Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk ...prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.
We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice.
We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.
Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Methods for ...quantitative assessments of protein kinetics are lacking, and despite knowledge gained from single-protein studies, integrative views of the coordinated behavior of multiple proteins in cardiac remodeling are scarce. Here, we developed a workflow that integrates deuterium oxide (2H2O) labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. Using this workflow, we characterized the in vivo turnover kinetics of 2,964 proteins in a mouse model of β-adrenergic-induced cardiac remodeling. The data provided a quantitative and longitudinal view of cardiac remodeling at the molecular level, revealing widespread kinetic regulations in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. We translated the workflow to human studies, creating a reference dataset of 496 plasma protein turnover rates from 4 healthy adults. The approach is applicable to short, minimal label enrichment and can be performed on as little as a single biopsy, thereby overcoming critical obstacles to clinical investigations. The protein turnover quantitation experiments and computational workflow described here should be widely applicable to large-scale biomolecular investigations of human disease mechanisms with a temporal perspective.
Donor-specific HLA antibodies (DSA) are associated with increased rates of rejection and of graft failure in cardiac transplantation. The goal of this study was to determine the association of ...preformed and posttransplant development of newly detected DSA (ndDSA) with antibody-mediated rejection (AMR) and characterize the clinical relevance of complement-activating DSA in heart allograft recipients.
The study included 128 adult and 48 pediatric heart transplant patients transplanted between 2010 and 2013. Routine posttransplant HLA antibody testing was performed by IgG single-antigen bead test. The C3d single-antigen bead assay was used to identify complement-activating antibodies. Rejection was diagnosed using International Society for Heart and Lung Transplantation criteria.
In this study, 22 patients were transplanted with preexisting DSA, and 43 patients developed ndDSA posttransplant. Pretransplant (P < 0.05) and posttransplant (P < 0.001) ndDSA were associated with higher incidence of AMR. Patients with C3d + DSA had significantly higher incidence of AMR compared with patients with no DSA (P < 0.001) or patients with C3d-DSA (P = 0.02). Nine (36%) of 25 patients with AMR developed transplant coronary artery disease compared with 17 (15.9%) of 107 patients without AMR (P < 0.05). Among the 47 patients who received ventricular assistant device (VAD), 7 of 9 VAD+ patients with preformed DSA experienced AMR compared with 7 of 38 VAD+ patients without preformed DSA, indicating presensitization to donor HLA significantly increased the risk of AMR (P < 0.01).
Preformed and posttransplant ndDSA were associated with AMR. C3d + DSA correlates with complement deposition on the graft and higher risk of AMR which may permit the application of personalized immunotherapy targeting the complement pathway.
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.
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.
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.
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 (β=.2187; P<.001), with the highest effect on cluster 1 (β=.3683; P<.001) and the lowest effect on cluster 4 (β=.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.
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.
ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.
Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction ...(MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.
We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.
Multiorgan dysfunction syndrome contributes to adverse outcomes in advanced heart failure (AdHF) patients after mechanical circulatory support (MCS) implantation and is associated with aberrant ...leukocyte activity. We tested the hypothesis that preoperative peripheral blood mononuclear cell (PBMC) gene expression profiles (GEP) can predict early postoperative improvement or non-improvement in patients undergoing MCS implantation. We believe this information may be useful in developing prognostic biomarkers.
We conducted a study with 29 patients undergoing MCS-surgery in a tertiary academic medical center from 2012 to 2014. PBMC samples were collected one day before surgery (day -1). Clinical data was collected on day -1 and day 8 postoperatively. Patients were classified by Sequential Organ Failure Assessment score and Model of End-stage Liver Disease Except INR score (measured eight days after surgery): Group I = improving (both scores improved from day -1 to day 8, n = 17) and Group II = not improving (either one or both scores did not improve from day -1 to day 8, n = 12). RNA-sequencing was performed on purified mRNA and analyzed using Next Generation Sequencing Strand. Differentially expressed genes (DEGs) were identified by Mann-Whitney test with Benjamini-Hochberg correction. Preoperative DEGs were used to construct a support vector machine algorithm to predict Group I vs. Group II membership.
