Expanded adaptation of an artificial intelligence model for predicting chemotherapy-induced cardiotoxicity using baseline electrocardiograms

Introduction

While effective as chemotherapeutics, anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD), which adversely affects the prognosis of patients with malignancies1–5. Despite guideline recommendations6–9, repeated echocardiograms are rarely performed10 with delayed diagnosis of CTRCD leading to unrecoverable cardiac dysfunction11. Recently, artificial intelligence (AI) was shown to be capable of detecting reduced left ventricular ejection fraction (LVEF) solely from electrocardiogram (ECG)12. Furthermore, this model was predictive of a future decrease in LVEF. Therefore, we hypothesized that an AI model detecting reduced LVEF (AI-EF model) could predict CTRCD from ECGs.

Purpose

To assess whether the AI-EF model could detect patients at a high risk of CTRCD by analyzing ECGs taken immediately prior to the initiation of cardiotoxic chemotherapy.

Methods

Among patients who received chemotherapy with a regimen including anthracyclines in two institutions between June 1st, 2015 and October 1st, 2020, those who underwent both an ECG and echocardiogram ≤90 days prior to initial treatment were selected. The ECGs were analyzed by the AI-EF model and patients were divided into two groups according to the scores from the model. CTRCD was defined as LVEF <53% and ≥10% decrease in LVEF from the baseline at any time after the start of chemotherapy13. The cumulative incidence of CTRCD was compared for the two groups using Kaplan-Meier curves, log-rank test, a univariate Cox proportional hazard model, and a multivariable Cox proportional hazard model adjusting for known risk factors for CTRCD. Finally, a prediction model for CTRCD using readily available clinical variables with the AI-EF score was compared with the model using the same variables without the AI-EF score.

Results

1,158 patients were included in this study. 99 of them developed CTRCD during follow-up. The AI-EF model displayed excellent risk stratification of developing CTRCD: while 7.1% in the low AI-EF score group developed CTRCD, 12.9% of the patients in the high AI-EF score group developed CTRCD (hazard ratio (HR), 2.14; 95% confidence interval (CI), 1.43–3.19; log-rank p<0.001; Figure 1). This finding was robust across subgroups such as cancer types, the initial dose of anthracycline and baseline LVEF, and consistent after adjusting for multiple risk factors (adjusted HR, 2.10; 95% CI, 1.37–3.22; p<0.001; Figure 2). Furthermore, the addition of the AI-EF score significantly improved the accuracy of predicting CTRCD compared to clinical features alone (time-dependent area under the received operating curve (AUROC) for 2 years, 77.1; 95% CI, 71.8–82.3 for the model with AI-EF score and AUROC 73.9; 95% CI, 69.0–80.1 for the model without AI-EF score; p=0.02).

Conclusion

The AI-EF model, by utilizing baseline ECG, could stratify patients according to the risk of CTRCD and robustly augmented CTRCD prediction.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): American Heart AssociationVerily

Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection

BACKGROUND

Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning-based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts.

METHODS

Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence.

RESULTS

We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88-0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79-0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90-0.96 and 0.90-0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram).

CONCLUSIONS

Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.

Abstract P212: A Community Intervention For Hypertension Control Using Health Worker Outreach And Algorithmic Software-driven Blood Pressure Management

Background: Hypertension (HTN) in black and brown communities remains under-diagnosed and poorly controlled. Obstacles include access, lack of trust, social determinants of health (SDOH) and insufficient licensed providers. We hypothesized that a multi-faceted solution, combining screening, identification and support to address SDOH, and a remote software-driven HTN management program that shifts work to care navigators would be effective in controlling HTN.

Methods: We launched a 500-person pilot in Detroit, MI, in March 2022. Community Health Workers (CHW) screened for patients with HTN in the community and surveyed them for SDOH. Patients received a cellular-enabled blood pressure (BP) cuff. Medications were initiated, titrated and side effects monitored according to a software-driven algorithm implementing latest HTN guidelines. Patients were contacted by a care navigator or community health worker using a combination of phone, text message, and email. Medication and testing were ordered by a licensed provider. Program success was evaluated based on proportion of patients who reached BP targets and patient satisfaction surveys.

