Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

Impact of Insurance Status and Region on Angiotensin Receptor-Neprilysin Inhibitor Prescription During Heart Failure Hospitalizations.

BACKGROUND: An angiotensin receptor-neprilysin inhibitor (ARNI) is the preferred renin-angiotensin system (RAS) inhibitor for heart failure with reduced ejection fraction (HFrEF). Among eligible patients, insurance status and prescriber concern regarding out-of-pocket costs may constrain early initiation of ARNI and other new therapies. OBJECTIVES: In this study, the authors sought to evaluate the association of insurance and other social determinants of health with ARNI initiation at discharge from HFrEF hospitalization. METHODS: The authors analyzed ARNI initiation from January 2017 to June 2020 among patients with HFrEF eligible to receive RAS inhibitor at discharge from hospitals in the Get With The Guidelines-Heart Failure registry. The primary outcome was the proportion of ARNI prescription at discharge among those prescribed RAS inhibitor who were not on ARNI on admission. A logistic regression model was used to determine the association of insurance status, U.S. region, and their interaction, as well as self-reported race, with ARNI initiation at discharge. RESULTS: From 42,766 admissions, 24,904 were excluded for absolute or relative contraindications to RAS inhibitors. RAS inhibitors were prescribed for 16,817 (94.2%) of remaining discharges, for which ARNI was prescribed in 1,640 (9.8%). Self-reported Black patients were less likely to be initiated on ARNI compared to self-reported White patients (OR: 0.64; 95% CI: 0.50-0.81). Compared to Medicare beneficiaries, patients with third-party insurance, Medicaid, or no insurance were less likely to be initiated on ARNI (OR: 0.47 [95% CI: 0.31-0.72], OR: 0.41 [95% CI: 0.25-0.67], and OR: 0.20 [95% CI: 0.08-0.47], respectively). ARNI therapy varied by hospital region, with lowest utilization in the Mountain region. An interaction was demonstrated between the impact of insurance disparities and hospital region. CONCLUSIONS: Among patients hospitalized between 2017 and 2020 for HFrEF who were prescribed RAS inhibitor therapy at discharge, insurance status, geographic region, and self-reported race were associated with ARNI initiation.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administration raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combing data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest a similar structure of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying complex disease interactions. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared etiology of diseases. The consistent core-periphery network structure offers a strategic approach to analyze disease clusters. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, UL1 TR002243, R21DK127075, R01HL140074, P50GM115305, R01CA227481.

Disparities in Metabolic Syndrome and Neurocognitive Function Among Older Hispanics/Latinos with Human Immunodeficiency Virus.

Neurocognitive impairment and metabolic syndrome (MetS) are prevalent in persons with HIV (PWH). We examined disparities in HIV-associated neurocognitive function between Hispanic and non-Hispanic White older PWH, and the role of MetS in explaining these disparities. Participants included 116 community-dwelling PWH aged 50-75 years enrolled in a cohort study in southern California [58 Hispanic (53% Spanish speaking) and 58 age-comparable non-Hispanic White; overall group: age: M = 57.9, standard deviation (SD) = 5.7; education (years): M = 13, SD = 3.4; 83% male, 58% AIDS, 94% on antiretroviral therapy]. Global neurocognition was derived from T-scores adjusted for demographics (age, education, sex, ethnicity, language) on a battery of 10 cognitive tests. MetS was ascertained via standard criteria that considered central obesity, and fasting elevated triglycerides, low high-density lipoprotein cholesterol and elevated glucose, or medical treatment for these conditions. Covariates examined included sociodemographic, psychiatric, substance use and HIV disease characteristics. Compared with non-Hispanic Whites, Hispanics showed worse global neurocognitive function (Cohen's d = 0.56, p < 0.05) and had higher rates of MetS (38% vs. 56%, p < 0.05). A stepwise regression model including ethnicity and significant covariates showed Hispanic ethnicity was the sole significant predictor of worse global neurocognition (B = -3.82, SE = 1.27, p < 0.01). A model also including MetS showed that both Hispanic ethnicity (B = -3.39, SE = 1.31, p = 0.01) and MetS (B = -2.73, SE = 1.31, p = 0.04) were independently associated with worse neurocognition. In conclusion, findings indicate that increased MetS is associated with worse neurocognitive function in both Hispanic and non-Hispanic White older PWH, but does not explain neurocognitive disparities. MetS remains an important target for intervention efforts to ameliorate neurocognitive dysfunction among diverse older PWH.