Out of 28 MCS-surgery patients alive 8 days postoperatively, one-year survival was 88% in Group I and 27% in Group II. We identified 28 preoperative DEGs between Group I and II, with an average 93% prediction accuracy. Out of 105 DEGs identified preoperatively between year 1 survivors and non-survivors, 12 genes overlapped with the 28 predictive genes.
In AdHF patients following MCS implantation, preoperative PBMC-GEP predicts early changes in organ function scores and correlates with long-term outcomes. Therefore, gene expression lends itself to outcome prediction and warrants further studies in larger longitudinal cohorts.
Proteasome complexes play essential roles in maintaining cellular protein homeostasis and serve fundamental roles in cardiac function under normal and pathological conditions. A functional detriment ...in proteasomal activities has been recognized as a major contributor to the progression of cardiovascular diseases. Therefore, approaches to restore proteolytic function within the setting of the diseased myocardium would be of great clinical significance.
In this study, we discovered that the cardiac proteasomal activity could be regulated by acetylation. Histone deacetylase (HDAC) inhibitors (suberoylanilide hydroxamic acid and sodium valproate) enhanced the acetylation of 20S proteasome subunits in the myocardium and led to an elevation of proteolytic capacity. This regulatory paradigm was present in both healthy and acutely ischemia/reperfusion (I/R) injured murine hearts, and HDAC inhibition in vitro restored proteolytic capacities to baseline sham levels in injured hearts. This mechanism of regulation was also viable in failing human myocardium. With 20S proteasomal complexes purified from murine myocardium treated with HDAC inhibitors in vivo, we confirmed that acetylation of 20S subunits directly, at least in part, presents a molecular explanation for the improvement in function. Furthermore, using high-resolution LC-MS/MS, we unraveled the first cardiac 20S acetylome, which identified the acetylation of nine N-termini and seven internal lysine residues. Acetylation on four lysine residues and four N-termini on cardiac proteasomes were novel discoveries of this study. In addition, the acetylation of five lysine residues was inducible via HDAC inhibition, which correlated with the enhancement of 20S proteasomal activity. Taken as a whole, our investigation unveiled a novel mechanism of proteasomal function regulation in vivo and established a new strategy for the potential rescue of compromised proteolytic function in the failing heart using HDAC inhibitors.
Objective Ventricular assist devices (VADs) are associated with increased anti–human leukocyte antigen antibody production. The purpose of this study is to characterize differences in sensitization ...patterns in patients receiving axial flow, implantable VADs versus pulsatile, paracorporeal biventricular assist devices (BIVADs) as bridges to transplantation. Methods The study is a retrospective review of 68 patients who were bridged to transplantation with either a VAD or a BIVAD, as described, from January 2007 to June 2010, at a university medical center. Results Five of 15 (33.3%) VAD patients became sensitized during treatment, compared with 30 of 53 (56.6%) BIVAD patients, P = .15. Multivariable analysis comparing BIVAD with VAD, while controlling for previous cardiac surgery, pregnancy, and packed red blood cell transfusion produced an odds ratio of 2.99, P = .14. Of sensitized patients, all 5 (100%) of the VAD patients had pre-existing antibodies before VAD placement, compared with 9 of 30 (30.0%) BIVAD patients, P = .006. Maximum cumulative mean fluorescence intensities for BIVAD were 46,259 ± 66,349 versus 42,540 ± 12,840 for VAD, P = .90. Time to maximum antibody expression was shorter for the VAD group (34 ± 28 days vs 5.8 ± 9 days, P = .04). Conclusions Device type was not a factor in patient sensitization after implantation. However, VAD patients required pre-existing sensitization before implantation to produce antibodies during their treatment interval, whereas more than two thirds of BIVAD patients developed de novo antibodies. These data suggest that the mechanism of sensitization between VAD and BIVAD patients may differ, and further mechanistic studies into the impact of device types on patient sensitization are warranted.
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