Myocardial infarction accelerates atherosclerosis.

During progression of atherosclerosis, myeloid cells destabilize lipid-rich plaques in the arterial wall and cause their rupture, thus triggering myocardial infarction and stroke. Survivors of acute coronary syndromes have a high risk of recurrent events for unknown reasons. Here we show that the systemic response to ischaemic injury aggravates chronic atherosclerosis. After myocardial infarction or stroke, Apoe-/- mice developed larger atherosclerotic lesions with a more advanced morphology. This disease acceleration persisted over many weeks and was associated with markedly increased monocyte recruitment. Seeking the source of surplus monocytes in plaques, we found that myocardial infarction liberated haematopoietic stem and progenitor cells from bone marrow niches via sympathetic nervous system signalling. The progenitors then seeded the spleen, yielding a sustained boost in monocyte production. These observations provide new mechanistic insight into atherogenesis and provide a novel therapeutic opportunity to mitigate disease progression.

Polymeric nanoparticle PET/MR imaging allows macrophage detection in atherosclerotic plaques.

RATIONALE: Myeloid cell content in atherosclerotic plaques associates with rupture and thrombosis. Thus, imaging of lesional monocytes and macrophages could serve as a biomarker of disease progression and therapeutic intervention. OBJECTIVE: To noninvasively assess plaque inflammation with dextran nanoparticle (DNP)-facilitated hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). METHODS AND RESULTS: Using clinically approved building blocks, we systematically developed 13-nm polymeric nanoparticles consisting of cross-linked short chain dextrans, which were modified with desferoxamine for zirconium-89 radiolabeling ((89)Zr-DNP) and a near-infrared fluorochrome (VT680) for microscopic and cellular validation. Flow cytometry of cells isolated from excised aortas showed DNP uptake predominantly in monocytes and macrophages (76.7%) and lower signal originating from other leukocytes, such as neutrophils and lymphocytes (11.8% and 0.7%, P<0.05 versus monocytes and macrophages). DNP colocalized with the myeloid cell marker CD11b on immunohistochemistry. PET/MRI revealed high uptake of (89)Zr-DNP in the aortic root of apolipoprotein E knock out (ApoE(-/-)) mice (standard uptake value, ApoE(-/-) mice versus wild-type controls, 1.9±0.28 versus 1.3±0.03; P<0.05), corroborated by ex vivo scintillation counting and autoradiography. Therapeutic silencing of the monocyte-recruiting receptor C-C chemokine receptor type 2 with short-interfering RNA decreased (89)Zr-DNP plaque signal (P<0.05) and inflammatory gene expression (P<0.05). CONCLUSIONS: Hybrid PET/MRI with a 13-nm DNP enables noninvasive assessment of inflammation in experimental atherosclerotic plaques and reports on therapeutic efficacy of anti-inflammatory therapy.

Monocyte-directed RNAi targeting CCR2 improves infarct healing in atherosclerosis-prone mice.

BACKGROUND: Exaggerated and prolonged inflammation after myocardial infarction (MI) accelerates left ventricular remodeling. Inflammatory pathways may present a therapeutic target to prevent post-MI heart failure. However, the appropriate magnitude and timing of interventions are largely unknown, in part because noninvasive monitoring tools are lacking. Here, we used nanoparticle-facilitated silencing of CCR2, the chemokine receptor that governs inflammatory Ly-6C(high) monocyte subset traffic, to reduce infarct inflammation in apolipoprotein E-deficient (apoE(-/-)) mice after MI. We used dual-target positron emission tomography/magnetic resonance imaging of transglutaminase factor XIII (FXIII) and myeloperoxidase (MPO) activity to monitor how monocyte subset-targeted RNAi altered infarct inflammation and healing. METHODS AND RESULTS: Flow cytometry, gene expression analysis, and histology revealed reduced monocyte numbers and enhanced resolution of inflammation in infarcted hearts of apoE(-/-) mice that were treated with nanoparticle-encapsulated siRNA. To follow extracellular matrix cross-linking noninvasively, we developed a fluorine-18-labeled positron emission tomography agent ((18)F-FXIII). Recruitment of MPO-rich inflammatory leukocytes was imaged with a molecular magnetic resonance imaging sensor of MPO activity (MPO-Gd). Positron emission tomography/magnetic resonance imaging detected anti-inflammatory effects of intravenous nanoparticle-facilitated siRNA therapy (75% decrease of MPO-Gd signal; P<0.05), whereas (18)F-FXIII positron emission tomography reflected unimpeded matrix cross-linking in the infarct. Silencing of CCR2 during the first week after MI improved ejection fraction on day 21 after MI from 29% to 35% (P<0.05). CONCLUSION: CCR2-targeted RNAi reduced recruitment of Ly-6C(high) monocytes, attenuated infarct inflammation, and curbed post-MI left ventricular remodeling.

Acoustic Voice and Speech Biomarkers of Treatment Status during Hospitalization for Acute Decompensated Heart Failure.

