Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization

Most current methods for modeling rehospitalization events in heart failure patients make use of only clinical and medications data that is available in the electronic health records. However, information about patient-reported functional limitations, behavioral variables and socio-economic background of patients may also play an important role in predicting the risk of readmission in heart failure patients. We developed methods for predicting the risk of rehospitalization in heart failure patients using models that integrate clinical characteristics with patient-reported functional limitations, behavioral and socio-economic characteristics. Our goal was to estimate the predictive accuracy of the joint model and compare it with models that make use of clinical data alone or behavioral and socio-economic characteristics alone, using real patient data. We collected data about the occurrence of hospital readmissions from a cohort of 789 heart failure patients for whom a range of clinical and behavioral characteristics data is also available. We applied the Cox model, four different variants of the Cox proportional hazards framework as well as an alternative non-parametric approach and determined the predictive accuracy for different categories of variables. The concordance index obtained from the joint prediction model including all types of variables was significantly higher than the accuracy obtained from using only clinical factors or using only behavioral, socioeconomic background and functional limitations in patients as predictors. Collecting information on behavior, patient-reported estimates of physical limitations and frailty and socio-economic data has significant value in the predicting the risk of readmissions with regards to heart failure events and can lead to substantially more accurate events prediction models

Selecting optimal partitioning schemes for phylogenomic datasets

BACKGROUND Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. METHODS We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. RESULTS We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. CONCLUSIONS These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.RL would like to acknowledge support from a National Evolutionary Synthesis Centre (NESCent) short-term visitor grant. We would also like to acknowledge support from NESCent to pay for open-access publishing

Pharmacogenomics in Heart Failure: Where Are We Now and How Can We Reach Clinical Application?

Heart failure is becoming increasingly prevalent in the United States and is a significant cause of morbidity and mortality. Several therapies are currently available to treat this chronic illness; however, clinical response to these treatment options exhibit significant interpatient variation. It is now clearly understood that genetics is a key contributor to diversity in therapeutic response, and evidence that genetic polymorphisms alter the pharmacokinetics, pharmacodynamics, and clinical response of heart failure drugs continues to accumulate. This suggests that pharmacogenomics has the potential to help clinicians improve the management of heart failure by choosing the safest and most effective medications and doses. Unfortunately, despite much supportive data, pharmacogenetic optimization of heart failure treatment regimens is not yet a reality. In order to attenuate the rising burden of heart failure, particularly in the context of the recent paucity of new effective interventions, there is an urgent need to extend pharmacogenetic knowledge and leverage these associations in order to enhance the effectiveness of existing heart failure therapies. The present review focuses on the current state of pharmacogenomics in heart failure and provides a glimpse of the aforementioned future needs

Short and long term outcomes of 200 patients supported by continuous-flow left ventricular assist devices

AIM: To study the institutional experience over 8 years with 200 continuous-flow (CF) - left ventricular assist devices (LVAD). METHODS: We evaluated our institution\u27s LVAD database and analyzed all patients who received a CF LVAD as a bridge to transplant (BTT) or destination therapy from March 2006 until June 2014. We identified 200 patients, of which 179 were implanted with a HeartMate II device (Thoratec Corp., Pleasanton, CA) and 21 received a Heartware HVAD (HeartWare Inc., Framingham, MA). RESULTS: The mean age of our LVAD recipients was 59.3 years (range 17-81), 76% (152/200) were males, and 49% were implanted for the indication of BTT. The survival rate for our LVAD patients at 30 d, 6 mo, 12 mo, 2 years, 3 years, and 4 years was 94%, 86%, 78%, 71%, 62% and 45% respectively. The mean duration of LVAD support was 581 d (range 2-2595 d). Gastrointestinal bleeding (was the most common adverse event (43/200, 21%), followed by right ventricular failure (38/200, 19%), stroke (31/200, 15%), re exploration for bleeding (31/200, 15%), ventilator dependent respiratory failure (19/200, 9%) and pneumonia (15/200, 7%). Our driveline infection rate was 7%. Pump thrombosis occurred in 6% of patients. Device exchanged was needed in 6% of patients. On multivariate analysis, preoperative liver dysfunction, ventilator dependent respiratory failure, tracheostomy and right ventricular failure requiring right ventricular assist device support were significant predictors of post LVAD survival. CONCLUSION: Short and long term survival for patients on LVAD support are excellent, although outcomes still remain inferior compared to heart transplantation. The incidence of driveline infections, pump thrombosis and pump exchange have declined significantly in recent years

Factors influencing patient willingness to participate in genetic research after a myocardial infarction

Abstract Background Achieving 'personalized medicine' requires enrolling representative cohorts into genetic studies, but patient self-selection may introduce bias. We sought to identify characteristics associated with genetic consent in a myocardial infarction (MI) registry. Methods We assessed correlates of participation in the genetic sub-study of TRIUMPH, a prospective MI registry (n = 4,340) from 24 US hospitals between April 2005 and December 2008. Factors examined included extensive socio-demographics factors, clinical variables, and study site. Predictors of consent were identified using hierarchical modified Poisson regression, adjusting for study site. Variation in consent rates across hospitals were quantified by the median rate ratio (MRR). Results Most subjects consented to donation of their genetic material (n = 3,484; 80%). Participation rates varied greatly between sites, from 40% to 100%. After adjustment for confounding factors, the MRR for hospital was 1.22 (95% confidence interval (CI) 1.11 to 1.29). The only patient-level factors associated with consent were race (RR 0.93 for African Americans versus whites, 95% CI 0.88 to 0.99) and body mass index (RR 1.03 for BMI ≥ 25, 95% CI 1.01 to 1.06). Conclusion Among patients with an MI there were notable differences in genetic consent by study site, but little association with patient-level factors. This suggests that variation in the way information is presented during recruitment, or other site factors, strongly influence patients' decision to participate in genetic studies.Peer Reviewe

