Citations

Background The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new‐onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non‐POAF. Methods and Results This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non‐POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non‐POAF; 12‐month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non‐POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non‐POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non‐POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87–2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04–1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82–0.85]; P <0.0001) than patients who saw a surgeon or other specialties. Conclusions The use of and adherence to OAC were higher in non‐POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non‐POAF.

Abstract An integral component of the practice of medicine is focused on the initiation of medications, based on clinical practice guidelines and underlying trial evidence, which usually test the addition of novel medications intended for life-long use in short-term clinical trials. Much less attention is given to the question of medication discontinuation, especially after a lengthy period of treatment, during which patients age gets older and diseases may either progress or new diseases may emerge. Given the paucity of data, clinical practice guidelines offer little to no guidance on when and how to deprescribe cardiovascular medications. Such decisions are often left to the discretion of clinicians, who, together with their patients, express concern of potential adverse effects of medication discontinuation. Even in the absence of adverse effects, the continuation of medications without any proven effect may cause harm due to drug–drug interactions, the emergence of polypharmacy, and additional preventable spending to already strained health systems. Herein, several cardiovascular medications or medication classes are discussed that in the opinion of this author group should generally be discontinued, either for the prevention of potential harm, for a lack of benefit, or for the availability of better alternatives.

A 34-year-old man was referred for progressive angina and exertional dyspnoea refractory to medical therapy, with a presumptive diagnosis of hypertrophic cardiomyopathy (HCM). Transthoracic echocardiography (TTE) revealed asymmetric septal hypertrophy without systolic anterior motion of the mitral valve leaflet and with no dynamic left ventricular outflow tract (LVOT) obstruction. However, the LVOT velocity was elevated at rest as well as with provocation, without the characteristic late peaking obstruction seen in HCM. Focused TTE to evaluate for suspected fixed obstruction demonstrated a subaortic membrane 2.2 cm below the aortic valve. Coronary CT angiography confirmed the presence of the subaortic membrane and was negative for concomitant coronary artery disease. Surgical resection of the subaortic membrane and septal myectomy resulted in significant symptomatic relief and lower LVOT velocities on postoperative TTE. This case reminds the clinician to carefully evaluate for alternative causes of LVOT obstruction, especially subaortic membrane, as a cause of symptoms mimicking HCM.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

Introduction: Atrial fibrillation (AF) is a serious complication, which occurs in 30% to 40% of patients after surgical aortic valve replacement (SAVR) and contributes to adverse outcomes. Advanced technology has enabled transcatheter aortic valve replacement (TAVR) in patients with High-risk Severe Aortic Stenosis (HRSAS). In these patients, the compliance of the systemic vasculature is an independent predictor of diastolic dysfunction, a known predictor of postoperative AF (POAF). We hypothesize that patients with HRSAS undergoing TAVR represent a population at increased risk for POAF. METHODS: The medical records of 195 TAVR patients at the Mayo Clinic from November 2008 to January 2013 were analyzed. All patient demographic, clinical, pre- and post-procedural outcomes were collected from the patients’ medical records, and reviewed to ascertain all cases of POAF (AF ≤30 days of surgery). Patients with a prior history of AF (n= 75 (38.5%)) were excluded. Logistic regression analysis was used to identify clinical predictors of POAF after TAVR. Results: The mean age of the study population was 80.8±7.8 years and 60.8% (n=73) were men. The mean STS risk score was 8.9±5.1. Of the 120 patients without prior AF, 20.8% (n=25) developed new-onset POAF after TAVR. Patients who developed POAF were more likely to have undergone a transapical or transaortic TAVR approach (OR 3.20; 95% CI (1.3-8.0), had a higher incidence of renal complications (52.0% vs 21.5%; p= 0.003), greater hospital length of stay (7.4±2.58 vs 5.0±2.6 days; p= 0.0003, and a trend toward higher 30-day mortality (28.0% vs 13.7%; p= 0.09) and pulmonary complications (16.0% vs 5.4%; p= 0.08) compared with those who did not develop POAF, respectively. Neurologic complications were not different between the 2 group (4.0% vs 3.2%; p= 0.85). Conclusions: Patients with High-risk Severe Aortic Stenosis undergoing TAVR have increased risk of POAF, primarily associated with a transapical or aortic surgical approach. Patients who develop POAF had higher incidence of post-procedural complications and longer hospitalizations. Therefore, perioperative strategies aimed at prevention of POAF are as important in TAVR patients as in those undergoing SAVR.

