Noseworthy, Peter A.

Background: Multimorbidity is common in atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on death and hospitalizations is uncertain. Methods: The prevalence of 17 chronic conditions was obtained in 1430 patients with incident AF from 2000-2010 from Olmsted County, MN. Chronic conditions were ascertained electronically prior to AF and were classified into 3 groups: cardiovascular (CV), other physical, and mental. All hospitalizations and deaths were obtained through 12/31/2014. Associations of the number and type of condition and each individual condition with hospitalizations and death were determined using Andersen-Gill and Cox regression, respectively. Results: Among the 1430 patients with AF (median age 76 years, 48.6% men), the mean (SD) number of chronic conditions was 4.7 (2.7). A total of 4496 hospitalizations and 812 deaths occurred over a mean (SD) follow-up of 5.4 (4.1) years. An increase in the number of conditions of each type was associated with increased risks of death and hospitalizations (Table). Mental conditions (which included depression, anxiety, dementia, schizophrenia, and substance abuse) exhibited the strongest association with death, whereas other physical conditions exhibited the strongest association with hospitalizations. Individual predictors of death included heart failure (HF), hypertension, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), depression, dementia, and substance abuse. Predictors of hospitalizations included HF, coronary artery disease, diabetes, COPD, CKD, arthritis, osteoporosis, and substance abuse. Conclusions: Patients with AF have many comorbid conditions which are associated with death and hospitalizations. Although conditions of each type were associated with outcomes, mental conditions exhibited strongest association with death. These data highlight the importance of management of not only CV comorbidities but other physical and mental comorbidities in patients with AF.

AbstractObjectivesWe sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort.BackgroundPatients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.MethodsWe applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.”ResultsAmong 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.”ConclusionsA deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.

AbstractBackgroundImplantable loop recorders (ILRs) are effective in achieving symptom‐rhythm correlation. However, diagnostic yield in routine clinical practice is not well established.MethodsPatients undergoing ILR implantation between April 2010 and May 2015 were included. All devices were enrolled in remote monitoring with automatic arrhythmia detection and P sense algorithms switched “ON.” Symptom‐rhythm correlation was assessed and changes in management were recorded.ResultsA total of 312 patients (57% male, age 53 ± 22 years; median CHADS2VaSc score = 1) were included in this study. ILRs were implanted for evaluation of syncope in 206 (66.0%), presyncope in 23 (7.4%), unexplained palpitations in 51 (16.3%), and cryptogenic stroke in 27 (8.7%) patients. ILR monitoring yielded a diagnosis that changed management strategy in 146 (46.8%) patients over a median of 12 (1‐42) months. Out of 163 (52.2%) patients with symptoms during the monitoring period, 100 (61.3%) had an arrhythmia. ILR was useful in ruling out an arrhythmic cause for symptoms in 63 (38.7%) patients. ILR results led to pacemaker implantation in 23 patients (7.4% overall and 11.2% of those with syncope) after median follow‐up of 3 months. A new diagnosis of atrial fibrillation was made in 38 (12.2%) patients, 11 of whom were initiated on oral anticoagulants. ILR results led to pacemaker implantation in 31 patients (9.9% overall and 19.0% of those with syncope) after median follow‐up of 3 months. A new diagnosis of atrial fibrillation was made in 38 (12.2%) patients, nine of whom were initiated on oral anticoagulants. Overall, ILR led to a change in management in 47% patients with a number needed to implant of 2.1 to change management.ConclusionILR monitoring is effective in achieving symptom‐rhythm correlation and results in changes in management in nearly half of implanted patients. Additional studies are needed to evaluate cost efficacy of ILR and the optimal monitoring duration.