Little is known about the prescribing of medications with potential to cause QTc-prolongation in the ambulatory care settings. Understanding real-world prescribing of QTc-prolonging medications and actions taken to mitigate this risk will help guide strategies to optimize safety and appropriate prescribing among ambulatory patients.
Despite the numerous studies evaluating various rhythm control strategies for atrial fibrillation (AF), determination of the optimal strategy in a single patient is often based on trial and error, with no one-size-fits-all approach based on international guidelines/recommendations. The decision, therefore, remains personal and lends itself well to help from a clinical decision support system, specifically one guided by artificial intelligence (AI). QRhythm utilizes a 2-stage machine learning (ML) model to identify the optimal rhythm management strategy in a given patient based on a set of clinical factors, in which the model first uses supervised learning to predict the actions of an expert clinician and identifies the best strategy through reinforcement learning to obtain the best clinical outcome-a composite of symptomatic recurrence, hospitalization, and stroke.We qualitatively evaluated a novel, AI-based, clinical decision support system (CDSS) for AF rhythm management, called QRhythm, which uses both supervised and reinforcement learning to recommend either a rate control or one of 3 types of rhythm control strategies-external cardioversion, antiarrhythmic medication, or ablation-based on individual patient characteristics.Thirty-three clinicians, including cardiology attendings and fellows and internal medicine attendings and residents, performed an assessment of QRhythm, followed by a survey to assess relative comfort with automated CDSS in rhythm management and to examine areas for future development.The 33 providers were surveyed with training levels ranging from resident to fellow to attending. Of the characteristics of the app surveyed, safety was most important to providers, with an average importance rating of 4.7 out of 5 (SD 0.72). This priority was followed by clinical integrity (a desire for the advice provided to make clinical sense; importance rating 4.5, SD 0.9), backward interpretability (transparency in the population used to create the algorithm; importance rating 4.3, SD 0.65), transparency of the algorithm (reasoning underlying the decisions made; importance rating 4.3, SD 0.88), and provider autonomy (the ability to challenge the decisions made by the model; importance rating 3.85, SD 0.83). Providers who used the app ranked the integrity of recommendations as their highest concern with ongoing clinical use of the model, followed by efficacy of the application and patient data security. Trust in the app varied; 1 (17%) provider responded that they somewhat disagreed with the statement, "I trust the recommendations provided by the QRhythm app," 2 (33%) providers responded with neutrality to the statement, and 3 (50%) somewhat agreed with the statement.Safety of ML applications was the highest priority of the providers surveyed, and trust of such models remains varied. Widespread clinical acceptance of ML in health care is dependent on how much providers trust the algorithms. Building this trust involves ensuring transparency and interpretability of the model.
Objective: Nonvasodilatory beta blockers are associated with inferior cardiovascular event reduction compared with other antihypertensive classes, and there is uncertainty about first-line use of beta blockers for hypertension in guidelines. The third generation vasodilatory beta blocker nebivolol has unique beneficial effects on central and peripheral vasculature. Our objective was to compare longitudinal cardiovascular outcomes of hypertensive patients taking nebivolol with those taking the nonvasodilatory beta blockers metoprolol and atenolol. Methods: We performed a retrospective cohort analysis of hypertensive adults in the University of Colorado health system, without preceding diagnosis of cardiovascular or cerebrovascular disease. The primary outcome was composite incident heart failure, stroke, myocardial infarction, angina, or coronary revascularization. Mahalanobis 1:2 distance matching and Cox proportional hazards regression was used. Matching and regression variables included baseline demographics, socioeconomic factors, medical insurance type, prescribing provider type, cardiovascular risk factors, Charlson comorbidity index, other medications, and follow-up duration. Results: After matching, patients were predominantly women (54%, 3085 of 5705) and non-Hispanic Caucasian (79%, 4534 of 5705), with median age of 58. In matched Cox regression analysis, nebivolol was associated with 17% reduction in incident cardiovascular events compared with all nonvasodilatory beta blockers [hazard ratio 0.83, 95% confidence interval (CI) 0.74–0.94, P = 0.004], and 24% reduction compared with metoprolol (hazard ratio 0.76, CI 0.66–0.87, P = 0.0001). Conclusion: The vasodilatory beta blocker nebivolol was associated with reduced incident cardiovascular events compared with nonvasodilatory beta blockers. Additional study of other beta blockers is necessary to determine if this is a vasodilatory beta blocker class effect or is specific to nebivolol. Graphical abstract: http://links.lww.com/HJH/B916
Abstract Infrared oculographic recordings from three patients with hemianopia due to an occipital lesion showed that these patients employed a consistent set of (presumably unconscious) compensatory strategies to find and fixate objects. For targets in the blind hemifield, patients at first used a staircase strategy consisting of a series of stepwise saccadic search movements. This is safe but slow. When retested later, one patient had adopted a more efficient strategy employing one large saccadc calculated to overshoot the target. Other strategies for finding targets in the blind hemifield were employed in response to specific situations presented by our experiments: a predictive strategy using past experience to anticipate where the target would be found, and special strategies for recovering a lost target and for awaiting the reappearance of the target. To fixate targets in the seeing hemifield, our subjects undershot the target to prevent losing it in the blind hemifield, then held it off‐fovea on the seeing side of the macula.