This study investigates acoustic voice and speech features as biomarkers for acute decompensated heart failure (ADHF), a serious escalation of heart failure symptoms including breathlessness and fatigue. ADHF-related systemic fluid accumulation in the lungs and laryngeal tissues is hypothesized to affect phonation and respiration for speech. A set of daily spoken recordings from 52 patients undergoing inpatient ADHF treatment was analyzed to identify voice and speech biomarkers for ADHF and to examine the trajectory of biomarkers during treatment. Results indicated that speakers produce more stable phonation, a more creaky voice, faster speech rates, and longer phrases after ADHF treatment compared to their pre-treatment voices. This project builds on work to develop a method of monitoring ADHF using speech biomarkers and presents a more detailed understanding of relevant voice and speech features.

Smartphone camera based assessment of adiposity: a validation study.

Body composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. Body mass index (BMI) and other clinical or commercially available tools for quantifying body fat (BF) such as DXA, MRI, CT, and photonic scanners (3DPS) are often inaccurate, cost prohibitive, or cumbersome to use. The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF). The VBC algorithm is based on a state-of-the-art convolutional neural network (CNN). The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. 134 healthy adults ranging in age (21-76 years), sex (61.2% women), race (60.4% White; 23.9% Black), and body mass index (BMI, 18.5-51.6 kg/m2) were evaluated at two clinical sites (N = 64 at MGH, N = 70 at PBRC). Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems. The PBRC participants also had air displacement plethysmography (ADP) measured. %BF measured by dual-energy x-ray absorptiometry (DXA) was set as the reference against which all other %BF measurements were compared. To test our scientific hypothesis we run multiple, pair-wise Wilcoxon signed rank tests where we compare each competing measurement tool (VBC, BIA, …) with respect to the same ground-truth (DXA). Relative to DXA, VBC had the lowest mean absolute error and standard deviation (2.16 ± 1.54%) compared to all of the other evaluated methods (p < 0.05 for all comparisons). %BF measured by VBC also had good concordance with DXA (Lin's concordance correlation coefficient, CCC: all 0.96; women 0.93; men 0.94), whereas BMI had very poor concordance (CCC: all 0.45; women 0.40; men 0.74). Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias -0.42%, R2 = 0.03; p = 0.062; LOA -5.5% to +4.7%), whereas all other evaluated methods had significant (p < 0.01) bias and wider limits of agreement. Bias in Bland-Altman analyses is defined as the discordance between the y = 0 axis and the regressed line computed from the data in the plot. In this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference; VBC performance exceeded those of all other BIA and ADP methods evaluated. The wide availability of smartphones suggests that the VBC method for evaluating %BF could play an important role in quantifying adiposity levels in a wide range of settings.Trial registration: ClinicalTrials.gov Identifier: NCT04854421.

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

Identifying the etiology: a systematic approach using delayed-enhancement cardiovascular magnetic resonance.

In patients who have heart failure, treatment and survival are directly related to the cause. Clinically, as a practical first step, patients are classified as having either ischemic or non-ischemic cardiomyopathy, a delineation usually based on the presence or absence of epicardial coronary artery disease. However, this approach does not account for patients with non-ischemic cardiomyopathy who also have coronary artery disease, which may be either incidental or partly contributing to myocardial dysfunction (mixed cardiomyopathy). By allowing direct assessment of the myocardium, delayed-enhancement cardiovascular magnetic resonance (DE-CMR) may aid in addressing these conundrums. This article explores the use of DE-CMR in identifying ischemic and non-ischemic myopathic processes and details a systematic approach to determine the cause of cardiomyopathy.

Training for our digital future: a human-centered design approach to graduate medical education for aspiring clinician-innovators.

In the current era of value-based healthcare with increasing emphasis on delivering higher quality care at lower costs, US healthcare innovation as a metric is at a premium. However, an implementation gap exists between technology-enabled innovations and patient-centered care secondary to a lack of formal training rooted in implementation science, healthcare operations, and clinical informatics for healthcare providers. We illustrate the application of human-centered design principles with focus on medical trainees as the end-user in a unique approach to developing clinician-innovators best suited to bridge the implementation gap.

Noninvasive Assessment of Jugular Venous Pressure via Force-Coupled Single Crystal Ultrasound.

OBJECTIVE: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). METHODS: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. RESULTS: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg; standing JVP 4.2 ± 1.9 mmHg (p < 0.0001). CONCLUSION: noninvasive assessment of JVP could prove valuable in informing rapid clinical decision-making across various pathologies and conditions leading to derangements in intravascular volume status.

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder.

BACKGROUND: Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR). OBJECTIVE: This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband. METHODS: Measurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. RESULTS: The HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device. CONCLUSIONS: The accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.

Using Digital Health Technology to Better Generate Evidence and Deliver Evidence-Based Care.

As we enter the information age of health care, digital health technologies offer significant opportunities to optimize both clinical care delivery and clinical research. Despite their potential, the use of such information technologies in clinical care and research faces major data quality, privacy, and regulatory concerns. In hopes of addressing both the promise and challenges facing digital health technologies in the transformation of health care, we convened a think tank meeting with academic, industry, and regulatory representatives in December 2016 in Washington, DC. In this paper, we summarize the proceedings of the think tank meeting and aim to delineate a framework for appropriately using digital health technologies in healthcare delivery and research.

