Transcriptional Regulation of Cardiac Remodeling in a Porcine Model with Validation in Human Subjects

Introduction: The majority of new atrial fibrillation (AF) cases occur in elderly patients with cardiac remodeling (CR) in the setting of structural heart disease and heart failure (HF). We leveraged a unique animal model to identify cardiac microRNAs (miRNAs) and gene regulatory mechanisms that drive this process. Methods: We prospectively quantified atrial expression of 48 miRNAs by high-throughput qRT-PCR in 15 pigs with right-atrial pacing-induced heart disease (5 pigs with AF/severe HF, 5 pigs with AF/mild HF, and 5 control pigs) as well as in 21 patients (11 with AF and CR and 10 controls) undergoing cardiac surgery. CR and HF were defined through a metric of left atrial volume index, BNP and ejection fraction. MiRNA levels were normalized to global mean and expression compared across pig subtypes and between the two human groups. Results: In the porcine model, miR-208b was upregulated at week 1 (ΔCT= -3.9, pT = -5.5, pT = -1.5, pT = -1.5, pT = -1.5, pT = -1.5, p\u3c0.05). Conclusions: Dysregulation of miR-208b is confirmed in our porcine model and is validated in humans. Prior studies have identified miR-208b in both myosin isoform switching and conduction disease. We theorize that dysregulation of miR-208b may play a critical role in atrial structural remodeling and vulnerability to AF

Role of TSH and excess Heart Age in Predicting Atrial Fibrillation Recurrence Post-Ablation

Background: The association between atrial fibrillation (AF) and thyroid disease as defined by thyroid stimulating hormone (TSH) is established in literature. However, the relationship between TSH and recurrence of AF post ablation has not been established. Methods: We studied 207 patients (60.54±9.39yrs, 35.7% female) with persistent or paroxysmal AF who underwent either Cryo or RFA ablation between April 2011 and Jan 2015 at our center. Patients were stratified into hypothyroid (TSH \u3e \u3e4.5 U/mL), euthyroid (TSH 0.5-4.5 U/mL) and hyperthyroid (TSH \u3c 0.5 U/mL) based on pre procedure testing. Heart age was computed based on Framingham risk factors. Excess heart age was defined as the difference between actual age and heart age. Logistic regression and cox-proportional hazards model were implemented using R statistical software (v3.2.0). Results: There was a statistically significant lower rate of AF recurrence among male patients (OR 2.92, p=0.003). In univariate analysis, there was no statistically significant relationship between TSH and incidence of AF recurrence (OR 1.05, p=0.74). Cox proportional hazards models did not show an association between recurrence and TSH states (HR 0.85, p=0.74 for hypothyroid and HR 0.75, p=0.56 for hyperthyroid). Conclusions: This exploratory showed that TSH may not play a role in AF recurrence. While there is a tendency towards an association between TSH and AF recurrence, this was not statistically significant. We hypothesize that overt hyperthyroidism prior to ablation will not increase chance of recurrence. This was true after adjustment for Framingham risk factors. The limitation of this study was the small sample size of the patients with TSH in the hyperthyroid range. Further analysis using larger dataset is indicated

Optimal WiFi Sensing via Dynamic Programming

The problem of finding an optimal sensing schedule for a mobile device that encounters an intermittent WiFi access opportunity is considered. At any given time, the WiFi is in any of the two modes, ON or OFF, and the mobile's incentive is to connect to the WiFi in the ON mode as soon as possible, while spending as little sensing energy. We introduce a dynamic programming framework which enables the characterization of an explicit solution for several models, particularly when the OFF periods are exponentially distributed. While the problem for non-exponential OFF periods is ill-posed in general, a usual workaround in literature is to make the mobile device aware if one ON period is completely missed. In this restricted setting, using the DP framework, the deterministic nature of the optimal sensing policy is established, and value iterations are shown to converge to the optimal solution. Finally, we address the blind situation where the distributions of ON and OFF periods are unknown. A continuous bandit based learning algorithm that has vanishing regret (loss compared to the optimal strategy with the knowledge of distributions) is presented, and comparisons with the optimal schemes are provided for exponential ON and OFF times

Predictors of Cardiac Mortality in the CCU: A Retrospective Study in a Tertiary Center

Background: Although prior studies have linked troponin I (TnI) elevation, serum sodium (Na) fluctuation, and reduced ejection fraction (EF) with an increased mortality in the medical/surgical critical care units, this has not been validated in the CCU. We aim to identify clinical and laboratory factors to predict cardiac related length of survival (LOS) in the CCU. Methods: We retrospectively analyzed 134 consecutive patients who were admitted to the CCU from December 2012 to March 2015, and who died during that admission. We used student T-test, correlation matrices, and Framingham risk factors adjusted multivariable logistic regression models to examine the role of TnI, serum Na, EF and other clinical covariates on LOS in cardiac death (CD) and non- cardiac death (NCD) group. Results: The average age of the study population was 70.0 ±14.3 (39.0% women). The prevalence of CD and NCD were 63% and 59%. LOS was statistically shorter in the CD vs. NCD group (5.3 days vs. 8.2 days, p=0.012). LOS negatively correlated with initial TnI (p= 0.05). LOS was not statistically affected by EF or Na level. Our regression models identified BMI and diabetes mellitus (DM) as strong predictors of CD (p= 0.04 and p=0.01). Conclusion: Our results validate prior studies showing that TnI, BMI, and DM are predictors of cardiac related mortality in the CCU. Patients with a cardiac etiology had a higher mortality rate and a shorter LOS. Future studies are needed to develop a scoring system specific for predicting mortality in the CCU

Association of Left Atrial Function Index with Atrial Fibrillation and Cardiovascular Disease: The Framingham Offspring Study