Right Ventricular Failure Following Left Ventricular Assist Device Implant: An Intermacs Analysis

Purpose: Right heart failure (RHF) management following LVAD include inotropes, right ventricular mechanical support and heart transplant. We analyzed the outcomes of severe RHF following implant of a fully magnetically levitated or hybrid magnetic centrifugal durable LVAD. Methods: In this INTERMACS analysis we identified patients who developed severe RHF following LVAD from 2013 until 2020 as bridge to recovery or transplant. Patients were categorized in three groups based on RHF treatment strategy: inotrope support (group 1), temporary mechanical support (group 2), and durable centrifugal RVAD (group 3). Kaplan Meier and Cox-regression survival analysis between groups was undertaken. Logistic regression analysis for new onset dialysis was conducted. Results: 2509 patients developed severe RHF after LVAD. 2199 (87.6%) patients were managed with inotropes (group 1), 233 (9.3%) with temporary RVAD (group 2) and 77 (3.1%) with durable RVAD (group 3). Group 1 had fewer patients with INTERMACS profile 1 and 2 (21.6%, p\u3c0.001). One year survival was 84.6%, 59.3%, and 63.8% in groups 1,2, and 3 (mortality HR=2.4 and 3.3 for groups 2 and 3 vs. group 1, p\u3c0.05). One year survival to transplant was 27%, 36.5%, and 53.6% in groups 1, 2, and 3, respectively (p\u3c0.05). Group 2 had higher incidence of new onset dialysis (42.6%, p=0.049). Conclusion: Survival with RHF following LVAD implant varies based on treatment strategy; inotrope support is associated with increased survival. Patients with durable RVAD are more likely to survive to transplant. Patient selection studies for durable RVAD with contraindications for transplant are necessary

VALIDATION OF POLYGENIC SCORE FOR BETA-BLOCKER SURVIVAL BENEFIT IN HEART FAILURE USING THE UNITED KINGDOM BIOBANK

Background: A novel polygenic response predictor (PRP) for beta blocker (BB) survival benefit in heart failure (HF) was recently described which separated European ancestry BB responders from non-responders using a score derived from 44 genetic loci. We tested whether this would replicate in the United Kingdom Biobank (UKB) dataset. Methods: UKB data pull identified patients with a HF diagnosis, genetic data and prescription data. Ejection fraction (EF) data was not available. BB exposure was quantified using BB dose and prescription frequency. The PRP was calculated using the genetic loci, weights, and cutoff value from the original description. Cox models were constructed of time to all-cause mortality adjusted for clinical risk (MAGGIC score), BB propensity score, BB exposure and BB exposure*PRP interaction. Results: Among 7502 HF patients included, 34% were women, 54% had coronary disease, 33% atrial fibrillation, 51% baseline BB usage, and 22% (n=1651) were PRP-predicted responders. Patients in the PRP responder group had strong survival benefit associated with BB exposure (HR=0.55, p=0.016), while PRP non-responders showed little BB effect (HR=0.92, p=0.466) and this difference was significant (p-interaction =0.051). Survival curves by PRP group and dichotomized BB exposure (high vs. low) are shown in the figure. Conclusion: The polygenic BB response predictor replicated in HF patients from the UKB regardless of EF. This innovative genomic medicine tool requires testing in a clinical trial

Genetics of heart rate in heart failure patients (GenHRate)

BACKGROUND: Elevated resting heart rate (HR) is a risk factor and therapeutic target in patients with heart failure (HF) and reduced ejection fraction (HFrEF). Previous studies indicate a genetic contribution to HR in population samples but there is little data in patients with HFrEF. METHODS: Patients who met Framingham criteria for HF and had an ejection fraction \u3c 50% were prospectively enrolled in a genetic HF registry (2007-2015, n = 1060). All participants donated blood for DNA and underwent genome-wide genotyping with additional variants called via imputation. We performed testing of previously identified variant hits (43 loci) as well as a genome-wide association (GWAS) of HR, adjusted for race, using Efficient Mixed-Model Association Expedited (EMMAX). RESULTS: The cohort was 35% female, 51% African American, and averaged 68 years of age. There was a 2 beats per minute (bpm) difference in HR by race, AA being slightly higher. Among 43 candidate variants, 4 single nucleotide polymorphisms (SNPs) in one gene (GJA1) were significantly associated with HR. In genome-wide testing, one statistically significant association peak was identified on chromosome 22q13, with strongest SNP rs535263906 (p = 3.3 x 10(-8)). The peak is located within the gene Cadherin EGF LAG Seven-Pass G-Type Receptor 1 (CELSR1), encoding a cadherin super-family cell surface protein identified in GWAS of other phenotypes (e.g., stroke). The highest associated SNP was specific to the African American population. CONCLUSIONS: These data confirm GJA1 association with HR in the setting of HFrEF and identify novel candidate genes for HR in HFrEF patients, particularly CELSR1. These associations should be tested in additional cohorts