A heart transplant center experience with basiliximab induction strategies: A double edged sword?

BACKGROUND: The use of induction immunosuppression for heart transplantation (HT) is debated given the uncertain benefit and potential risks of infection and malignancy. METHODS: This is a retrospective single-center analysis of 475 consecutive HT recipients from 2003 to 2020 grouped by use of induction with basiliximab group (BG) and the no basiliximab group (NBG). Subgroup analysis by era compared pre-2016 standard-basiliximab (BX) induction and 2016-2020 with selective-BX use as part of a calcineurin-inhibitor-sparing regimen. RESULTS: When adjusted for confounders (sex, age, PRA, eGFR), the BG was less likely to have acute cellular rejection (ACR) (OR.42, p < .001), but had more antibody mediated rejection (AMR) (OR 11.7, p < .001) and more cardiac allograft vasculopathy (CAV) (OR 3.8, p = .04). There was no difference between BG and NBG in the incidence of malignancies or infections. When stratified by era (pre-2016 vs. 2016-2020), ACR remained less common in the BG than the NBG (36% vs. 50%, p = .045) groups, while AMR remained more common (9.7 vs. 0% p = .005). There was no significant difference in conditional survival comparing pre-and post-2016 NBG (HR 2.20 (95% CI.75-6.43); however, both pre-2016 BG and post-2016 BG have significantly higher mortality (HR 2.37 [95% CI 1.02-5.50) and HR 2.69 (95% CI 1.08-6.71), p = .045 and.03, respectively]. CONCLUSION: Basiliximab reduces the incidence of ACR but increases the risk of AMR, CAV, and may be associated with increased mortality. Mechanistic studies are needed to describe a potential T-cell-escape mechanism with enhanced humoral immunity.

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.

Effect of Immunosuppressive Therapy and Biopsy Status in Monitoring Therapy Response in Suspected Cardiac Sarcoidosis.

BACKGROUND: Patients with suspected cardiac sarcoidosis frequently undergo fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging to assess disease activity at baseline and after treatment initiation. OBJECTIVES: This study investigated the effect of immunosuppressive therapy and biopsy status to achieve complete treatment response (CTR), partial treatment response (PTR), or no response (NR) on myocardial FDG-PET/CT. METHODS: This study analyzed 83 patients with suspected cardiac sarcoidosis (aged 53 ± 1.8 years, 71% were male, 69% were White, 61% had a history of biopsy-confirmed sarcoidosis) who were treatment naive, had evidence of myocardial FDG at baseline, and underwent repeat PET imaging after treatment initiation. CTR was graded visually, and PTR/NR were measured both visually and quantitatively using the total glycolytic activity. Patients were also evaluated for the occurrence of death, sustained ventricular arrhythmias, and heart failure admissions. RESULTS: Overall, 59 patients (71%) achieved CTR/PTR (30%/41%) at follow-up scan (P = 0.04). Total glycolytic activity and visual estimate of PTR/NR had excellent agreement (κ = 0.86 [95% CI: 0.72-0.99]; P < 0.0001). In patients receiving prednisone only, the highest rates of CTR/PTR were observed in patients initiated on moderate or high dose (P < 0.01). In a regression model, moderate prednisone start dose (P = 0.03) was more strongly associated with achieving CTR/PTR than was high prednisone start dose. However, the latter patients were tapered faster between start dose and follow-up scan (P < 0.01). After a median follow-up of 4.7 (IQR: 3.1-7.8) years, patients who were biopsy-proven (vs non-biopsy-proven; P = 0.029) and with preserved left ventricular function (P = 002) were less likely to experience major adverse cardiac events. Outcomes based on treatment response status (CTR vs PTR vs NR; P = 0.23) were not significantly different. CONCLUSIONS: Among patients with suspected sarcoidosis and evidence of myocardial inflammation, treatment response by serial FDG-PET was variable, but a favorable response was more common when using moderate-to-high intensity prednisone dose. Biopsy-proven individuals and those with preserved systolic function were less likely to experience adverse outcomes during follow-up.

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.

The XVth Banff Conference on Allograft Pathology the Banff Workshop Heart Report: Improving the diagnostic yield from endomyocardial biopsies and Quilty effect revisited.