Large language models facilitate the generation of electronic health record phenotyping algorithms.

OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

Leptomeningeal Dissemination Complicated With Acute Tetraplegia From a Supratentorial Multicentric Isocitrate Dehydrogenase (IDH)-Wildtype Glioblastoma: A Case Report.

Glioblastoma (GBM) is a major concern for neurosurgeons and oncologists, being a malignant tumor with a high recurrence rate and reduced survival. Leptomeningeal dissemination (LMD) of GBM is rare and difficult to diagnose due to the low rate of cellular detection in the cerebrospinal fluid and clinical and imaging similarities with fungal and tuberculous meningitis. We report the case of a 25-year-old female patient suffering from multicentric GBM who developed hydrocephalus and extensive LMD three months after surgery for a left frontal parafalcine cerebral GBM isocitrate dehydrogenase (IDH)-wildtype.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

The electrophysiologic effects of KCNQ1 extend beyond expression of IKs: evidence from genetic and pharmacologic block.

AIMS: While variants in KCNQ1 are the commonest cause of the congenital long QT syndrome, we and others find only a small IKs in cardiomyocytes from human induced pluripotent stem cells (iPSC-CMs) or human ventricular myocytes. METHODS AND RESULTS: We studied population control iPSC-CMs and iPSC-CMs from a patient with Jervell and Lange-Nielsen (JLN) syndrome due to compound heterozygous loss of function KCNQ1 variants. We compared the effects of pharmacologic IKs block to those of genetic KCNQ1 ablation, using JLN cells, cells homozygous for the KCNQ1 loss of function allele G643S, or siRNAs reducing KCNQ1 expression. We also studied the effects of two blockers of IKr, the other major cardiac repolarizing current, in the setting of pharmacologic or genetic ablation of KCNQ1: moxifloxacin, associated with a very low risk of drug-induced long QT, and dofetilide, a high-risk drug.In control cells, a small IKs was readily recorded but pharmacologic IKs block produced no change in action potential duration at 90% repolarization (APD90). By contrast, in cells with genetic ablation of KCNQ1 (JLN), baseline APD90 was markedly prolonged compared with control cells (469 ± 20 vs. 310 ± 16 ms). JLN cells displayed increased sensitivity to acute IKr block: the concentration (µM) of moxifloxacin required to prolong APD90 100 msec was 237.4 (median, IQR 100.6-391.6, n = 7) in population cells versus 23.7 (17.3-28.7, n = 11) in JLN cells. In control cells, chronic moxifloxacin exposure (300µM) mildly prolonged APD90 (10%) and increased IKs, while chronic exposure to dofetilide (5 nM) produced greater prolongation (67%) and no increase in IKs. However, in the siRNA-treated cells, moxifloxacin did not increase IKs, and markedly prolonged APD90. CONCLUSION: Our data strongly suggest that KCNQ1 expression modulates baseline cardiac repolarization, and the response to IKr block, through mechanisms beyond simply generating IKs. TRANSLATIONAL PERSPECTIVE: Mutations in KCNQ1 - whose expression generates IKs - are the major cause of long QT syndrome. We report here that while pharmacologic IKs block in human cardiomyocytes generates minimal change in repolarization, suppressing KCNQ1 expression markedly increases both baseline repolarization duration and sensitivity to some (but not all) specific IKr blockers. Thus, beyond simply generating IKs, KCNQ1 subserves critical additional role(s) in repolarization control at baseline and in response to IKr block. Our findings imply that assessment of arrhythmic risk in individual patients and by drugs requires a framework that extends beyond a simple one gene-one ion current paradigm.

