Redefining the molecular rejection states in 3230 heart transplant biopsies: Relationships to parenchymal injury and graft survival.

The first-generation Molecular Microscope (MMDx) system for heart transplant endomyocardial biopsies used expression of rejection-associated transcripts (RATs) to diagnose not only T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) but also acute injury. However, the ideal system should detect rejection without being influenced by injury, to permit analysis of the relationship between rejection and parenchymal injury. To achieve this, we developed a new rejection classification in an expanded cohort of 3230 biopsies: 1641 from INTERHEART (ClinicalTrials.gov NCT02670408), plus 1589 service biopsies added to improve the power of the machine learning algorithms. The new system used 6 rejection classifiers instead of RATs and generated 7 rejection archetypes: No rejection, 48%; Minor, 24%; TCMR1, 2.3%; TCMR2, 2.7%; TCMR/mixed, 2.7%; early-stage ABMR, 3.9%; and fully developed ABMR, 16%. Using rejection classifiers eliminated cross-reactions with acute injury, permitting separate assessment of rejection and injury. TCMR was associated with severe-recent injury and late atrophy-fibrosis and rarely had normal parenchyma. ABMR was better tolerated, seldom producing severe injury, but in later biopsies was often associated with atrophy-fibrosis, indicating long-term risk. Graft survival and left ventricular ejection fraction were reduced not only in hearts with TCMR but also in hearts with severe-recent injury and atrophy-fibrosis, even without rejection.

Molecular states associated with dysfunction and graft loss in heart transplants.

BACKGROUND: We explored the changes in gene expression correlating with dysfunction and graft failure in endomyocardial biopsies. METHODS: Genome-wide microarrays (19,462 genes) were used to define mRNA changes correlating with dysfunction (left ventricular ejection fraction [LVEF] ≤ 55) and risk of graft loss within 3 years postbiopsy. LVEF data was available for 1,013 biopsies and survival data for 779 patients (74 losses). Molecular classifiers were built for predicting dysfunction (LVEF ≤ 55) and postbiopsy 3-year survival. RESULTS: Dysfunction is correlated with dedifferentiation-decreased expression of normal heart transcripts, for example, solute carriers, along with increased expression of inflammation genes. Many genes with reduced expression in dysfunction were matrix genes such as fibulin 1 and decorin. Gene ontology (GO) categories suggested matrix remodeling and inflammation, not rejection. Genes associated with the risk of failure postbiopsy overlapped dysfunction genes but also included genes affecting microcirculation, for example, arginase 2, which reduces NO production, and endothelin 1. GO terms also reflected increased glycolysis and response to hypoxia, but decreased VEGF and angiogenesis pathways. T cell-mediated rejection was associated with reduced survival and antibody-mediated rejection with relatively good survival, but the main determinants of survival were features of parenchymal injury. Both dysfunction and graft loss were correlated with increased biopsy expression of BNP (gene NPPB). Survival probability classifiers divided hearts into risk quintiles, with actuarial 3-year postbiopsy survival >95% for the highest versus 50% for the lowest. CONCLUSIONS: Dysfunction in transplanted hearts reflects dedifferentiation, decreased matrix genes, injury, and inflammation. The risk of short-term loss includes these changes but is also associated with microcirculation abnormalities, glycolysis, and response to hypoxia.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated With Heart Failure With Improved Ejection Fraction.

Heart failure with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced EF (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using International Classification of Diseases codes, echocardiographic data, and natriuretic peptide levels. The main end points were HFimpEF (defined as EF >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. The study included 1,307 patients with HFrEF with a median follow-up of 16.3 months (interquartile range 8.0 to 30.6). The median age was 65 years; 68% were male whereas 57% were White. On follow-up, 38.7% (n = 506) developed HFimpEF, whereas 61.3% (n = 801) had persistent HFrEF. A multivariate Cox regression model identified gender, race, co-morbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF (p <0.05). The HFimpEF group had better survival compared with the persistent HFrEF group (p <0.001). Echocardiographic and laboratory trajectories differed between groups. In this HFrEF cohort, 38.7% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, gender, co-morbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated with Heart Failure with Improved Ejection Fraction.