The XVth Banff Conference on Allograft Pathology meeting was held on September 23-27, 2019, in Pittsburgh, Pennsylvania, USA. During this meeting, two main topics in cardiac transplant pathology were addressed: (a) Improvement of endomyocardial biopsy (EMB) accuracy for the diagnosis of rejection and other significant injury patterns, and (b) the orphaned lesion known as Quilty effect or nodular endocardial infiltrates. Molecular technologies have evolved in recent years, deciphering pathophysiology of cardiac rejection. Diagnostically, it is time to integrate the histopathology of EMBs and molecular data. The goal is to incorporate molecular pathology, performed on the same paraffin block as a companion test for histopathology, to yield more accurate and objective EMB interpretation. Application of digital image analysis from hematoxylin and eosin (H&E) stain to multiplex labeling is another means of extracting additional information from EMBs. New concepts have emerged exploring the multifaceted significance of myocardial injury, minimal rejection patterns supported by molecular profiles, and lesions of arteriolitis/vasculitis in the setting of T cell-mediated rejection (TCMR) and antibody-mediated rejection (AMR). The orphaned lesion known as Quilty effect or nodular endocardial infiltrates. A state-of-the-art session with historical aspects and current dilemmas was reviewed, and possible pathogenesis proposed, based on advances in immunology to explain conflicting data. The Quilty effect will be the subject of a multicenter project to explore whether it functions as a tertiary lymphoid organ.

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.

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.

Inflammation and Arterial Stiffness in Chronic Kidney Disease: Findings From the CRIC Study.

BACKGROUND: Chronic kidney disease (CKD) and arterial stiffness are associated with increased cardiovascular morbidity and mortality. Inflammation is proposed to have a role in the development of arterial stiffness, and CKD is recognized as a proinflammatory state. Arterial stiffness is increased in CKD, and cross-sectional data has suggested a link between increased inflammatory markers in CKD and higher measures of arterial stiffness. However, no large scale investigations have examined the impact of inflammation on the progression of arterial stiffness in CKD. METHODS: We performed baseline assessments of 5 inflammatory markers in 3,939 participants from the chronic renal insufficiency cohort (CRIC), along with serial measurements of arterial stiffness at 0, 2, and 4 years of follow-up. RESULTS: A total of 2,933 participants completed each of the follow-up stiffness measures. In cross-sectional analysis at enrollment, significant associations with at least 2 measures of stiffness were observed for fibrinogen, interleukin-6, high-sensitivity C-reactive protein, proteinuria, and composite inflammation score after adjustment for confounders. In longitudinal analyses, there were few meaningful correlations between baseline levels of inflammation and changes in metrics of arterial stiffness over time. CONCLUSION: In a large cohort of CKD participants, we observed multiple significant correlations between initial markers of inflammation and metrics of arterial stiffness, but baseline inflammation did not predict changes in arterial stiffness over time. While well-described biologic mechanisms provide the basis for our understanding of the cross-sectional results, continued efforts to design longitudinal studies are necessary to fully elucidate the relationship between chronic inflammation and arterial stiffening.

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.

Frequency of aspirin resistance in a community hospital.

Aspirin resistance and its predictors were studied in community hospital patients who required antiplatelet therapy for thrombotic event prophylaxis. Demographic and antiplatelet medication data were collected and medication response followed. Aspirin resistance was assayed with the VerifyNow System with > or = 550 aspirin reaction units (ARUs) used as a dichotomous indicator of aspirin resistance. Patients (n = 123) were 21 to 95 years old; 49.6% were women, 77.2% were black, 95.1% were hypertensive, 85.4% had coronary disease, and 30.1% were smokers. ARU score for 325 versus 81 mg/day was 435.2 +/- 93.7 versus 401.9 +/- 83.9 ARU (p = 0.04), with a 12.1% (8 of 66 patients) nonresponse rate to 81 mg/day. Of the 8 patients who were unresponsive to 81 mg/day of aspirin, 7 responded to 325 mg/day. The 5.3% (3 of 57 patients) who were resistant to 325 mg/day received clopidogrel; 2 became responders. Multivariate analysis demonstrated significant associations of aspirin resistance with smoking (risk ratio 11.47, 95% confidence interval 6.69 to 18.63, p < 0.0001), including a significant interaction between smoking and aspirin resistance. In conclusion, this study estimates aspirin resistance prevalence and shows a strong association of smoking with platelet hyperactivity in a diverse community hospital population. Nonresponders to 81 mg/day frequently responded to 325 mg/day or to the addition of clopidogrel.