Hospitalizations for opportunistic infections following transplantation and associated risk factors: A national cohort study of Medicare beneficiaries.

BACKGROUND: Opportunistic infections (OIs) are a significant cause of morbidity and mortality after organ transplantation, though data in the liver transplant (LT) population are limited. METHODS: We performed a retrospective cohort study of LT recipients between January 1, 2007 and Deceber 31, 2016 using Medicare claims data linked to the Organ Procurement and Transplantation Network database. Multivariable Cox regression models evaluated factors independently associated with hospitalizations for early (≤1 year post transplant) and late (>1 year) OIs, with a particular focus on immunosuppression. RESULTS: There were 11 320 LT recipients included in the study, of which 13.2% had at least one OI hospitalization during follow-up. Of the 2638 OI hospitalizations, 61.9% were early post-LT. Cytomegalovirus was the most common OI (45.4% overall), although relative frequency decreased after the first year (25.3%). Neither induction or maintenance immunosuppression were associated with early OI hospitalization (all p > .05). The highest risk of early OI was seen with primary sclerosing cholangitis (aHR 1.74; p = .003 overall). Steroid-based and mechanistic target of rapamycin inhibitor-based immunosuppression at 1 year post LT were independently associated with increased late OI (p < .001 overall). CONCLUSION: This study found OI hospitalizations to be relatively common among LT recipients and frequently occur later than previously reported. Immunosuppression regimen may be an important modifiable risk factor for late OIs.

Real-world evidence for factors associated with maintenance treatment practices among US adults with autoimmune hepatitis.

BACKGROUND AND AIMS: While avoidance of long-term corticosteroids is a common objective in the management of autoimmune hepatitis (AIH), prolonged immunosuppression is usually required to prevent disease progression. This study investigates the patient and provider factors associated with treatment patterns in US patients with AIH. APPROACH AND RESULTS: A retrospective cohort of adults with the incident and prevalent AIH was identified from Optum's deidentified Clinformatics Data Mart Database. All patients were followed for at least 2 years, with exposures assessed during the first year and treatment patterns during the second. Patient and provider factors associated with corticosteroid-sparing monotherapy and cumulative prednisone use were identified using multivariable logistic and linear regression, respectively.The cohort was 81.2% female, 66.3% White, 11.3% Black, 11.2% Hispanic, and with a median age of 61 years. Among 2203 patients with ≥1 AIH prescription fill, 83.1% received a single regimen for >6 months of the observation year, which included 52.2% azathioprine monotherapy, 16.9% azathioprine/prednisone, and 13.3% prednisone monotherapy. Budesonide use was uncommon (2.1% combination and 1.9% monotherapy). Hispanic ethnicity (aOR: 0.56; p = 0.006), cirrhosis (aOR: 0.73; p = 0.019), osteoporosis (aOR: 0.54; p =0.001), and top quintile of provider AIH experience (aOR: 0.66; p = 0.005) were independently associated with lower use of corticosteroid-sparing monotherapy. Cumulative prednisone use was greater with diabetes (+441 mg/y; p = 0.004), osteoporosis (+749 mg/y; p < 0.001), and highly experienced providers (+556 mg/y; p < 0.001). CONCLUSIONS: Long-term prednisone therapy remains common and unexpectedly higher among patients with comorbidities potentially aggravated by corticosteroids. The greater use of corticosteroid-based therapy with highly experienced providers may reflect more treatment-refractory disease.

Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity.

BACKGROUND: Cardiac allograft rejection is the leading cause of early graft failure and is a major focus of postheart transplant patient care. While histological grading of endomyocardial biopsy samples remains the diagnostic standard for acute rejection, this standard has limited diagnostic accuracy. Discordance between biopsy rejection grade and patient clinical trajectory frequently leads to both overtreatment of indolent processes and delayed treatment of aggressive ones, spurring the need to investigate the adequacy of the current histological criteria for assessing clinically important rejection outcomes. METHODS: N=2900 endomyocardial biopsy images were assigned a rejection grade label (high versus low grade) and a clinical trajectory label (evident versus silent rejection). Using an image analysis approach, n=370 quantitative morphology features describing the lymphocytes and stroma were extracted from each slide. Two models were constructed to compare the subset of features associated with rejection grades versus those associated with clinical trajectories. A proof-of-principle machine learning pipeline-the cardiac allograft rejection evaluator-was then developed to test the feasibility of identifying the clinical severity of a rejection event. RESULTS: The histopathologic findings associated with conventional rejection grades differ substantially from those associated with clinically evident allograft injury. Quantitative assessment of a small set of well-defined morphological features can be leveraged to more accurately reflect the severity of rejection compared with that achieved by the International Society of Heart and Lung Transplantation grades. CONCLUSIONS: Conventional endomyocardial samples contain morphological information that enables accurate identification of clinically evident rejection events, and this information is incompletely captured by the current, guideline-endorsed, rejection grading criteria.