Tomas Konecny, 2012 Finalist and Presenting Author

Introduction Current evidence suggests that abnormal ventriculo-vascular coupling may contribute to the development of atrial fibrillation (AF) by increasing ventricular filling pressures and consequently, left atrial pressure. Recent investigations have also suggested that inflammation might have a role in the pathophysiology of AF. We hypothesize that autoimmune vasculitis plays a significant role in the pathogenetic mechanism of AF. Methods In this longitudinal cohort study, residents of Olmsted County, MN with first- time diagnosed AF during 1980-2000 and a reference age- and sex-matched control group from the same underlying population and time period were identified and followed until death, or 2000. The relationship between autoimmune vasculitis and first documented AF was analyzed. Results Of a total of 8766 patients (mean age 61.1+14.9 years; 56% women), 691 (7.9%) were diagnosed with autoimmune vasculitis during the 20 year period. The prevalence of autoimmune vasculitis in the AF group was 9.9% and 5.9% in the control group; p <0.001, respectively. After adjusting for CAD and LV dysfunction, the odds of autoimmune vasculitis in AF was 1.7 times higher than in controls (OR 1.69, 95%; CI 1.42 - 2.01, p < 0.0001). AF patients with autoimmune vasculitis had a worse survival compared to controls; p=.0114 (log-rank test). Conclusion Autoimmune vasculitis strongly increases the risk of atrial fibrillation and portends shorter survival in the AF population. These observations may represent one mechanism linking increased arterial stiffness and inflammation in the pathogenesis and prognosis of AF as an age-related disorder.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Introduction: Hospitalization for COVID-19 is often associated with myocardial injury (elevated cardiac troponin [cTn]). In the initial pandemic waves, patients with myocardial injury had increased likelihood of cardiovascular complications and inpatient mortality; however, the differences in impact of distinct subtypes of myocardial injury on mortality is unclear. In addition, little is known about how these outcomes compare with the subsequent, milder, Omicron wave. Methods: Utilizing Mayo Clinic enterprise patients we developed algorithms based on diagnostic/procedural codes, cTn levels, and temporal relationships to positive COVID-19 PCR tests, to determine 5 common subtypes of myocardial injury: stress cardiomyopathy, myocarditis, pericarditis, pulmonary embolism, and myocardial infarction. We examined hospitalized adults with COVID-19; those with elevated cTn designated as cases and others as controls. We fitted Cox models using the following covariates: spline transformed age, sex, race, ethnicity, index date and clinical comorbidities. Patients were analyzed based on admission dates coinciding with either the Alpha-Delta or Omicron waves. Results: Across both wave cohorts, hospitalized patients with acute COVID-19 complicated by myocardial injury had an increased risk of mortality compared to those without ( Figure ). Patients with one of the 5 subtypes examined tended to have higher risk than those in the “other myocardial injury” category. Admission during the Omicron wave, compared to the Alpha-Delta waves, tended to have higher mortality risk in all subtypes except myocarditis. Conclusions: Our algorithms successfully identified subgroups of myocardial injury in patients hospitalized with acute COVID-19 leading to stratification of mortality risk. Surprisingly, among hospitalized patients during the Omicron wave, myocardial injury of most subtypes was tended to have increased risk of mortality compared to the Alpha-Delta waves.

Introduction: Recently, an artificial intelligence (AI) algorithm was validated for identifying ECG signatures of atrial fibrillation (AF) risk during normal sinus rhythm. Traditionally, socioeconomically disadvantaged populations are less likely to receive novel or guideline-recommended treatments, and have high chronic disease burden, including cardiovascular diseases. There have also been concerns about new technologies creating a digital divide, worsening existing health disparities. Hypothesis: An AI algorithm will detect AF risk unequally across patients with different levels of social determinants of health (SDOH). Methods: The initial prospective, non-randomized, interventional study leveraged EHR and digital technologies to prospectively enroll 1004 patients with a broad range of AI-ECG risks of AF, who received continuous cardiac rhythm monitoring for up to 30 days. The Area Deprivation Index (ADI) was used as a composite measure of SDOH which is comprised of indicators spanning four domains: income, education, employment, and house quality. 904 intervention patients linked to the ADI data were matched to 904 real-world control patients derived from eligible but unenrolled patients. Those with ADI under 33% were defined as having better SODH; and patients with ADI above 33% were defined as having worse SODH. Results: Median age was 74 years and 38% were women. After matching, the intervention and control arms were balanced in all baseline characteristics, including HF, HTN, diabetes, stroke, CAD and ADI. A hazard ratio of 3.11 (P<0.001) was computed when comparing intervention to control for primary AF diagnosis outcome. In patients with worse SDOH, the intervention increased AF detection from 2.6% to 8.8% (HR 3.63 [2.09, 6.32]); in the better SDOH, the intervention increased AF detection from 3.9% to 9.3% (2.41 [1.23, 4.74]). Conclusions: An AI-guided AF screening program increased the detection of AF in both patients with better or worse SDOH. The current study validates the use of AI in historically disadvantaged populations displaying the possibility for broad implementation with decreased possibility of perpetuating disparities.