Results: During the initial deployment, 135 patients were screened and 86 (64%) were found to have Stage 1 HTN or greater. A total of 48 patients consented to join the program and 25 received a confirmatory BP measurement and cellular BP device. The average age of enrolled patients was 58±11 years with 52% females (N = 13), with 100% identified as Black. The median BP was 149/91mmHg, (IQR 144-159/87-98 mmHg). HTN severity on enrollment was 4% Stage 1, 59% Stage 2, and 37% Stage 3. Sixty-five % of patients were already taking one or more anti-HTN agents. A mean of 0.8 dose changes were made per patient per week. Results of the initial 250 managed patients will be presented including the percentage that reached BP goals, the number of medications required, time to reach goal, as well as impact of SDOH measures on BP control.

Conclusions: Screening directly in an urban community resulted in a higher than expected rate of hypertension and included a high rate of stage 3 disease. A community-centered remote software-driven HTN program was feasible to implement.

Promoting validation and cross-phylogenetic integration in model organism research

Model organism (MO) research provides a basic understanding of biology and disease due to the evolutionary conservation of the molecular and cellular language of life. MOs have been used to identify and understand the function of orthologous genes, proteins, cells and tissues involved in biological processes, to develop and evaluate techniques and methods, and to perform whole-organism-based chemical screens to test drug efficacy and toxicity. However, a growing richness of datasets and the rising power of computation raise an important question: How do we maximize the value of MOs? In-depth discussions in over 50 virtual presentations organized by the National Institutes of Health across more than 10 weeks yielded important suggestions for improving the rigor, validation, reproducibility and translatability of MO research. The effort clarified challenges and opportunities for developing and integrating tools and resources. Maintenance of critical existing infrastructure and the implementation of suggested improvements will play important roles in maintaining productivity and facilitating the validation of animal models of human biology and disease.

Single Cell Biology: Exploring Somatic Cell Behaviors, Competition and Selection in Chronic Disease

The full range of cell functions is under-determined in most human diseases. The evidence that somatic cell competition and clonal imbalance play a role in non-neoplastic chronic disease reveal a need for a dedicated effort to explore single cell function if we are to understand the mechanisms by which cell population behaviors influence disease. It will be vital to document not only the prevalent pathologic behaviors but also those beneficial functions eliminated or suppressed by competition. An improved mechanistic understanding of the role of somatic cell biology will help to stratify chronic disease, define more precisely at an individual level the role of environmental factors and establish principles for prevention and potential intervention throughout the life course and across the trajectory from wellness to disease.

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors

BACKGROUND

Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.

METHODS

We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.

RESULTS

Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.

CONCLUSIONS

We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

Stx4 is required to regulate cardiomyocyte Ca2+ handling during vertebrate cardiac development

Requirements for vesicle fusion within the heart remain poorly understood, despite the multitude of processes that necessitate proper intracellular trafficking within cardiomyocytes. Here, we show that Syntaxin 4 (STX4), a target-Soluble N-ethylmaleimide sensitive factor attachment receptor (t-SNARE) protein, is required for normal vertebrate cardiac conduction and vesicular transport. Two patients were identified with damaging variants in STX4. A patient with a homozygous R240W missense variant displayed biventricular dilated cardiomyopathy, ectopy, and runs of non-sustained ventricular tachycardia, sensorineural hearing loss, global developmental delay, and hypotonia, while a second patient displayed severe pleiotropic abnormalities and perinatal lethality. CRISPR/Cas9-generated stx4 mutant zebrafish exhibited defects reminiscent of these patients' clinical presentations, including linearized hearts, bradycardia, otic vesicle dysgenesis, neuronal atrophy, and touch insensitivity by 3 days post fertilization. Imaging of Vamp2+ vesicles within stx4 mutant zebrafish hearts showed reduced docking to the cardiomyocyte sarcolemma. Optical mapping of the embryonic hearts coupled with pharmacological modulation of Ca²⁺ handling together support that zebrafish stx4 mutants have a reduction in L-type Ca²⁺ channel modulation. Transgenic overexpression of zebrafish Stx4^R241W, analogous to the first patient's STX4^R240W variant, indicated that the variant is hypomorphic. Thus, these data show an in vivo requirement for SNAREs in regulating normal embryonic cardiac function and that variants in STX4 are associated with pleiotropic human disease, including cardiomyopathy.

Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility

Brugada syndrome (BrS) is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A, encoding the cardiac sodium channel Na_V1.5, susceptibility genes remain largely unknown. Here we performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with BrS and 10,001 controls, and identified 21 association signals at 12 loci (10 new). Single nucleotide polymorphism (SNP)-heritability estimates indicate a strong polygenic influence. Polygenic risk score analyses based on the 21 susceptibility variants demonstrate varying cumulative contribution of common risk alleles among different patient subgroups, as well as genetic associations with cardiac electrical traits and disorders in the general population. The predominance of cardiac transcription factor loci indicates that transcriptional regulation is a key feature of BrS pathogenesis. Furthermore, functional studies conducted on MAPRE2, encoding the microtubule plus-end binding protein EB2, point to microtubule-related trafficking effects on Na_V1.5 expression as a new underlying molecular mechanism. Taken together, these findings broaden our understanding of the genetic architecture of BrS and provide new insights into its molecular underpinnings.

PIEZO1 mediates a mechanothrombotic pathway in diabetes

[Figure: see text].

Population health management of low-density lipoprotein cholesterol via a remote, algorithmic, navigator-executed program

BACKGROUND

Implementation of guideline-directed cholesterol management remains low despite definitive evidence establishing such measures reduce cardiovascular (CV) events, especially in high atherosclerotic CV disease (ASCVD) risk patients. Modern electronic resources now exist that may help improve health care delivery. While electronic medical records (EMR) allow for population health screening, the potential for coupling EMR screening to remotely delivered algorithmic population-based management has been less studied as a way of overcoming barriers to optimal cholesterol management.

METHODS

In an academically affiliated healthcare system, using EMR screening, we sought to identify 1,000 high ASCVD risk patients not meeting guideline-directed low-density lipoprotein-cholesterol (LDL-C) goals within specific system-affiliated primary care practices. Contacted patients received cholesterol education and were offered a remote, guideline-directed, algorithmic cholesterol management program executed by trained but non-licensed "navigators" under professional supervision. Navigators used telephone, proprietary software and internet resources to facilitate algorithm-driven, guideline-based medication initiation/titration, and laboratory testing until patients achieved LDL-C goals or exited the program. As a clinical effectiveness program for cholesterol guideline implementation, comparison was made to those contacted patients who declined program-based medication management, and received education only, along with their usual care.

RESULTS

1021 patients falling into guideline-defined high ASCVD risk groups warranting statin therapy (ASCVD, type 2 diabetes, LDL ≥ 190 mg/dL, calculated 10-year ASCVD risk ≥7.5%) and not achieving guideline-defined target LDL-C levels and/or therapy were identified and contacted. Among the 698 such patients who opted for program medication management, significant LDL-C reductions occurred in the total cohort (mean -65.4 mg/dL, 45% decrease), and each high ASCVD risk subgroup: ASCVD (-57.2 mg/dL, -48.0%); diabetes mellitus (-53.1 mg/dL, -40.0%); severe hypercholesterolemia (-76.3 mg/dL, -45.7%); elevated ASCVD 10-year risk (-62.8 mg/dL, -41.1%) (P<0.001 for all), without any significant complications. Among 20% of participants with reported statin intolerance, average LDL-C decreased from baseline 143 mg/dL to 85 mg/dL using mainly statins and ezetimibe, with limited PCSK9 inhibitor use. In comparison, eligible high ASCVD risk patients who were contacted but opted for education only, a 17% LDL-C decrease occurred over a similar timeframe, with 80% remaining with an LDL-C over 100 mg/dL.

CONCLUSIONS

A remote, algorithm-driven, navigator-executed cholesterol management program successfully identified high ASCVD risk undertreated patients using EMR screening and was associated with significantly improved guideline-directed LDL-C control, supporting this approach as a novel strategy for improving health care access and delivery.

Noninvasive Scale Measurement of Stroke Volume and Cardiac Output Compared With the Direct Fick Method: A Feasibility Study