Levels of evidence for human system risk evaluation.

NASA uses a continuous risk management process to seek out new knowledge of spaceflight-induced risk to human health and performance. The evidence base that informs the risk assessments in this domain is constantly changing as more information is gleaned from a continuous human presence in space and from ongoing research. However, the limitations of this evidence are difficult to characterize because fewer than 700 humans have ever flown in space, and information comes from a variety of sources that span disciplines, including engineering, medicine, food and nutrition, and many other life sciences. The Human System Risk Board (HSRB) at NASA is responsible for assessing risk to astronauts and communicating this risk to agency decision-makers. A critical part of that communication is conveying the uncertainty regarding the understanding of the changes that spaceflight induces in human processes and the complex interactions between humans and the spacecraft. Although the strength of evidence grades is common in the academic literature, these scores are often not useful for the problems of human spaceflight. The HSRB continues to update the processes used to report the levels of evidence. This paper describes recent updates to the methods used to assign the level of evidence scores to the official risk postures and to the causal diagrams used by the HSRB.

The Association Between Physical Activity and Cognition in a Racially/Ethnically Diverse Cohort of Older Adults: Results From the Kaiser Healthy Aging and Diverse Life Experiences Study.

OBJECTIVE: Most prior research on physical activity (PA) and cognition is based on predominantly white cohorts and focused on associations of PA with mean (average) cognition versus the distribution of cognition. Quantile regression offers a novel way to quantify how PA affects cognition across the entire distribution. METHODS: The Kaiser Healthy Aging and Diverse Life Experiences study includes 30% white, 19% black, 25% Asian, and 26% Latinx adults age 65+ living in Northern California (n = 1600). The frequency of light or heavy PA was summarized as 2 continuous variables. Outcomes were z-scored executive function, semantic memory, and verbal episodic memory. We tested associations of PA with mean cognition using linear regression and used quantile regression to estimate the association of PA with the 10th-90th percentiles of cognitive scores. RESULTS: Higher levels of PA were associated with higher mean semantic memory (b = 0.10; 95% CI: 0.06, 0.14) and executive function (b = 0.05; 95% CI: 0.01, 0.09). Associations of PA across all 3 cognitive domains were stronger at low quantiles of cognition. CONCLUSION: PA is associated with cognition in this racially/ethnically diverse sample and may have larger benefits for individuals with low cognitive scores, who are most vulnerable to dementia.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, Setting, and Participants: In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure: Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main Outcome and Measures: Incident obesity (BMI ≥30). Results: A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and Relevance: In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma.

BACKGROUND: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. METHODS: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. RESULTS: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. CONCLUSIONS: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Impact of the Timing of Mechanical Circulatory Support on the Outcomes in Myocardial Infarction-Related Cardiogenic Shock: Subanalysis of the PREPARE CS Registry.

(1) Background: Mechanical circulatory support (MCS) in myocardial infarction-associated cardiogenic shock is subject to debate. This analysis aims to elucidate the impact of MCS's timing on patient outcomes, based on data from the PREPARE CS registry. (2) Methods: The PREPARE CS prospective registry includes patients who experienced cardiogenic shock (SCAI classes C-E) and were subsequently referred for cardiac catheterization. Our present analysis included a subset of this registry, in whom MCS was used and who underwent coronary intervention due to myocardial infarction. Patients were categorized into an Upfront group and a Procedural group, depending on the timing of MCS's introduction in relation to their PCI. The endpoint was in-hospital mortality. (3) Results: In total, 71 patients were included. MCS was begun prior to PCI in 33 (46%) patients (Upfront), whereas 38 (54%) received MCS during or after the initiation of PCI (Procedural). The groups' baseline characteristics and hemodynamic parameters were comparable. The Upfront group had a higher utilization of the Impella® device compared to extracorporeal membrane oxygenation (67% vs. 33%), while the Procedural group exhibited a balanced use of both (50% vs. 50%). Most patients suffered from multi-vessel disease in both groups (82% vs. 84%, respectively; p = 0.99), and most patients required a complex PCI procedure; the latter was more prevalent in the Upfront group (94% vs. 71%, respectively; p = 0.02). Their rates of complete revascularization were comparable (52% vs. 34%, respectively; p = 0.16). Procedural CPR was significantly more frequent in the Procedural group (45% vs. 79%, p < 0.05); however, in-hospital mortality was similar (61% vs. 79%, respectively; p = 0.12). (4) Conclusions: The upfront implantation of MCS in myocardial infarction-associated CS did not provide an in-hospital survival benefit.