Adult human cardiomyocyte mechanics in osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is an extracellular matrix disorder characterized by defects in collagen-1 transport or synthesis, resulting in bone abnormalities. Although reduced collagen in OI hearts has been associated with reduced myocardial stiffness and left ventricular remodeling, its impact on cardiomyocyte (CM) function has not been studied. Here, we explore the tissue-level and CM-level properties of a heart from a deceased organ donor with OI type I. Proteomics and histology confirmed strikingly low expression of collagen 1. Trabecular stretch confirmed low stiffness on the tissue level. However, CMs retained normal viscoelastic properties as revealed by nanoindentation. Interestingly, OI CMs were hypercontractile relative to nonfailing controls after 24 h of culture. In response to 48 h of culture on surfaces with physiological (10 kPa) and pathological (50 kPa) stiffness, OI CMs demonstrated a greater reduction in contractility than nonfailing CMs, suggesting that OI CMs may have an impaired stress response. Levels of detyrosinated α-tubulin, known to be responsive to extracellular stiffness, were reduced in OI CMs. Together these data confirm multiple CM-level adaptations to low stiffness that extend our understanding of OI in the heart and how CMs respond to extracellular stiffness.NEW & NOTEWORTHY In a rare donation of a heart from an individual with osteogenesis imperfecta (OI), we explored cardiomyocyte (CM) adaptations to low stiffness. This represents the first assessment of cardiomyocyte mechanics in OI. The data reveal the hypercontractility of OI CMs with rapid rundown when exposed to acute stiffness challenges, extending our understanding of OI. These data demonstrate that the impact of OI on myocardial mechanics includes cardiomyocyte adaptations beyond known direct effects on the extracellular matrix.

Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.

BACKGROUND: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. Although clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high versus low risk of developing aggressive CAV. This investigation aimed to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMBs) to develop a precision medicine tool for predicting CAV years before overt clinical presentation. METHODS: Clinical data from 1 year after transplant were collected on 302 transplant recipients from the University of Pennsylvania, including 53 patients with early-onset CAV and 249 no early-onset CAV controls. These data were used to generate a clinical model (Clinical Risk Factor Future Cardiac Allograft Vasculopathy Prediction Model [ClinCAV-Pr]) for predicting future CAV development. From this cohort, 183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year posttransplant EMBs from 50 patients with early-onset CAV and 82 patients with no early-onset CAV, as well as 51 EMBs from disease control patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically inspired, handcrafted features extracted from digitized EMBs, quantitative histological models for differentiating no early-onset CAV from disease controls (Histological Cardiac Allograft Vasculopathy Diagnostic Model [HistoCAV-Dx]) and for predicting future CAV from 1-year posttransplant EMBs were developed (Histological Future Cardiac Allograft Vasculopathy Prediction Model [HistoCAV-Pr]). The performance of histological and clinical models for predicting future CAV (ie, HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set before being combined to assess the added predictive value of an integrated predictive model (Integrated Histological/Clinical Risk Factor Future Cardiac Allograft Vasculopathy Prediction Model [iCAV-Pr]). RESULTS: ClinCAV-Pr achieved modest performance on the independent test set, with an area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, whereas the HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set. CONCLUSIONS: Prediction of future CAV development is greatly improved by incorporation of computationally extracted histological features. These results suggest morphological details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for patients after heart transplant.

An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.

AIM: Allograft rejection is a serious concern in heart transplant medicine. Though endomyocardial biopsy with histological grading is the diagnostic standard for rejection, poor inter-pathologist agreement creates significant clinical uncertainty. The aim of this investigation is to demonstrate that cellular rejection grades generated via computational histological analysis are on-par with those provided by expert pathologists. METHODS AND RESULTS: The study cohort consisted of 2472 endomyocardial biopsy slides originating from three major US transplant centres. The 'Computer-Assisted Cardiac Histologic Evaluation (CACHE)-Grader' pipeline was trained using an interpretable, biologically inspired, 'hand-crafted' feature extraction approach. From a menu of 154 quantitative histological features relating the density and orientation of lymphocytes, myocytes, and stroma, a model was developed to reproduce the 4-grade clinical standard for cellular rejection diagnosis. CACHE-grader interpretations were compared with independent pathologists and the 'grade of record', testing for non-inferiority (δ = 6%). Study pathologists achieved a 60.7% agreement [95% confidence interval (CI): 55.2-66.0%] with the grade of record, and pair-wise agreement among all human graders was 61.5% (95% CI: 57.0-65.8%). The CACHE-Grader met the threshold for non-inferiority, achieving a 65.9% agreement (95% CI: 63.4-68.3%) with the grade of record and a 62.6% agreement (95% CI: 60.3-64.8%) with all human graders. The CACHE-Grader demonstrated nearly identical performance in internal and external validation sets (66.1% vs. 65.8%), resilience to inter-centre variations in tissue processing/digitization, and superior sensitivity for high-grade rejection (74.4% vs. 39.5%, P < 0.001). CONCLUSION: These results show that the CACHE-grader pipeline, derived using intuitive morphological features, can provide expert-quality rejection grading, performing within the range of inter-grader variability seen among human pathologists.

