Noseworthy, Peter A.

Abstract Objective The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation. Materials and Methods We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses. Results We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model. Conclusions We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions.

Background: Multimorbidity is common in patients with atrial fibrillation (AF), yet comorbidity patterns are not well documented. Methods: The prevalence of 18 chronic conditions was obtained in patients with AF from 2013-2017 from a 27-county region in the Midwest. All patients had AF documented on an electrocardiogram or Holter monitor. To compare comorbidity patterns between patients with and without AF, controls were matched 1:1 on age (± 5 years) and sex. For AF patients and controls separately, the Somers’ D statistic was used to quantify the non-random concordance of each pair of chronic conditions beyond chance (ranging from -1 [perfect disagreement] to 1 [perfect agreement]; 0 indicating coincidental agreement by chance alone). Significance of the Somers’ D statistic was evaluated using a permutation approach with the Benjamini-Hochberg procedure for controlling the false discovery rate at 0.1%. Results: Among the 16,543 patients with AF (median age 76 years, 57% men), the most common conditions were hypertension (70%), hyperlipidemia (61%), arthritis (41%), and diabetes (40%). Many pairs of chronic conditions co-occurred more frequently than expected. However, no material differences were observed in the co-occurrence of conditions in patients with AF compared to controls (data not shown). Among patients with AF, the co-occurrence of conditions differed by age (Figure). In those ≤65 years, the strongest co-occurrence of conditions was depression/anxiety, although several condition pairs had Somers’ D statistics of >0.4. In those aged 65-74 years, hypertension/hyperlipidemia and depression/anxiety had the strongest co-occurrence, whereas hypertension/hyperlipidemia was strongest for those aged ≥75 years. Conclusion: Although differences in the co-occurrence of conditions did not exist between AF and community controls, among the patients with AF, the patterns of co-existing conditions differed by age. A better understanding of the clinical consequences of multimorbidity in AF patients is needed.

Introduction: Recent studies showed post-operative atrial fibrillation (POAF) is associated with increased stroke risk - even approaching that of unprovoked AF. This raises the question of whether patients with POAF should receive anticoagulation. We aim to compare different anticoagulation strategies in patients with POAF after cardiac and non-cardiac surgeries. Methods: Using a large national administrative database (OptumLabs Data Warehouse), we identified 33,981 patients with POAF between 1/1/2013-9/30/2019. Patients were divided to four groups based on the initial treatment within 30 days of POAF: (1) no anticoagulation (N=28,807) (2) apixaban (N=2178) (3) rivaroxaban (N=1320) (4) warfarin (N=1676). Follow up started at the 30 th day after POAF to exclude patients who died shortly after surgery and minimize immortal time bias. The follow up continued until patients changed treatment strategies, insurance, or died. 1:1 propensity score matching was performed between each anticoagulant to no treatment to balance on 86 baseline characteristics. Results: The mean age was 71.1±11.2 yr; mean CHA 2 DS 2 -VASc was 4.1±1.8; 29.1% received cardiac surgeries; 15.2% received anticoagulation within 30 days of POAF. The event rate per 100 person years was 1.58 for stroke in no-anticoagulation group, and 1.29 for anticoagulation group; 2.86 for major bleeding in no-anticoagulation group and 4.50 for anticoagulation group. After matching, apixaban and rivaroxaban were associated with a lower risk of stroke, but a higher risk of major bleeding; warfarin was not associated with a lower risk of stroke, but a higher risk of major bleeding (Figure). The effect was consistent across subgroups, e.g., age and surgery type. Conclusions: In patients with POAF, the stroke rate was lower than what was typically observed in clinical AF. Treatment with apixaban and rivaroxaban immediately after POAF was associated with a reduced risk of stroke but an increased risk of major bleeding.

