. The median age of the entire cohort was 69 years (range 26-89+ years) and 55% were men. Major comorbidities included hypertension, diabetes, and coronary artery disease. Syncopal episodes were categorized as (a) unspecified in 59.4% patients, (b) neurocardiogenic in 15.6% patients, (c) hypotensive in 12.5% patients, and (d) cardiopulmonary in 3.1% patients with fall versus syncope and seizure versus syncope in 2 of 32 (6.3%) and 1 of 33 (3.1%) patients, respectively. Compared with the "control" group, there were no significant differences in both admission and peak blood levels of d-dimer, troponin-I, and CRP in the "study" group. Additionally, there were no differences in arrhythmias or death between both groups. CONCLUSIONS: Syncope/presyncope in patients hospitalized with COVID-19 is uncommon and is infrequently associated with a cardiac etiology or associated with adverse outcomes compared to those who do not present with these symptoms.

Urologic Complications in Patients Receiving Indwelling Urinary Catheters During Transcatheter Aortic Valve Replacement.

OBJECTIVES: The minimalist approach to transcatheter aortic valve replacement (TAVR) focuses on avoiding extraneous invasive measures. Data describing the clinical impact of routine indwelling urinary catheter (IUC) in TAVR patients is limited. We sought to examine outcomes after IUC placement in patients undergoing TAVR. METHODS: We performed a retrospective analysis of 773 consecutive patients undergoing TAVR between 2011 and 2015. Patients were excluded who did not receive an IUC, had a pre-existing IUC, had renal replacement therapy, or underwent non-transfemoral TAVR. Patients were classified by presence of the composite of in-hospital urologic adverse events (UAEs), defined as urinary retention, IUC reinsertion, discharge with IUC, new hematuria, or urinary tract infection (UTI). The primary study endpoint was all-cause mortality at 1 year. RESULTS: A total of 520 patients met study criteria and were analyzed. The incidence of UAE was 28.6%. Urinary retention after IUC removal occurred in 14.6% of patients. UTIs occurred in 6.5% and acute kidney injury occurred in 13.6% of IUC patients. UAE was associated with an increased rate of 30-day and 1-year all-cause mortality (hazard ratio [HR], 2.84; 95% confidence interval [CI], 1.09-7.35; P=.02 and HR, 1.96; 95% CI, 1.22-3.16; P<.01, respectively). After multivariable adjustment for important baseline differences, UAEs were associated with significantly greater hazard of 1-year mortality (adjusted HR, 1.79; 95% CI, 1.07-2.99; P=.03) but not 30-day mortality (adjusted HR, 1.96; 95% CI, 0.67-5.49; P=.22). CONCLUSION: UAEs were frequent in patients receiving an IUC during TAVR and were associated with substantial morbidity, as well as longer hospital stay. Further research is required to establish whether avoidance of IUC as a component of the minimalist approach will reduce UAEs.

Cardiogenic Shock and Mitral Valve Chord Rupture: A Rare Presentation of Libman-Sacks Endocarditis.

Distinguishing Libman-Sacks endocarditis from other valvular heart disease etiologies has important implications for management. We present a case of a 23-year-old man who presented in extremis with fever and cardiogenic shock caused by Libman-Sacks endocarditis with associated mitral valve chord rupture. (Level of Difficulty: Beginner.).

Impact of Beta-Blockade on Cardiac Events in Patients with Chronic Severe Nonischemic Mitral Regurgitation.

OBJECTIVES: The aim of this study was to examine the impact of beta-blockade on cardiac events among patients with initially asymptomatic chronic severe nonischemic mitral valve regurgitation (MR). METHODS: Data from 52 consecutive patients in our prospective natural history study of isolated chronic severe nonischemic MR were assessed post hoc over 19 years to examine the relation of chronic beta-blockade use to subsequent cardiac events (death or indications for mitral valve surgery, MVS). At entry, all patients were free of surgical indications; 9 received beta-blockers. Cardiac event rate differences were analyzed by Kaplan-Meier log rank comparison. RESULTS: During follow-up, cardiac events included sudden death (1), heart failure (8), atrial fibrillation (6), left ventricular dimensions at systole ≥4.5 cm (11), left ventricular ejection fraction <60% (6), right ventricular ejection fraction <35% (2), and a combination of cardiac events (7). The cardiac event risk was 4-fold higher among patients receiving beta-blockers (average annual risk = 60.6%) versus those not receiving beta-blockers (average annual risk = 15.2%; p = 0.001). These effects remained statistically significant (p = 0.005) when analysis was adjusted for other baseline covariates. CONCLUSIONS: Beta-blockade appears to confer an increased risk of sudden cardiac death or indications for MVS among patients with chronic severe nonischemic MR. Randomized trials are needed to confirm these findings.