Impact of controlled hypothermic preservation on outcomes following heart transplantation.

BACKGROUND: Severe primary graft dysfunction (PGD) is a major cause of early mortality after heart transplant, but the impact of donor organ preservation conditions on severity of PGD and survival has not been well characterized. METHODS: Data from US adult heart-transplant recipients in the Global Utilization and Registry Database for Improved Heart Preservation-Heart Registry (NCT04141605) were analyzed to quantify PGD severity, mortality, and associated risk factors. The independent contributions of organ preservation method (traditional ice storage vs controlled hypothermic preservation) and ischemic time were analyzed using propensity matching and logistic regression. RESULTS: Among 1,061 US adult heart transplants performed between October 2015 and December 2022, controlled hypothermic preservation was associated with a significant reduction in the incidence of severe PGD compared to ice (6.6% [37/559] vs 10.4% [47/452], p = 0.039). Following propensity matching, severe PGD was reduced by 50% (6.0% [17/281] vs 12.1% [34/281], respectively; p = 0.018). The Kaplan-Meier terminal probability of 1-year mortality was 4.2% for recipients without PGD, 7.2% for mild or moderate PGD, and 32.1%, for severe PGD (p < 0.001). The probability of severe PGD increased for both cohorts with longer ischemic time, but donor hearts stored on ice were more likely to develop severe PGD at all ischemic times compared to controlled hypothermic preservation. CONCLUSIONS: Severe PGD is the deadliest complication of heart transplantation and is associated with a 7.8-fold increase in probability of 1-year mortality. Controlled hypothermic preservation significantly attenuates the risk of severe PGD and is a simple yet highly effective tool for mitigating post-transplant morbidity.

Recurrence After Atrial Fibrillation Ablation and Investigational Biomarkers of Cardiac Remodeling.

BACKGROUND: Recurrence after atrial fibrillation (AF) ablation remains common. We evaluated the association between recurrence and levels of biomarkers of cardiac remodeling, and their ability to improve recurrence prediction when added to a clinical prediction model. METHODS AND RESULTS: Blood samples collected before de novo catheter ablation were analyzed. Levels of bone morphogenetic protein-10, angiopoietin-2, fibroblast growth factor-23, insulin-like growth factor-binding protein-7, myosin-binding protein C3, growth differentiation factor-15, interleukin-6, N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were measured. Recurrence was defined as ≥30 seconds of an atrial arrhythmia 3 to 12 months postablation. Multivariable logistic regression was performed using biomarker levels along with clinical covariates: APPLE score (Age >65 years, Persistent AF, imPaired eGFR [<60 ml/min/1.73m2], LA diameter ≥43 mm, EF <50%; which includes age, left atrial diameter, left ventricular ejection fraction, persistent atrial fibrillation, and estimated glomerular filtration rate), preablation rhythm, sex, height, body mass index, presence of an implanted continuous monitor, year of ablation, and additional linear ablation. A total of 1873 participants were included. A multivariable logistic regression showed an association between recurrence and levels of angiopoietin-2 (odds ratio, 1.08 [95% CI, 1.02-1.15], P=0.007) and interleukin-6 (odds ratio, 1.02 [95% CI, 1.003-1.03]; P=0.02). The area under the receiver operating characteristic curve of a model that only contained clinical predictors was 0.711. The addition of any of the 9 studied biomarkers to the predictive model did not result in a statistically significant improvement in the area under the receiver operating characteristic curve. CONCLUSIONS: Higher angiopoietin-2 and interleukin-6 levels were associated with recurrence after atrial fibrillation ablation in multivariable modeling. However, the addition of biomarkers to a clinical prediction model did not significantly improve recurrence prediction.

