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Background: Stroke prevention using warfarin is challenging in AF patients with CKD, due to high bleeding risk and difficulties in the INR control. NOACs provide alternative options, but all have greater degrees of renal clearance. This study aimed to compare the outcomes of apixaban, dabigatran, rivaroxaban, and warfarin across the range of kidney function in patients with AF. Methods: Using a US administrative database including private insurance or Medicare Advantage patients with linked claims and laboratory data, we identified 34,569 new users of oral anticoagulants with AF and eGFR ≥15 between 10/1/2010-11/29/2017. Stabilized IPTW balanced four treatment groups on 66 baseline characteristics. The primary outcomes included stroke, major bleeding, and mortality. Weighted Cox proportional hazards models compared treatments in the overall population and in each eGFR subgroup, with mortality as a competing risk for stroke and major bleeding. Results: The proportion of patients using warfarin increased as the kidney function declined - 26.5%, 30.4%, 34.6%, 40.5%, and 55.0% of patients were prescribed warfarin in eGFR ≥90, 60-90, 45-60, 30-45, 15-30 groups, respectively. In comparison to warfarin, apixaban was associated with a lower risk of stroke, major bleeding, and mortality; dabigatran was associated with a similar risk of stroke, and a lower risk of major bleeding and mortality; rivaroxaban was associated with a lower risk of stroke, major bleeding, and mortality (Figure). When comparing one NOAC to another NOAC, apixaban and dabigatran were associated with a lower risk of major bleeding than rivaroxaban (HR 0.61 [0.51-0.73], p<0.001 for apixaban versus rivaroxaban; HR 0.67 [0.50-0.90], p=0.007 for dabigatran versus rivaroxaban); dabigatran was associated with a higher risk of stroke than apixaban (HR 1.65 [1.11-2.46], p=0.01); there was no difference in mortality. There was no significant interaction between treatment and eGFR categories for any outcome, but the number of patients with low eGFR was small. Conclusions: In practice, relative to warfarin, NOACs are progressively less commonly used with increasing degree of renal dysfunction. However, each NOAC was consistently associated with at least equivalent effectiveness and safety compared with warfarin across the range of kidney function.

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.