BACKGROUND: Left atrial (LA) size, a marker of atrial structural remodeling, is associated with increased risk for atrial fibrillation (AF) and cardiovascular disease (CVD). LA function may also relate to AF and CVD, irrespective of LA structure. We tested the hypothesis that LA function index (LAFI), an echocardiographic index of LA structure and function, may better characterize adverse LA remodeling and predict incident AF and CVD than existing measures. METHODS AND RESULTS: In 1786 Framingham Offspring Study eighth examination participants (mean age, 66+/-9 years; 53% women), we related LA diameter and LAFI (derived from the LA emptying fraction, left ventricular outflow tract velocity time integral, and indexed maximal LA volume) to incidence of AF and CVD on follow-up. Over a median follow-up of 8.3 years (range, 7.5-9.1 years), 145 participants developed AF and 139 developed CVD. Mean LAFI was 34.5+/-12.7. In adjusted Cox regression models, lower LAFI was associated with higher risk of incident AF (hazard ratio=3.83, 95% confidence interval=2.23-6.59, lowest [Q1] compared with highest [Q4] LAFI quartile) and over 2-fold higher risk of incident CVD (hazard ratio=2.20, 95% confidence interval=1.32-3.68, Q1 versus Q4). Addition of LAFI, indexed maximum LA volume, or LA diameter to prediction models for AF or CVD did not significantly improve model discrimination for either outcome. CONCLUSIONS: In our prospective investigation of a moderate-sized community-based sample, LAFI, a composite measure of LA size and function, was associated with incident AF and CVD. Addition of LAFI to the risk prediction models for AF or CVD, however, did not significantly improve their performance

Relations between plasma microRNAs, echocardiographic markers of atrial remodeling, and atrial fibrillation: Data from the Framingham Offspring study

BACKGROUND: Circulating microRNAs may reflect or influence pathological cardiac remodeling and contribute to atrial fibrillation (AF). OBJECTIVE: The purpose of this study was to identify candidate plasma microRNAs that are associated with echocardiographic phenotypes of atrial remodeling, and incident and prevalent AF in a community-based cohort. METHODS: We analyzed left atrial function index (LAFI) of 1788 Framingham Offspring 8 participants. We quantified expression of 339 plasma microRNAs. We examined associations between microRNA levels with LAFI and prevalent and incident AF. We constructed pathway analysis of microRNAs\u27 predicted gene targets to identify molecular processes involved in adverse atrial remodeling in AF. RESULTS: The mean age of the participants was 66 +/- 9 years, and 54% were women. Five percent of participants had prevalent AF at the initial examination and 9% (n = 157) developed AF over a median 8.6 years of follow-up (IQR 8.1-9.2 years). Plasma microRNAs were associated with LAFI (N = 73, p \u3c 0.0001). Six of these plasma microRNAs were significantly associated with incident AF, including 4 also associated with prevalent AF (microRNAs 106b, 26a-5p, 484, 20a-5p). These microRNAs are predicted to regulate genes involved in cardiac hypertrophy, inflammation, and myocardial fibrosis. CONCLUSIONS: Circulating microRNAs 106b, 26a-5p, 484, 20a-5p are associated with atrial remodeling and AF

Association of Left Atrial Function Index with Atrial Fibrillation and Cardiovascular Disease: The Framingham Offspring Study

Background: Left atrial (LA) size, a marker of atrial structural remodeling, is associated with increased risk for atrial fibrillation (AF) and cardiovascular disease (CVD). LA function may also relate to AF and CVD, irrespective of LA structure. We tested the hypothesis that LA function index (LAFI), an echocardiographic index of LA structure and function, may better characterize adverse LA remodeling and predict incident AF and CVD than existing measures. Methods and Results: In 1786 Framingham Offspring Study eighth examination participants (mean age, 66±9 years; 53% women), we related LA diameter and LAFI (derived from the LA emptying fraction, left ventricular outflow tract velocity time integral, and indexed maximal LA volume) to incidence of AF and CVD on follow‐up. Over a median follow‐up of 8.3 years (range, 7.5–9.1 years), 145 participants developed AF and 139 developed CVD. Mean LAFI was 34.5±12.7. In adjusted Cox regression models, lower LAFI was associated with higher risk of incident AF (hazard ratio=3.83, 95% confidence interval=2.23–6.59, lowest [Q1] compared with highest [Q4] LAFI quartile) and over 2‐fold higher risk of incident CVD (hazard ratio=2.20, 95% confidence interval=1.32–3.68, Q1 versus Q4). Addition of LAFI, indexed maximum LA volume, or LA diameter to prediction models for AF or CVD did not significantly improve model discrimination for either outcome. Conclusions: In our prospective investigation of a moderate‐sized community‐based sample, LAFI, a composite measure of LA size and function, was associated with incident AF and CVD. Addition of LAFI to the risk prediction models for AF or CVD, however, did not significantly improve their performance

Data modeling and handling for analysis and visualization in a collaborative setting

This paper discusses the development of a data modeling and handling methodology to display results from a large-scale Finite Element Analysis in real-time from any geographic location in the world to aid in complex decision-making. The developed methodology enables real-time collaboration before, during, and after a complex engineering analysis. The collaborative capabilities include a three dimensional, interactive representation of the analysis data available through the Internet on any computing platform without the need of installed software or specialized hardware. A scientist has the ability to change data resolutions on-the-fly as well as view animated representations of the analysis results. In this paper, the developed methodology was applied to a geophysical situation. However, the benefits could be realized in a range of application areas from mechanical design to biomedical imaging. The details of the development are presented in this paper. The full paper will present additional descriptions as well as example problems.This is a conference proceeding from 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (2004): AIAA 2004-4603, doi: 10.2514/6.2004-4603. Posted with permission.</p