Thoracoabdominal Wall Motion-Guided Biofeedback Treatment of Abdominal Distension: A Randomized Placebo-Controlled Trial.

BACKGROUND & AIMS: Abdominal distention results from abdominophrenic dyssynergia (ie, diaphragmatic contraction and abdominal wall relaxation) in patients with disorders of the gut-brain interaction. This study aimed to validate a simple biofeedback procedure, guided by abdominothoracic wall motion, for treating abdominal distension. METHODS: In this randomized, parallel, placebo-controlled trial, 42 consecutive patients (36 women and 6 men; ages 17-64 years) with meal-triggered visible abdominal distension were recruited. Recordings of abdominal and thoracic wall motion were obtained using inductance plethysmography via adaptable belts. The signal was shown to patients in the biofeedback group, who were taught to mobilize the diaphragm. In contrast, the signal was not shown to the patients in the placebo group, who were given a placebo capsule. Three sessions were performed over a 4-week intervention period, with instructions to perform exercises (biofeedback group) or to take placebo 3 times per day (control group) at home. Outcomes were assessed through response to an offending meal (changes in abdominothoracic electromyographic activity and girth) and clinical symptoms measured using daily scales for 7 days. RESULTS: Patients in the biofeedback group (n = 19) learned to correct abdominophrenic dyssynergia triggered by the offending meal (intercostal activity decreased by a mean ± SE of 82% ± 10%, anterior wall activity increased by a mean ± SE of 97% ± 6%, and increase in girth was a mean ± SE of 108% ± 4% smaller) and experienced improved clinical symptoms (abdominal distension scores decreased by a mean ± SE of 66% ± 5%). These effects were not observed in the placebo group (all, P < .002). CONCLUSIONS: Abdominothoracic wall movements serve as an effective biofeedback signal for correcting abdominophrenic dyssynergia and abdominal distention in patients with disorders of the gut-brain interaction. ClincialTrials.gov, Number: NCT04043208.

Outcomes in Heart Transplant Recipients by Bridge to Transplant Strategy When Using the SherpaPak Cardiac Transport System.

The last several years have seen a rise in use of mechanical circulatory support (MCS) to bridge heart transplant recipients. A controlled hypothermic organ preservation system, the SherpaPak Cardiac Transport System (SCTS), was introduced in 2018 and has grown in utilization with reports of improved posttransplant outcomes. The Global Utilization And Registry Database for Improved heArt preservatioN (GUARDIAN)-Heart registry is an international, multicenter registry assessing outcomes after transplant using the SCTS. This analysis examines outcomes in recipients bridged with various MCS devices in the GUARDIAN-Heart Registry. A total of 422 recipients with donor hearts transported using SCTS were included and identified. Durable ventricular assist devices (VADs) were used exclusively in 179 recipients, temporary VADs or intra-aortic balloon pump (IABP) in 197, and extracorporeal membrane oxygenation (ECMO) in 14 recipients. Average ischemic times were over 3.5 hours in all cohorts. Severe primary graft dysfunction (PGD) posttransplant increased across groups (4.5% VAD, 5.1% temporary support, 21.4% ECMO), whereas intensive care unit (ICU) length of stay (18.2 days) and total hospital stay (39.4 days) was longer in the ECMO cohort than the VAD and IABP groups. A comparison of outcomes of MCS bridging in SCTS versus traditional ice revealed significantly lower rates of both moderate/severe right ventricular (RV) dysfunction and severe PGD in the SCTS cohort; however, upon propensity matching only the reductions in moderate/severe RV dysfunction were statistically significant. Use of SCTS in transplant recipients with various bridging strategies results in excellent outcomes.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.