Background: Heart failure (HF) with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced ejection fraction (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. Methods: This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using ICD codes, echocardiographic data, and natriuretic peptide levels. The main endpoints were HFimpEF (defined as ejection fraction >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. Results: The study included 1307 HFrEF patients with a median follow-up of 16.3 months (IQR 8.0-30.6). The median age was 65 years; 68% were male while 57% were white. On follow-up, 39% (n=506) developed HFimpEF, while 61% (n=801) had persistent HFrEF. A multivariate Cox regression model identified sex, race comorbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF ( p <0.05). The HFimpEF group had better survival compared to the persistent HFrEF group ( p <0.001). Echocardiographic and laboratory trajectories differed between groups. Conclusion: In this HFrEF cohort, 39% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, sex, comorbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

Remote monitoring titration clinic to implement guideline-directed therapy for heart failure patients with reduced ejection fraction: a pilot quality-improvement intervention.

Introduction: Guideline-directed medical therapy (GDMT) is the recommended treatment for heart failure with reduced ejection fraction (HFrEF). However, the implementation remains limited, with suboptimal use and dosing. The study aimed to assess the feasibility and effect of a remote monitoring titration program on GDMT implementation. Methods: HFrEF patients were randomly assigned to receive either usual care or a quality-improvement remote titration with remote monitoring intervention. The intervention group used wireless devices to transmit heart rate, blood pressure, and weight data daily, which were reviewed by physicians and nurses every 2-4 weeks. Medication tolerance was assessed via phone, and dosage instructions were given. This workflow was repeated until target doses were reached or further adjustments were not tolerated. A 4-GDMT score measured use and target dosage, with the primary endpoint being the score at 6 months follow-up. Results: Baseline characteristics were similar (n = 55). A median of 85% of patients complied with transmitting device data every week. At the 6-month follow-up, the intervention group had a 4-GDMT score of 64.6% compared to 56.5% in the usual care group (p = 0.01), with a difference of 8.1% (95% CI: 1.7%-14.5%). Similar results were seen at the 12-month follow-up [difference 12.8% (CI: 5.0%-20.6%)]. The intervention group showed a positive trend in ejection fraction and natriuretic peptides, with no significant difference between groups. Conclusions: The study suggests that a full-scale trial is feasible and that utilizing a remote titration clinic with remote monitoring has the potential to enhance the implementation of guideline-directed therapy for HFrEF.

Unsupervised mRNA-seq classification of heart transplant endomyocardial biopsies.

BACKGROUND: Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation. METHODS: Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications. RESULTS: Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification. CONCLUSION: We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.

Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial.

BACKGROUND: Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities. OBJECTIVE: This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities. METHODS: This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation-related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. RESULTS: Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (ß=.2187; P<.001), with the highest effect on cluster 1 (ß=.3683; P<.001) and the lowest effect on cluster 4 (ß=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS: The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. TRIAL REGISTRATION: ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.

Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.

Background and Objective: Heart failure (HF) in the pediatric population is a multi-factorial process with a wide spectrum of etiologies and clinical manifestations, that are distinct from the adult HF population, with congenital heart disease (CHD) as the most common cause. CHD has high morbidity/mortality with nearly 60% developing HF during the first 12 months of life. Hence, early discovery and diagnosis of CHD in neonates is pivotal. Plasma B-type natriuretic peptide (BNP) is an increasingly popular clinical marker in pediatric HF, however, in contrast to adult HF, it is not yet included in pediatric HF guidelines and there is no standardized reference cut-off value. We explore the current trends and prospects of biomarkers in pediatric HF, including CHD that can aid in diagnosis and management. Methods: As a narrative review, we will analyze biomarkers with respect to diagnosis and monitoring in specific anatomical types of CHD in the pediatric population considering all English PubMed publications till June 2022. Key Content and Findings: We present a concise description of our own experience in applying plasma BNP as a clinical biomarker in pediatric HF and CHD (tetralogy of fallot vs. ventricular septal defect) in the context of surgical correction, as well as untargeted metabolomics analyses. In the current age of Information Technology and large data sets we also explored new biomarker discovery using Text Mining of 33M manuscripts currently on PubMed. Conclusions: (Multi) Omics studies from patient samples as well as Data Mining can be considered for the discovery of potential pediatric HF biomarkers useful in clinical care. Future research should focus on validation and defining evidence-based value limits and reference ranges for specific indications using the most up-to-date assays in parallel to commonly used studies.

