Utility of routine evaluations for rejection in patients greater than 2 years after heart transplantation

AimsGuidelines support routine surveillance testing for rejection for at least 5 years after heart transplant (HT). In patients greater than 2 years post‐HT, we examined which clinical characteristics predict continuation of routine surveillance studies, outcomes following discontinuation of routine surveillance, and the cost‐effectiveness of different surveillance strategies.Methods and resultsWe retrospectively identified subjects older than 18 who underwent a first HT at our centre from 2007 to 2016 and who survived ≥760 days (n = 217) post‐HT. The clinical context surrounding all endomyocardial biopsies (EMBs) and gene expression profiles (GEPs) was reviewed to determine if studies were performed routinely or were triggered by a change in clinical status. Subjects were categorized as following a test‐based surveillance (n = 159) or a signs/symptoms surveillance (n = 53) strategy based on treating cardiologist intent to continue routine studies after the second post‐transplant year. A Markov model was constructed to compare two test‐based surveillance strategies to a baseline strategy of discontinuing routine studies. One thousand twenty studies were performed; 835 were routine. Significant rejection was absent in 99.0% of routine EMBs and 99.8% of routine GEPs. The treating cardiologist’s practice duration, patient age, and immunosuppressive regimen predicted surveillance strategy. There were no differences in outcomes between groups. Routine surveillance EMBs cost more and were marginally less effective than a strategy of discontinuing routine studies after 2 years; surveillance GEPs had an incremental cost‐effectiveness ratio of $1.67 million/quality‐adjusted life‐year.ConclusionsAcute asymptomatic rejection is rare after the second post‐transplant year. Obtaining surveillance studies beyond the second post‐transplant year is not cost‐effective.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156231/2/ehf212745.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156231/1/ehf212745_am.pd

Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) Study: A Subgroup Analysis (MIPACT-AFib)

BackgroundThe rising adoption of wearable technology increases the potential to identify arrhythmias. However, specificity of these notifications is poorly defined and may cause anxiety and unnecessary resource utilization. Herein, we report results of a follow-up screening protocol for incident atrial fibrillation/flutter (AF) within a large observational digital health study.MethodsThe MIPACT Study enrolled 6,765 adult patients who were provided an Apple Watch and blood pressure (BP) monitors. From March to July 2019, participants were asked to contact the study team for any irregular heart rate (HR) notification. They were assessed using structured questionnaires and asked to provide 6 Apple Watch EKGs. Those with arrhythmias or non-diagnostic EKGs were sent 7-day monitors. The EHR was reviewed after 3 years to determine if participants developed arrhythmias.Results86 participants received notifications and met inclusion criteria. Mean age was 50.5 (SD 16.9) years, and 46 (53.3%) were female. Of 76 participants assessed by the study team, 32 (42.1%) reported anxiety surrounding notifications. Of 59 participants who sent at least 1 EKG, 52 (88.1%) were in sinus rhythm, 3 (5.1%) AF, 2 (3.4%) indeterminate, and 2 (3.4%) sinus bradycardia. Cardiac monitor demonstrated AF in 2 of 3 participants with AF on Apple Watch EKGs. 2 contacted their PCPs and were diagnosed with AF. In total, 5 cases of AF were diagnosed with 1 additional case identified during EHR review.ConclusionWearable devices produce alarms that can frequently be anxiety provoking. Research is needed to determine the implications of these alarms and appropriate follow-up

Statin intensity and risk for cardiovascular events after heart transplantation

AimsStatins improve survival and reduce rejection and cardiac allograft vasculopathy after heart transplantation (HT). The impact of different statin intensities on clinical outcomes has never been assessed. We set out to determine the impact of statin exposure on cardiovascular outcomes after HT.Methods and resultsWe performed a retrospective study of 346 adult patients who underwent HT from 2006 to 2018. Statin intensity was determined longitudinally after HT based on American College of Cardiology/American Heart Association (ACC/AHA) guidelines. The primary outcome was the time to the first primary event defined as the composite of heart failure hospitalization, myocardial infarction, revascularization, and all‐cause mortality. Secondary outcomes included time to significant rejection and time to moderate–severe cardiac allograft vasculopathy. Adverse events were evaluated for subjects on high‐intensity statin therapy. A Cox proportional hazards model was used to evaluate the relationship between clinical variables, statin intensity, and outcomes. Most subjects were treated with low‐intensity statin therapy although this declined from 89.9% of the population at 1month after HT to 42.8% at 5years after HT. History of ischaemic cardiomyopathy, significant acute rejection, older donor age, and lesser statin intensity (p ≤ 0.001) were associated with reduced time to the primary outcome in a multivariable Cox model. Greater intensity of statin therapy was most beneficial early after HT. There were no statin‐related adverse events for the 14 subjects on high‐intensity statin therapy.ConclusionsGreater statin intensity was associated with a reduction in adverse cardiovascular outcomes after HT.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162686/2/ehf212784.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162686/1/ehf212784_am.pd

A Novel Tropical Geometry-based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification

Impact of Vitamin C on Endothelial Function and Exercise Capacity in Patients with a Fontan Circulation