Introduction: Beta blockers are not guideline-recommended first-line agents for hypertension, based on evidence that older generation beta blockers such as atenolol are associated with inferior reduction of some cardiovascular events compared to other antihypertensive classes. Vasodilatory beta blockers such as nebivolol have been found to have beneficial effects on peripheral vasculature through nitric oxide, endothelin-1, and tissue plasminogen activator pathways. The objective of this study is to compare longitudinal cardiovascular outcomes of hypertensive patients taking the vasodilatory beta blocker nebivolol with hypertensive patients taking the non-vasodilatory beta blockers atenolol and metoprolol. Hypothesis: Nebivolol will be associated with a reduction in odds of adverse cardiovascular outcomes compared with non-vasodilatory beta blockers. Methods: The study is a retrospective cohort analysis of de-identified data from adults in the University of Colorado health system with hypertension and on the vasodilatory beta blocker nebivolol or the non-vasodilatory beta blockers atenolol or metoprolol, without preceding diagnosis of cardiovascular or cerebrovascular disease. The primary outcome is incident cardiovascular hospitalization or diagnosis of cardiovascular event including heart failure, stroke, myocardial infarction, angina pectoris, or coronary revascularization based on diagnosis or procedure codes. Nearest-available propensity matching logistic regression was used, with each patient taking nebivolol matched to two control patients taking a non-vasodilatory beta blocker. Propensity matching variables included baseline demographics, cardiovascular risk factors, Charlson comorbidity index, other cardiovascular medications, and duration of follow-up. Results: There were 1395 patients taking nebivolol, and 20208 patients taking atenolol or metoprolol. Patients were predominantly female (54%, 11681 of 21603) and non-Hispanic white (75%, 16185 of 21603), with mean age of 60. The primary outcome occurred in 19% (259 of 1395) of those taking nebivolol, 29% (1891 of 6527) of those taking atenolol, and 40% (5500 of 13681) of those taking metoprolol. In propensity matched logistic regression, nebivolol is associated with reduced odds of incident cardiovascular events when compared to the non-vasodilatory beta blockers atenolol and metoprolol (OR 0.33, 95% CI 0.28 to 0.40). This association was also found with individual comparison with atenolol (OR 0.47, 95% CI 0.39 to 0.57) and metoprolol (OR 0.26, 95% CI 0.21 to 0.32). Conclusions: The vasodilatory beta blocker nebivolol is associated with reduced odds of incident cardiovascular events compared to non-vasodilatory beta blockers. Additional study of other beta blockers is necessary to determine if this is a vasodilatory beta blocker class effect, or is specific to nebivolol.
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of methods exist for screening for AF, a targeted approach, which requires an efficient method for identifying patients at risk, would be preferred.
Objective
To examine machine learning approaches applied to electronic health record data that have been harmonized to the Observational Medical Outcomes Partnership Common Data Model for identifying risk of AF.
Design, Setting, and Participants
This diagnostic study used data from 2 252 219 individuals cared for in the UCHealth hospital system, which comprises 3 large hospitals in Colorado, from January 1, 2011, to October 1, 2018. Initial analysis was performed in December 2018; follow-up analysis was performed in July 2019.
Exposures
All Observational Medical Outcomes Partnership Common Data Model–harmonized electronic health record features, including diagnoses, procedures, medications, age, and sex.
Main Outcomes and Measures
Classification of incident AF in designated 6-month intervals, adjudicated retrospectively, based on area under the receiver operating characteristic curve and F1 statistic.
Results
Of 2 252 219 individuals (1 225 533 [54.4%] women; mean [SD] age, 42.9 [22.3] years), 28 036 (1.2%) developed incident AF during a designated 6-month interval. The machine learning model that used the 200 most common electronic health record features, including age and sex, and random oversampling with a single-layer, fully connected neural network provided the optimal prediction of 6-month incident AF, with an area under the receiver operating characteristic curve of 0.800 and an F1 score of 0.110. This model performed only slightly better than a more basic logistic regression model composed of known clinical risk factors for AF, which had an area under the receiver operating characteristic curve of 0.794 and an F1 score of 0.079.
Conclusions and Relevance
Machine learning approaches to electronic health record data offer a promising method for improving risk prediction for incident AF, but more work is needed to show improvement beyond standard risk factors.
Low levels of physical activity are associated with increased mortality risk, especially in cardiac patients, but most studies are based on self-report. Cardiac implantable electronic devices (CIEDs) offer an opportunity to collect data for longer periods of time. However, there is limited agreement on the best approaches for quantification of activity measures due to the time series nature of the data. We examined physical activity time series data from 235 subjects with CIEDs and at least 365 days of uninterrupted measures. Summary statistics for raw daily physical activity (minutes/day), including statistical moments (e.g., mean, standard deviation, skewness, kurtosis), time series regression coefficients, frequency domain components, and forecasted predicted values, were calculated for each individual, and used to predict occurrence of ventricular tachycardia (VT) events as recorded by the device. In unsupervised analyses using principal component analysis, we found that while certain features tended to cluster near each other, most provided a reasonable spread across activity space without a large degree of redundancy. In supervised analyses, we found several features that were associated with the outcome (P < 0.05) in univariable and multivariable approaches, but few were consistent across models. Using a machine-learning approach in which the data was split into training and testing sets, and models ranging in complexity from simple univariable logistic regression to ensemble decision trees were fit, there was no improvement in classification of risk over naïve methods for any approach. Although standard approaches identified summary features of physical activity data that were correlated with risk of VT, machine-learning approaches found that none of these features provided an improvement in classification. Future studies are needed to explore and validate methods for feature extraction and machine learning in classification of VT risk based on device-measured activity.