Assessing the Relationship Between Molecular Rejection and Parenchymal Injury in Heart Transplant Biopsies.

BACKGROUND: The INTERHEART study (ClinicalTrials.gov #NCT02670408) used genome-wide microarrays to detect rejection in endomyocardial biopsies; however, many heart transplants with no rejection have late dysfunction and impaired survival. We used the microarray measurements to develop a molecular classification of parenchymal injury. METHODS: In 1320 endomyocardial biopsies from 645 patients previously studied for rejection-associated transcripts, we measured the expression of 10 injury-induced transcript sets: 5 induced by recent injury; 2 reflecting macrophage infiltration; 2 normal heart transcript sets; and immunoglobulin transcripts, which correlate with time. We used archetypal clustering to assign injury groups. RESULTS: Injury transcript sets correlated with impaired function. Archetypal clustering based on the expression of injury transcript sets assigned each biopsy to 1 of 5 injury groups: 87 Severe-injury, 221 Late-injury, and 3 with lesser degrees of injury, 376 No-injury, 526 Mild-injury, and 110 Moderate-injury. Severe-injury had extensive loss of normal transcripts (dedifferentiation) and increase in macrophage and injury-induced transcripts. Late-injury was characterized by high immunoglobulin transcript expression. In Severe- and Late-injury, function was depressed, and short-term graft failure was increased, even in hearts with no rejection. T cell-mediated rejection almost always had parenchymal injury, and 85% had Severe- or Late-injury. In contrast, early antibody-mediated rejection (AMR) had little injury, but late AMR often had the Late-injury state. CONCLUSIONS: Characterizing heart transplants for their injury state provides new understanding of dysfunction and outcomes and demonstrates the differential impact of T cell-mediated rejection versus AMR on the parenchyma. Slow deterioration from AMR emerges as a major contributor to late dysfunction.

Whole transcriptome profiling of prospective endomyocardial biopsies reveals prognostic and diagnostic signatures of cardiac allograft rejection.

BACKGROUND: Heart transplantation provides a significant improvement in survival and quality of life for patients with end-stage heart disease, however many recipients experience different levels of graft rejection that can be associated with significant morbidities and mortality. Current clinical standard-of-care for the evaluation of heart transplant acute rejection (AR) consists of routine endomyocardial biopsy (EMB) followed by visual assessment by histopathology for immune infiltration and cardiomyocyte damage. We assessed whether the sensitivity and/or specificity of this process could be improved upon by adding RNA sequencing (RNA-seq) of EMBs coupled with histopathological interpretation. METHODS: Up to 6 standard-of-care, or for-cause EMBs, were collected from 26 heart transplant recipients from the prospective observational Clinical Trials of Transplantation (CTOT)-03 study, during the first 12-months post-transplant and subjected to RNA-seq (n = 125 EMBs total). Differential expression and random-forest-based machine learning were applied to develop signatures for classification and prognostication. RESULTS: Leveraging the unique longitudinal nature of this study, we show that transcriptional hallmarks for significant rejection events occur months before the actual event and are not visible using traditional histopathology. Using this information, we identified a prognostic signature for 0R/1R biopsies that with 90% accuracy can predict whether the next biopsy will be 2R/3R. CONCLUSIONS: RNA-seq-based molecular characterization of EMBs shows significant promise for the early detection of cardiac allograft rejection.

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study.

BACKGROUND: Higher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated. OBJECTIVE: This study aims to evaluate the association of shared care networks with 30-day HF excessive readmission rates using a longitudinal observational study. METHODS: We curated publicly available data on hospital discharges and HF excessive readmission ratios from hospitals in California between 2012 and 2017. Shared care areas were delineated as data-driven units of care coordination emerging from discharge networks. The localization index, the proportion of patients who reside in the same shared care area in which they are admitted, was calculated by year. Generalized estimating equations were used to evaluate the association between the localization index and the excessive readmission ratio of hospitals controlling for race/ethnicity and socioeconomic factors. RESULTS: A total of 300 hospitals in California in a 6-year period were included. The HF excessive readmission ratio was negatively associated with the adjusted localization index (ß=-.0474, 95% CI -0.082 to -0.013). The percentage of Black residents within the shared care areas was the only statistically significant covariate (ß=.4128, 95% CI 0.302 to 0.524). CONCLUSIONS: Higher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the Hospital Reduction Readmission Program may need to characterize and reward shared care to guide hospitals toward a more organized HF care system.