Introduction: Watchman has been increasingly used in AF patients who have difficulties taking anticoagulation, but the RCTs only compared Watchman to warfarin. There was also a signal of increased ischemic stroke with Watchman in comparison to warfarin in RCTs, which made some clinicians question the fundamental premise that Watchman can reduce stroke. We aimed to compare Watchman to no anticoagulation in a large national cohort of patients in the US. Methods: We identified AF patients treated with Watchman (N=7,309) or no anticoagulation (N=1,234,543) between 2015-2017 from the OptumLabs Data Warehouse, which is the first to link private insurance, Medicare Advantage, and Medicare Fee-for-Service records at patient level longitudinally. One-to-one propensity score matching was used to balance patients on demographics and medical history. The primary outcomes were ischemic stroke or systemic embolism (hereafter referred to as stroke) and major bleeding. Secondary outcomes included all-cause mortality and intracranial bleeding. Results: In the unmatched cohort, the mean age was 76.6±6.4 yr in the Watchman group and 76.6 ±8.5 yr in the no anticoagulation group, but Watchman patients were more likely to have a history of bleeding (44.9% vs 13.7%) and intracranial bleeding (9.5% vs 2.6%). After matching, the two groups were similar on 18 baseline characteristics. Watchman was associated with a lower risk of stroke and mortality, but a higher risk of major bleeding and a similar risk of intracranial bleeding. The associations were largely consistent across subgroups, including those with relative contraindications to anticoagulation (Figure). Conclusions: In AF patients who had difficulties taking anticoagulation, Watchman offers an alternative approach to reduce stroke and mortality. The results provide important evidence to inform clinical decision making in the absence of RCTs, since 40% of AF patients in routine practice are not on anticoagulation.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background and Purpose: Cerebral microbleeds (CMBs) are represented by small areas of hemosiderin deposition, detected on brain magnetic resonance imaging (MRI), and found in ≈23% of the cognitively normal population over age of 60 years. CMBs predict risk of hemorrhagic and ischemic stroke. They correlate with increased cardiovascular mortality. In this article, we sought to determine in a population-based study whether antithrombotic medications correlate with CMBs and, if present, whether the association was direct or mediated by another variable. Methods: The study consisted of 1253 participants from the population-based Mayo Clinic Study of Aging who underwent T2* gradient-recalled echo magnetic resonance imaging. We tested the relationship between antithrombotic medications and CMB presence and location, using multivariable logistic-regression models. Ordinal logistic models tested the relationship between antithrombotics and CMB frequency. Using structural equation models, we assessed the effect of antithrombotic medications on presence/absence of CMBs and count of CMBs in the CMB-positive group, after considering the effects of age, sex, vascular risk factors, amyloid load by positron emission tomography, and apoE. Results: Two hundred ninety-five participants (26.3%) had CMBs. Among 678 participants taking only antiplatelet medications, 185 (27.3%) had CMBs. Among 95 participants taking only an anticoagulant, 43 (45.3%) had CMBs. Among 44 participants taking an anticoagulant and antiplatelet therapy, 21 (48.8%) had CMBs. Anticoagulants correlated with the presence and frequency of CMBs, whereas antiplatelet agents were not. Structural equation models showed that predictors for presence/absence of CMBs included older age at magnetic resonance imaging, male sex, and anticoagulant use. Predictors of CMB count in the CMB-positive group were male sex and amyloid load. Conclusions: Anticoagulant use correlated with presence of CMBs in the general population. Amyloid positron emission tomography correlated with the count of CMBs in the CMB-positive group.

Objective Shared decision making (SDM) tools can help implement guideline recommendations for patients with atrial fibrillation (AF) considering stroke prevention strategies. We sought to characterize all available SDM tools for this purpose and examine their quality and clinical impact. Methods We searched through multiple bibliographic databases, social media, and an SDM tool repository from inception to May 2020 and contacted authors of identified SDM tools. Eligible tools had to offer information about warfarin and ≥1 direct oral anticoagulant. We extracted tool characteristics, assessed their adherence to the International Patient Decision Aids Standards, and obtained information about their efficacy in promoting SDM. Results We found 14 SDM tools. Most tools provided up-to-date information about the options, but very few included practical considerations (e.g., out-of-pocket cost). Five of these SDM tools, all used by patients prior to the encounter, were tested in trials at high risk of bias and were found to produce small improvements in patient knowledge and reductions in decisional conflict. Conclusion Several SDM tools for stroke prevention in AF are available, but whether they promote high-quality SDM is yet to be known. The implementation of guidelines for SDM in this context requires user-centered development and evaluation of SDM tools that can effectively promote high-quality SDM and improve stroke prevention in patients with AF.

Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P < 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.