Background Objective markers of cardiac function are limited in the outpatient setting and may be beneficial for monitoring patients with chronic cardiac conditions. We assess the accuracy of a scale, with the ability to capture ballistocardiography, electrocardiography, and impedance plethysmography signals from a patient's feet while standing on the scale, in measuring stroke volume and cardiac output compared with the gold-standard direct Fick method. Methods and Results Thirty-two patients with unexplained dyspnea undergoing level 3 invasive cardiopulmonary exercise test at a tertiary medical center were included in the final analysis. We obtained scale and direct Fick measurements of stroke volume and cardiac output before and immediately after invasive cardiopulmonary exercise test. Stroke volume and cardiac output from a cardiac scale and the direct Fick method correlated with r=0.81 and r=0.85, respectively (P<0.001 each). The mean absolute error of the scale estimated stroke volume was -1.58 mL, with a 95% limits of agreement of -21.97 to 18.81 mL. The mean error for the scale estimated cardiac output was -0.31 L/min, with a 95% limits of agreement of -2.62 to 2.00 L/min. The changes in stroke volume and cardiac output before and after exercise were 78.9% and 96.7% concordant, respectively, between the 2 measuring methods. Conclusions In a proof-of-concept study, this novel scale with cardiac monitoring abilities may allow for noninvasive, longitudinal measures of cardiac function. Using the widely accepted form factor of a bathroom scale, this method of monitoring can be easily integrated into a patient's lifestyle.

Valsartan in early-stage hypertrophic cardiomyopathy: a randomized phase 2 trial

Hypertrophic cardiomyopathy (HCM) is often caused by pathogenic variants in sarcomeric genes and characterized by left ventricular (LV) hypertrophy, myocardial fibrosis and increased risk of heart failure and arrhythmias. There are no existing therapies to modify disease progression. In this study, we conducted a multi-center, double-blind, placebo-controlled phase 2 clinical trial to assess the safety and efficacy of the angiotensin II receptor blocker valsartan in attenuating disease evolution in early HCM. In total, 178 participants with early-stage sarcomeric HCM were randomized (1:1) to receive valsartan (320 mg daily in adults; 80-160 mg daily in children) or placebo for 2 years ( NCT01912534 ). Standardized changes from baseline to year 2 in LV wall thickness, mass and volumes; left atrial volume; tissue Doppler diastolic and systolic velocities; and serum levels of high-sensitivity troponin T and N-terminal pro-B-type natriuretic protein were integrated into a single composite z-score as the primary outcome. Valsartan (n = 88) improved cardiac structure and function compared to placebo (n = 90), as reflected by an increase in the composite z-score (between-group difference +0.231, 95% confidence interval (+0.098, +0.364); P = 0.001), which met the primary endpoint of the study. Treatment was well-tolerated. These results indicate a key opportunity to attenuate disease progression in early-stage sarcomeric HCM with an accessible and safe medication.

MIC-Drop: A platform for large-scale in vivo CRISPR screens

[Figure: see text].

Targeting the Microtubule EB1-CLASP2 Complex Modulates NaV1.5 at Intercalated Discs

[Figure: see text].

A Call to Action for New Global Approaches to Cardiovascular Disease Drug Solutions

While we continue to wrestle with the immense challenge of implementing equitable access to established evidence-based treatments, substantial gaps remain in our pharmacotherapy armament for common forms of cardiovascular disease including coronary and peripheral arterial disease, heart failure, hypertension, and arrhythmia. We need to continue to invest in the development of new approaches for the discovery, rigorous assessment, and implementation of new therapies. Currently, the time and cost to progress from lead compound/product identification to the clinic, and the success rate in getting there reduces the incentive for industry to invest, despite the enormous burden of disease and potential size of market. There are tremendous opportunities with improved phenotyping of patients currently batched together in syndromic "buckets." Use of advanced imaging and molecular markers may allow stratification of patients in a manner more aligned to biological mechanisms that can, in turn, be targeted by specific approaches developed using high-throughput molecular technologies. Unbiased "omic" approaches enhance the possibility of discovering completely new mechanisms in such groups. Furthermore, advances in drug discovery platforms, and models to study efficacy and toxicity more relevant to the human disease, are valuable. Re-imagining the relationships among discovery, translation, evaluation, and implementation will help reverse the trend away from investment in the cardiovascular space, establishing innovative platforms and approaches across the full spectrum of therapeutic development.

Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

A call to action for new global approaches to cardiovascular disease drug solutions

Whilst we continue to wrestle with the immense challenge of implementing equitable access to established evidence-based treatments, substantial gaps remain in our pharmacotherapy armament for common forms of cardiovascular disease including coronary and peripheral arterial disease, heart failure, hypertension, and arrhythmia. We need to continue to invest in the development of new approaches for the discovery, rigorous assessment, and implementation of new therapies. Currently, the time and cost to progress from lead compound/product identification to the clinic, and the success rate in getting there reduces the incentive for industry to invest, despite the enormous burden of disease and potential size of market. There are tremendous opportunities with improved phenotyping of patients currently batched together in syndromic 'buckets'. Use of advanced imaging and molecular markers may allow stratification of patients in a manner more aligned to biological mechanisms that can, in turn, be targeted by specific approaches developed using high-throughput molecular technologies. Unbiased 'omic' approaches enhance the possibility of discovering completely new mechanisms in such groups. Furthermore, advances in drug discovery platforms, and models to study efficacy and toxicity more relevant to the human disease, are valuable. Re-imagining the relationships among discovery, translation, evaluation, and implementation will help reverse the trend away from investment in the cardiovascular space, establishing innovative platforms and approaches across the full spectrum of therapeutic development.

Evaluation of the Usage and Dosing of Guideline-Directed Medical Therapy for Heart Failure With Reduced Ejection Fraction Patients in Clinical Practice

BACKGROUND

Although strategies for optimization of pharmacologic therapy in patients with heart failure with reduced ejection fraction (HFrEF) are scripted by guidelines, data from HF registries suggests that guideline-directed medical therapies (GDMT) are underutilized among eligible patients. Whether this discrepancy reflects medication intolerance, contraindications, or a quality of care issue remains unclear.

OBJECTIVE

The objective of this initiative was to identify reasons for underutilization and under-dosing of HFrEF therapy in patients at a large, academic medical center.

METHODS

Among 500 patients with HFrEF enrolled in a quality improvement project at a tertiary center, we evaluated usage and dosing of 4 categories of GDMT: ACE inhibitors/Angiotensin Receptor Blockers (ACE-i/ARB), Angiotensin Receptor-Neprilysin Inhibitors (ARNi), beta blockers, and Mineralocorticoid Receptor Antagonists (MRA). Reasons for nonprescription and usage of suboptimal doses were abstracted from notes in the chart and from telephone review of previous medication trials with the patient.

RESULTS

Of 500 patients identified, 472 subjects had complete data for analysis. Among eligible patients, ACE-i/ARB were prescribed in 81.4% (293 of 360) and beta blockers in 94.4% (442 of 468). Of these patients, 10.6% were prescribed target doses of ACE-i/ARB and 12.4% were prescribed target doses of beta blockers. Utilization of other categories of GDMT was lower, with 54% of eligible patients prescribed MRAs and 27% prescribed an ARNi. In most cases, the reasons for nonprescription or under-dosing of GDMT were not apparent on review of the health record or discussion with the patient.

CONCLUSION

Clear rationale for nonprescription and under-dosing of GDMT often cannot be ascertained from detailed review and is only rarely related to documented medication intolerance or contraindications, suggesting an opportunity for quality improvement.

The endosomal trafficking regulator LITAF controls the cardiac Nav1.5 channel via the ubiquitin ligase NEDD4-2

The QT interval is a recording of cardiac electrical activity. Previous genome-wide association studies identified genetic variants that modify the QT interval upstream of LITAF (lipopolysaccharide-induced tumor necrosis factor-α factor), a protein encoding a regulator of endosomal trafficking. However, it was not clear how LITAF might impact cardiac excitation. We investigated the effect of LITAF on the voltage-gated sodium channel Nav1.5, which is critical for cardiac depolarization. We show that overexpressed LITAF resulted in a significant increase in the density of Nav1.5-generated voltage-gated sodium current INa and Nav1.5 surface protein levels in rabbit cardiomyocytes and in HEK cells stably expressing Nav1.5. Proximity ligation assays showed co-localization of endogenous LITAF and Nav1.5 in cardiomyocytes, whereas co-immunoprecipitations confirmed they are in the same complex when overexpressed in HEK cells. In vitro data suggest that LITAF interacts with the ubiquitin ligase NEDD4-2, a regulator of Nav1.5. LITAF overexpression down-regulated NEDD4-2 in cardiomyocytes and HEK cells. In HEK cells, LITAF increased ubiquitination and proteasomal degradation of co-expressed NEDD4-2 and significantly blunted the negative effect of NEDD4-2 on INa We conclude that LITAF controls cardiac excitability by promoting degradation of NEDD4-2, which is essential for removal of surface Nav1.5. LITAF-knockout zebrafish showed increased variation in and a nonsignificant 15% prolongation of action potential duration. Computer simulations using a rabbit-cardiomyocyte model demonstrated that changes in Ca2+ and Na+ homeostasis are responsible for the surprisingly modest action potential duration shortening. These computational data thus corroborate findings from several genome-wide association studies that associated LITAF with QT interval variation.