Many heart transplant biopsies currently diagnosed as no rejection have mild molecular antibody-mediated rejection-related changes.

BACKGROUND: The Molecular Microscope (MMDx) system classifies heart transplant endomyocardial biopsies as No-rejection (NR), Early-injury, T cell-mediated (TCMR), antibody-mediated (ABMR), mixed, and possible rejection (possible TCMR, possible ABMR). Rejection-like gene expression patterns in NR biopsies have not been described. We extended the MMDx methodology, using a larger data set, to define a new "Minor" category characterized by low-level inflammation in non-rejecting biopsies. METHODS: Using MMDx criteria from a previous study, molecular rejection was assessed in 1,320 biopsies (645 patients) using microarray expression of rejection-associated transcripts (RATs). Of these biopsies, 819 were NR. A new archetypal analysis model in the 1,320 data set split the NRs into NR-Normal (N = 462) and NR-Minor (N = 359). RESULTS: Compared to NR-Normal, NR-Minor were more often histologic TCMR1R, with a higher prevalence of donor-specific antibody (DSA). DSA positivity increased in a gradient: NR-Normal 24%; NR-Minor 34%; possible ABMR 42%; ABMR 66%. The top 20 transcripts distinguishing NR-Minor from NR-Normal were all ABMR-related and/or IFNG-inducible, and also exhibited a gradient of increasing expression from NR-Normal through ABMR. In random forest analysis, TCMR and Early-injury were associated with reduced LVEF and increased graft loss, but NR-Minor and ABMR scores were not. Surprisingly, hearts with MMDx ABMR showed comparatively little graft loss. CONCLUSIONS: Many heart transplants currently diagnosed as NR by histologic or molecular assessment have minor increases in ABMR-related and IFNG-inducible transcripts, associated with DSA positivity and mild histologic inflammation. These results suggest that low-level ABMR-related molecular stress may be operating in many more hearts than previously estimated. (ClinicalTrials.gov #NCT02670408).

Dietary Management of Heart Failure: DASH Diet and Precision Nutrition Perspectives.

Heart failure (HF) is a major health care burden increasing in prevalence over time. Effective, evidence-based interventions for HF prevention and management are needed to improve patient longevity, symptom control, and quality of life. Dietary Approaches to Stop Hypertension (DASH) diet interventions can have a positive impact for HF patients. However, the absence of a consensus for comprehensive dietary guidelines and for pragmatic evidence limits the ability of health care providers to implement clinical recommendations. The refinement of medical nutrition therapy through precision nutrition approaches has the potential to reduce the burden of HF, improve clinical care, and meet the needs of diverse patients. The aim of this review is to summarize current evidence related to HF dietary recommendations including DASH diet nutritional interventions and to develop initial recommendations for DASH diet implementation in outpatient HF management. Articles involving human studies were obtained using the following search terms: Dietary Approaches to Stop Hypertension (DASH diet), diet pattern, diet, metabolism, and heart failure. Only full-text articles written in English were included in this review. As DASH nutritional interventions have been proposed, limitations of these studies are the small sample size and non-randomization of interventions, leading to less reliable evidence. Randomized controlled interventions are needed to offer definitive evidence related to the use of the DASH diet in HF management.

Noninvasive biomarkers for prediction and diagnosis of heart transplantation rejection.