In Situ Immune Profiling of Heart Transplant Biopsies Improves Diagnostic Accuracy and Rejection Risk Stratification.

Recognizing that guideline-directed histologic grading of endomyocardial biopsy tissue samples for rejection surveillance has limited diagnostic accuracy, quantitative, in situ characterization was performed of several important immune cell types in a retrospective cohort of clinical endomyocardial tissue samples. Differences between cases were identified and were grouped by histologic grade versus clinical rejection trajectory, with significantly increased programmed death ligand 1+, forkhead box P3+, and cluster of differentiation 68+ cells suppressed in clinically evident rejections, especially cases with marked clinical-histologic discordance. Programmed death ligand 1+, forkhead box P3+, and cluster of differentiation 68+ cell proportions are also significantly higher in "never-rejection" when compared with "future-rejection." These findings suggest that in situ immune modulators regulate the severity of cardiac allograft rejection.

Tunable and Reversible Substrate Stiffness Reveals a Dynamic Mechanosensitivity of Cardiomyocytes.

New directions in material applications have allowed for the fresh insight into the coordination of biophysical cues and regulators. Although the role of the mechanical microenvironment on cell responses and mechanics is often studied, most analyses only consider static environments and behavior, however, cells and tissues are themselves dynamic materials that adapt in myriad ways to alterations in their environment. Here, we introduce an approach, through the addition of magnetic inclusions into a soft poly(dimethylsiloxane) elastomer, to fabricate a substrate that can be stiffened nearly instantaneously in the presence of cells through the use of a magnetic gradient to investigate short-term cellular responses to dynamic stiffening or softening. This substrate allows us to observe time-dependent changes, such as spreading, stress fiber formation, Yes-associated protein translocation, and sarcomere organization. The identification of temporal dynamic changes on a short time scale suggests that this technology can be more broadly applied to study targeted mechanisms of diverse biologic processes, including cell division, differentiation, tissue repair, pathological adaptations, and cell-death pathways. Our method provides a unique in vitro platform for studying the dynamic cell behavior by better mimicking more complex and realistic microenvironments. This platform will be amenable to future studies aimed at elucidating the mechanisms underlying mechanical sensing and signaling that influence cellular behaviors and interactions.

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.

Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.

Allograft rejection remains a significant concern after all solid organ transplants. Although qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy to diagnose cardiac allograft rejection illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted endomyocardial biopsy as the diagnostic gold standard. In this context, automated approaches to complex data analysis problems-often referred to as "machine learning"-represent promising strategies to improve overall diagnostic accuracy. By focusing on cardiac allograft rejection, where tissue sampling is relatively frequent, this review highlights the limitations of the current approach to diagnosing allograft rejection, introduces the basic methodology behind machine learning and automated image feature detection, and highlights the initial successes of these approaches within cardiovascular medicine.

Atherosclerotic plaque inflammation varies between vascular sites and correlates with response to inhibition of lipoprotein-associated phospholipase A2.

BACKGROUND: Despite systemic exposure to risk factors, the circulatory system develops varying patterns of atherosclerosis for unclear reasons. In a porcine model, we investigated the relationship between site-specific lesion development and inflammatory pathways involved in the coronary arteries (CORs) and distal abdominal aortas (AAs). METHODS AND RESULTS: Diabetes mellitus (DM) and hypercholesterolemia (HC) were induced in 37 pigs with 3 healthy controls. Site-specific plaque development was studied by comparing plaque severity, macrophage infiltration, and inflammatory gene expression between CORs and AAs of 17 DM/HC pigs. To assess the role of lipoprotein-associated phospholipase A2 (Lp-PLA2) in plaque development, 20 DM/HC pigs were treated with the Lp-PLA2 inhibitor darapladib and compared with the 17 DM/HC untreated pigs. DM/HC caused site-specific differences in plaque severity. In the AAs, normalized plaque area was 4.4-fold higher (P<0.001) and there were more fibroatheromas (9 of the 17 animals had a fibroatheroma in the AA and not the COR, P=0.004), while normalized macrophage staining area was 1.5-fold higher (P=0.011) compared with CORs. DM/HC caused differential expression of 8 of 87 atherosclerotic genes studied, including 3 important in inflammation with higher expression in the CORs. Darapladib-induced attenuation of normalized plaque area was site-specific, as CORs responded 2.9-fold more than AAs (P=0.045). CONCLUSIONS: While plaque severity was worse in the AAs, inflammatory genes and inflammatory pathways that use Lp-PLA2 were more important in the CORs. Our results suggest fundamental differences in inflammation between vascular sites, an important finding for the development of novel anti-inflammatory therapeutics.