Objective.  To evaluate the impact of antioxidant therapy on functional health status in Fontan‐palliated patients. Design.  Prospective, randomized, double‐blind, placebo‐controlled trial. Patients.  Fifty‐three generally asymptomatic Fontan patients. Interventions.  Patients were randomized to receive either high‐dose ascorbic acid (vitamin C) or placebo for 4 weeks. Outcome Measures.  Peripheral vascular function, as measured with endothelium‐dependent digital pulse amplitude testing (EndoPAT), and exercise capacity were assessed before and after study drug treatment. Primary outcome measures included the EndoPAT index and peripheral arterial tonometry (PAT) ratio, both validated markers of vascular function. Secondary outcome measures included peak oxygen consumption and work. Results.  Twenty‐three vitamin C‐ and 21 placebo‐assigned subjects completed the protocol (83%). Median age and time from Fontan completion were 15 (interquartile range [IQR] 11.7–18.2) and 11.9 years (IQR 9.0–15.7), respectively. Right ventricular morphology was dominant in 30 (57%). Outcome measures were similar between groups at baseline. Among all subjects, vitamin C therapy was not associated with a statistical improvement in either primary or secondary outcome measures. In subjects with abnormal vascular function at baseline, compared with placebo, vitamin C therapy more frequently resulted in normalization of the EndoPAT index (45% vs. 17%) and PAT ratio (38% vs. 13%). Conclusions.  Short‐term therapy with vitamin C does not alter endothelial function or exercise capacity in an asymptomatic Fontan population overall. Vitamin C may provide benefit to a subset of Fontan patients with abnormal vascular function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92126/1/j.1747-0803.2011.00605.x.pd

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

Abstract Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation. In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively. The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels

Acceptability of a Text Message‐Based Mobile Health Intervention to Promote Physical Activity in Cardiac Rehabilitation Enrollees: A Qualitative Substudy of Participant Perspectives

Background Mobile health (mHealth) interventions have the potential to deliver longitudinal support to users outside of episodic clinical encounters. We performed a qualitative substudy to assess the acceptability of a text message‐based mHealth intervention designed to increase and sustain physical activity in cardiac rehabilitation enrollees. Methods and Results Semistructured interviews were conducted with intervention arm participants of a randomized controlled trial delivered to low‐ and moderate‐risk cardiac rehabilitation enrollees. Interviews explored participants' interaction with the mobile application, reflections on tailored text messages, integration with cardiac rehabilitation, and opportunities for improvement. Transcripts were thematically analyzed using an iteratively developed codebook. Sample size consisted of 17 participants with mean age of 65.7 (SD 8.2) years; 29% were women, 29% had low functional capacity, and 12% were non‐White. Four themes emerged from interviews: engagement, health impact, personalization, and future directions. Participants engaged meaningfully with the mHealth intervention, finding it beneficial in promoting increased physical activity. However, participants desired greater personalization to their individual health goals, fitness levels, and real‐time environment. Generally, those with lower functional capacity and less experience with exercise were more likely to view the intervention positively. Finally, participants identified future directions for the intervention including better incorporation of exercise physiologists and social support systems. Conclusions Cardiac rehabilitation enrollees viewed a text message‐based mHealth intervention favorably, suggesting the potentially high usefulness of mHealth technologies in this population. Addressing participant‐identified needs on increased user customization and inclusion of clinical and social support is crucial to enhancing the effectiveness of future mHealth interventions. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882

Recruitment Strategies of a Decentralized Randomized Placebo Controlled Clinical Trial: The Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure (CHIEF-HF) Trial

BACKGROUND: There has been growing Interest in patient-centered clinical trials using mobile technologies to reduce the need for in-person visits. The CHIEF-HF (Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure) trial was designed as a double-blind, randomized, fully decentralized clinical trial (DCT) that identified, consented, treated, and followed participants without any in-person visits. Patient-reported questionnaires were the primary outcome, which were collected by a mobile application. To inform future DCTs, we sought to describe the strategies used in successful trial recruitment. METHODS: This article describes the operational structure and novel strategies employed in a completely DCT by summarizing the recruitment, enrollment, engagement, retention, and follow-up processes used in the execution of the trial at 18 centers. RESULTS: A total of 18 sites contacted 130,832 potential participants, of which 2572 (2.0%) opened a hyperlink to the study website, completed a brief survey, and agreed to be contacted for potential inclusion. Of these, 1333 were eligible, and 658 consented; there were 182 screen failures, due primarily to baseline Kansas City Cardiomyopathy Questionnaire scores\u27 not meeting inclusion criteria, resulting in 476 participants\u27 being enrolled (18.5%). There was significant site-level variation in the number of patients invited (median = 2976; range 73-46,920) and in those agreeing to be contacted (median = 2.4%; range 0.05%-16.4%). At the site with the highest enrollment, patients contacted by electronic medical record portal messaging were more likely to opt into the study successfully than those contacted by e-mail alone (7.8% vs 4.4%). CONCLUSIONS: CHIEF-HF used a novel design and operational structure to test the efficacy of a therapeutic treatment, but marked variability across sites and strategies for recruiting participants was observed. This approach may be advantageous for clinical research across a broader range of therapeutic areas, but further optimization of recruitment efforts is warranted. REGISTRATION: NCT04252287 https://clinicaltrials.gov/ct2/show/NCT04252287