For most patients with end-stage heart failure, heart transplantation is the treatment of choice. Allograft rejection is one of the major post-transplantation complications affecting graft outcome and survival. Recent advancements in science and technology offer an opportunity to integrate genomic and other omics-based biomarkers into clinical practice, facilitating noninvasive evaluation of allograft for diagnostic and prognostic purposes. Omics, including gene expression profiling (GEP) of blood immune cell components and donor-derived cell-free DNA (dd-cfDNA) are of special interest to researchers. Several studies have investigated levels of dd-cfDNA and miroRNAs in blood as potential markers for early detection of allograft rejection. One of the achievements in the field of transcriptomics is AlloMap, GEP of peripheral blood mononuclear cells (PBMC), which can identify 11 differentially expressed genes and help with detection of moderate and severe acute cellular rejection in stable heart transplant recipients. In recent years, the utilization of GEP of PBMC for identifying differentially expressed genes to diagnose acute antibody-mediated rejection and cardiac allograft vasculopathy has yielded promising results. Advancements in the field of metabolomics and proteomics as well as their potential implications have been further discussed in this paper.

Rejection-associated Mitochondrial Impairment After Heart Transplantation.

BACKGROUND: Mitochondrial dysfunction is associated with poor allograft prognosis. Mitochondrial-related gene expression (GE) in endomyocardial biopsies (EMBs) could be useful as a nonimmune functional marker of rejection. We hypothesize that acute cardiac allograft rejection is associated with decreased mitochondrial-related GE in EMBs. METHODS: We collected 64 routines or clinically indicated EMB from 47 patients after heart transplant. The EMBs were subjected to mRNA sequencing. We conducted weighted gene coexpression network analysis to construct module-derived eigengenes. The modules were assessed by gene ontology enrichment and hub gene analysis. Modules were correlated with the EMBs following the International Society of Heart and Lung Transplantation histology-based criteria and a classification based on GE alone; we also correlated with clinical parameters. RESULTS: The modules enriched with mitochondria-related and immune-response genes showed the strongest correlation to the clinical traits. Compared with the no-rejection samples, rejection samples had a decreased activity of mitochondrial-related genes and an increased activity of immune-response genes. Biologic processes and hub genes in the mitochondria-related modules were primarily involved with energy generation, substrate metabolism, and regulation of oxidative stress. Compared with International Society of Heart and Lung Transplantation criteria, GE-based classification had stronger correlation to the weighted gene coexpression network analysis-derived functional modules. The brain natriuretic peptide level, ImmuKnow, and Allomap scores had negative relationships with the expression of mitochondria-related modules and positive relationships with immune-response modules. CONCLUSIONS: During acute cardiac allograft rejection, there was a decreased activity of mitochondrial-related genes, related to an increased activity of immune-response genes, and depressed allograft function manifested by brain natriuretic peptide elevation. This suggests a rejection-associated mitochondrial impairment.

Discovery of non-HLA antibodies associated with cardiac allograft rejection and development and validation of a non-HLA antigen multiplex panel: From bench to bedside.

We analyzed humoral immune responses to nonhuman leukocyte antigen (HLA) after cardiac transplantation to identify antibodies associated with allograft rejection. Protein microarray identified 366 non-HLA antibodies (>1.5 fold, P < .5) from a discovery cohort of HLA antibody-negative, endothelial cell crossmatch-positive sera obtained from 12 cardiac allograft recipients at the time of biopsy-proven rejection. From these, 19 plasma membrane proteins and 10 autoantigens identified from gene ontology analysis were combined with 48 proteins identified through literature search to generate a multiplex bead array. Longitudinal sera from a multicenter cohort of adult cardiac allograft recipients (samples: n = 477 no rejection; n = 69 rejection) identified 18 non-HLA antibodies associated with rejection (P < .1) including 4 newly identified non-HLA antigenic targets (DEXI, EMCN, LPHN1, and SSB). CART analysis showed 5/18 non-HLA antibodies distinguished rejection vs nonrejection. Antibodies to 4/18 non-HLA antigens synergize with HLA donor-specific antibodies and significantly increase the odds of rejection (P < .1). The non-HLA panel was validated using an independent adult cardiac transplant cohort (n = 21 no rejection; n = 42 rejection, >1R) with an area under the curve of 0.87 (P < .05) with 92.86% sensitivity and 66.67% specificity. We conclude that multiplex bead array assessment of non-HLA antibodies identifies cardiac transplant recipients at risk of rejection.

A Data-Driven Social Network Intervention for Improving Organ Donation Awareness Among Minorities: Analysis and Optimization of a Cross-Sectional Study.