Lysolipids in Vascular Development, Biology, and Disease

Membrane phospholipid metabolism forms lysophospholipids, which possess unique biochemical and biophysical properties that influence membrane structure and dynamics. However, lysophospholipids also function as ligands for G-protein-coupled receptors that influence embryonic development, postnatal physiology, and disease. The 2 most well-studied species-lysophosphatidic acid and S1P (sphingosine 1-phosphate)-are particularly relevant to vascular development, physiology, and cardiovascular diseases. This review summarizes the role of lysophosphatidic acid and S1P in vascular developmental processes, endothelial cell biology, and their roles in cardiovascular disease processes. In addition, we also point out the apparent connections between lysophospholipid biology and the Wnt (int/wingless family) pathway, an evolutionarily conserved fundamental developmental signaling system. The discovery that components of the lysophospholipid signaling system are key genetic determinants of cardiovascular disease has warranted current and future research in this field. As pharmacological approaches to modulate lysophospholipid signaling have entered the clinical sphere, new findings in this field promise to influence novel therapeutic strategies in cardiovascular diseases.

Ecosystem Barriers to Innovation Adoption in Clinical Practice

Despite increasing ability to understand and correct molecular derangements in disease, genomics and novel phenotypic assays are unevenly deployed in clinical practice. This has hampered translational research and our ability to identify clinically actionable subtypes of disease. Historic examples illustrate how the perspectives of stakeholders across the healthcare ecosystem can influence adoption of innovations in healthcare. Consideration of these factors, from discovery to implementation, can accelerate adoption of new molecular and digital phenotypes in a 'learning' healthcare ecosystem.

Remote Optimization of Guideline-Directed Medical Therapy in Patients With Heart Failure With Reduced Ejection Fraction

Importance

Optimal treatment of heart failure with reduced ejection fraction (HFrEF) is scripted by treatment guidelines, but many eligible patients do not receive guideline-directed medical therapy (GDMT) in clinical practice.

Objective

To determine whether a remote, algorithm-driven, navigator-administered medication optimization program could enhance implementation of GDMT in HFrEF.

Design, Setting, and Participants

In this case-control study, a population-based sample of patients with HFrEF was offered participation in a quality improvement program directed at GDMT optimization. Treating clinicians in a tertiary academic medical center who were caring for patients with heart failure and an ejection fraction of 40% or less (identified through an electronic health record-based search) were approached for permission to adjust medical therapy according to a sequential titration algorithm modeled on the current American College of Cardiology/American Heart Association heart failure guidelines. Navigators contacted participants by telephone to direct medication adjustment and conduct longitudinal surveillance of laboratory tests, blood pressure, and symptoms under supervision of a pharmacist, nurse practitioner, and heart failure cardiologist. Patients and clinicians declining to participate served as a control group.

Exposures

Navigator-led remote optimization of GDMT compared with usual care.

Main Outcomes and Measures

Proportion of patients receiving GDMT in the intervention and control groups at 3 months.

Results

Of 1028 eligible patients (mean [SD] values: age, 68 [14] years; ejection fraction, 32% [8%]; and systolic blood pressure, 122 [18] mm Hg; 305 women (30.0%); 892 individuals [86.8%] in New York Heart Association class I and II), 197 (19.2%) participated in the medication optimization program, and 831 (80.8%) continued with usual care as directed by their treating clinicians (585 [56.9%] general cardiologists; 443 [43.1%] heart failure specialists). At 3 months, patients participating in the remote intervention experienced significant increases from baseline in use of renin-angiotensin system antagonists (138 [70.1%] to 170 [86.3%]; P < .001) and β-blockers (152 [77.2%] to 181 [91.9%]; P < .001) but not mineralocorticoid receptor antagonists (51 [25.9%] to 60 [30.5%]; P = .14). Doses for each category of GDMT also increased from baseline in the intervention group. Among the usual-care group, there were no changes from baseline in the proportion of patients receiving GDMT or the dose of GDMT in any category.

Conclusions and Relevance

Remote titration of GDMT by navigators using encoded algorithms may represent an efficient, population-level strategy for rapidly closing the gap between guidelines and clinical practice in patients with HFrEF.