A Systems Approach to Healthcare Innovation Using the MIT Hacking Medicine Model.

MIT Hacking Medicine is a student, academic, and community-led organization that uses systems-oriented "healthcare hacking" to address challenges around innovation in healthcare. The group has organized more than 80 events around the world that attract participants with diverse backgrounds. These participants are trained to address clinical needs from the perspective of multiple stakeholders and emphasize utility and implementation viability of proposed solutions. We describe the MIT Hacking Medicine model as a potential method to integrate collaboration and training in rapid innovation techniques into academic medical centers. Built upon a systems approach to healthcare innovation, the time-compressed but expertly guided nature of the events could enable more widely accessible preliminary training in systems-level innovation methodology, as well as creating a structured opportunity for interdisciplinary congregation and collaboration.

The quantified patient of the future: Opportunities and challenges.

The healthcare system is undergoing rapid transformation as national policies increase patient access, reward positive health outcomes, and push for an end to the current era of episodic care. Advances in health sensors are rapidly moving diagnostic and monitoring capabilities into consumer products, enabling new care models. Although hospitals and health care providers have been slow to embrace novel health technologies, such innovations may help meet mounting pressure to provide timely, high quality, and low-cost care to large populations. This leading edge perspective focuses on the quantified-self movement and highlights the opportunities and challenges for patients, providers, and researchers.

Quantification of coronary flow reserve in patients with ischaemic and non-ischaemic cardiomyopathy and its association with clinical outcomes.

AIMS: Patients with left ventricular systolic dysfunction frequently show abnormal coronary vascular function, even in the absence of overt coronary artery disease. Moreover, the severity of vascular dysfunction might be related to the aetiology of cardiomyopathy.We sought to determine the incremental value of assessing coronary vascular dysfunction among patients with ischaemic (ICM) and non-ischaemic (NICM) cardiomyopathy at risk for adverse cardiovascular outcomes. METHODS AND RESULTS: Coronary flow reserve (CFR, stress/rest myocardial blood flow) was quantified in 510 consecutive patients with rest left ventricular ejection fraction (LVEF) ≤45% referred for rest/stress myocardial perfusion PET imaging. The primary end point was a composite of major adverse cardiovascular events (MACE) including cardiac death, heart failure hospitalization, late revascularization, and aborted sudden cardiac death.Median follow-up was 8.2 months. Cox proportional hazards model was used to adjust for clinical variables. The annualized MACE rate was 26.3%. Patients in the lowest two tertiles of CFR (CFR ≤ 1.65) experienced higher MACE rates than those in the highest tertile (32.6 vs. 15.5% per year, respectively, P = 0.004), irrespective of aetiology of cardiomyopathy. CONCLUSION: Impaired coronary vascular function, as assessed by reduced CFR by PET imaging, is common in patients with both ischaemic and non-ischaemic cardiomyopathy and is associated with MACE.

Magnetic ligation method for quantitative detection of microRNAs.

A magnetic ligation method is utilized for the detection of microRNAs among a complex biological background without polymerase chain reaction or nucleotide modification. The sandwich probes assay can be adapted to analyze a panel of microRNAs associated with cardiovascular diseases in heart tissue samples.

Cardiovascular molecular imaging: the road ahead.

Despite significant advancements in medical and device-based therapies, cardiovascular disease remains the number one cause of death in the United States. Early detection of atherosclerosis, prevention of myocardial infarction and sudden cardiac death, and modulation of adverse ventricular remodeling still remain elusive goals. Molecular imaging focuses on identifying critical cellular and molecular targets and therefore plays an integral role in understanding these biologic processes in vivo. Because many imaging targets are upregulated before irreversible tissue damage occurs, early detection could ultimately lead to development of novel, preventive therapeutic strategies. This review addresses recent work on radionuclide imaging of cardiovascular inflammation, infection, and infarct healing. We further discuss opportunities provided by multimodality approaches such as PET/MRI and PET/optical imaging.

Effects of lipid-altering therapies on ventricular arrhythmias and sudden cardiac death.

Sudden cardiac death remains a leading cause of mortality in the United States, with an incidence of 300,000 to 400,000 deaths annually. Despite advances in the management of cardiovascular disease, the only effective treatments proven to reduce the risk of sudden cardiac death are beta-adrenergic blockers and implantable cardioverter-defibrillators. Antiarrhythmic medications are effective at treating symptomatic and asymptomatic ventricular arrhythmias, but several are associated with increased mortality. Although effective at lowering mortality, implantable cardioverter-defibrillators pose an economic burden and some morbidity to patients when associated with frequent shock therapies. Thus, there is renewed interest in developing additional pharmacologic alternatives that could reduce the risk of fatal ventricular arrhythmias. A post hoc analysis of 2 large clinical trials suggested an association between the use of lipid-altering therapy and decreased rates of sudden death. Retrospective review of other clinical trials and experimental data using animal models provide further insight into the potential antiarrhythmic properties of lipid-altering therapy. This review examines the current status of basic science and clinical research that explores the antiarrhythmic properties of lipid-altering therapy, with a focus on 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins) and polyunsaturated fatty acids.