BACKGROUND: Increasing the number of organ donors may enhance organ transplantation, and past health interventions have shown the potential to generate both large-scale and sustainable changes, particularly among minorities. OBJECTIVE: This study aimed to propose a conceptual data-driven framework that tracks digital markers of public organ donation awareness using Twitter and delivers an optimized social network intervention (SNI) to targeted audiences using Facebook. METHODS: We monitored digital markers of organ donation awareness across the United States over a 1-year period using Twitter and examined their association with organ donation registration. We delivered this SNI on Facebook with and without optimized awareness content (ie, educational content with a weblink to an online donor registration website) to low-income Hispanics in Los Angeles over a 1-month period and measured the daily number of impressions (ie, exposure to information) and clicks (ie, engagement) among the target audience. RESULTS: Digital markers of organ donation awareness on Twitter are associated with donation registration (beta=.0032; P<.001) such that 10 additional organ-related tweets are associated with a 3.20% (33,933/1,060,403) increase in the number of organ donor registrations at the city level. In addition, our SNI on Facebook effectively reached 1 million users, and the use of optimization significantly increased the rate of clicks per impression (beta=.0213; P<.004). CONCLUSIONS: Our framework can provide a real-time characterization of organ donation awareness while effectively delivering tailored interventions to minority communities. It can complement past approaches to create large-scale, sustainable interventions that are capable of raising awareness and effectively mitigate disparities in organ donation.

Temporal expression of cytokines and B-cell phenotypes during mechanical circulatory support.

BACKGROUND: Allosensitization during mechanical circulatory support (MCS) is a well-described phenomenon, although its mechanism remains unknown. Although immune-mediated interactions from devices or blood transfusions have been proposed, the role of inflammation in this development is less clear. This study was undertaken to further investigate the temporal association of cytokines and B-cell phenotypes in the MCS population. METHODS: Adult patients who received the Heartmate II (Thoratec, Pleasanton, Calif) at our center between September 2012 and March 2015 were prospectively followed after device implantation. Blood draws for anti-human leukocyte antigen (HLA) antibody, cytokine expression, and B-cell immunophenotyping were performed before implantation and for 3 weeks postoperatively. Time courses for cytokines and B-cell subsets were expressed using visual representations of median levels as heat maps, and mixed modeling analysis was used to model changes with time and patient factors. RESULTS: Twenty patients who received the Heartmate II (Thoratec) were analyzed during the study period. Four patients showed measureable levels of anti-HLA antibody during the follow-up period, although 3 of these had evidence of antibodies preoperatively. Analysis of cytokine trends revealed early (interleukin [IL]-6, IL-8, and IL-10) and late peaking (IL-3, IL-4, fibroblast growth factor 2, and CD40L) patterns. Upregulation of switched memory, transitional, and plasma blast B cells occurred over time. Right ventricular assist device use and low Interagency Registry for Mechanically Assisted Circulatory Support score were associated with decreased mature naive and increased antibody-secreting cells. CONCLUSIONS: MCS device implantation was associated with increased inflammatory cytokines and maturation of B-cell phenotypes. No patients developed de novo HLA antibodies, whereas several showed increases in anti-HLA antibody levels detected before implantation. This suggests that inflammation and maturation of existing sensitized B cells might play an important role in the pathogenesis of allosensitization in MCS.

An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.

BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we examined the stability of machine-learning algorithms in new biopsies, compared 3AA vs 4AA algorithms, assessed supervised binary classifiers trained on histologic or molecular diagnoses, created a report combining many scores into an ensemble of estimates, and examined possible automated sign-outs. METHODS: We studied 889 EMBs from 454 transplant recipients at 8 centers: the initial cohort (N = 331) and a new cohort (N = 558). Published 3AA algorithms derived in Cohort 331 were tested in Cohort 558, the 3AA and 4AA models were compared, and supervised binary classifiers were created. RESULTS: A`lgorithms derived in Cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (areas under the curve [AUCs] >0.87) better than histologic rejection (AUCs <0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by 1 expert showed highly significant agreement with histology (p < 0.001), but with many discrepancies, as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated signouts. CONCLUSIONS: Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states.