Novel Liver Injury Phenotypes and Outcomes in Clinical Trial Participants with Pulmonary Hypertension.

RATIONALE: Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) cause right ventricular dysfunction which can impact other solid organs. However, the profiles and consequences of hepatic injury due to PAH and CTEPH have not been well-studied. OBJECTIVES: We aimed to identify underlying patterns of liver injury in a cohort of PAH and CTEPH patients enrolled in 15 randomized clinical trials conducted between 1998 and 2014. METHODS: We used unsupervised machine learning to identify liver injury clusters in 13 trials and validated the findings in two additional trials. We then determined whether these liver injury clusters were associated with clinical outcomes or treatment effect heterogeneity. MEASUREMENTS AND MAIN RESULTS: Our training dataset included 4,219 patients and our validation dataset included 1,756 patients with serum total bilirubin, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin data. Using k-means clustering, we identified phenotypes with no liver injury, hepatocellular injury, cholestatic injury, and combined injury patterns. Patients in the cholestatic injury liver cluster had the shortest time to clinical worsening and the highest risk of mortality. The cholestatic injury group also experienced the greatest placebo-corrected treatment effect on six-minute walk distance. Randomization to the experimental arm transitioned patients to a healthier liver status. CONCLUSIONS: Liver injury was associated with adverse outcomes in patients with PAH and CTEPH. Randomization to active treatment had beneficial effects on liver health compared to placebo. The role of liver disease (often subclinical) in determining outcomes warrants prospective studies.

Novel Liver Injury Phenotypes and Outcomes in Pulmonary Arterial Hypertension.

Background: Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) are disorders of the pulmonary vasculature that cause right ventricular dysfunction. Systemic consequences of right ventricular dysfunction include damage to other solid organs, such as the liver. However, the profiles and consequences of hepatic injury due to PAH and CTEPH have not been well-studied. Methods: We aimed to identify underlying patterns of liver injury in a cohort of PAH and CTEPH patients enrolled in 15 randomized clinical trials conducted between 1998 and 2012. We used unsupervised machine learning to identify liver injury clusters in 13 trials and validated the findings in two additional trials. We then determined whether these liver injury clusters were associated with clinical outcomes or treatment effect heterogeneity. Results: Our training dataset included 4,219 patients and our validation dataset included 1,756 patients with complete liver laboratory panels (serum total bilirubin, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin). Using k-means clustering paired with factor analysis, we identified four unique liver phenotypes (no liver injury, hepatocellular injury, cholestatic injury, and combined injury patterns). Patients in the cholestatic injury liver cluster had the shortest time to clinical worsening and highest chance of worsening World Health Organization functional class. Randomization to the experimental arm was associated with a transition to healthier liver clusters compared to randomization to the control arm. The cholestatic injury group experienced the greatest placebo-corrected treatment benefit in terms of six-minute walk distance. Conclusions: Liver injury patterns were associated with adverse outcomes in patients with PAH and CTEPH. Randomization to active treatment of pulmonary hypertension in these clinical trials had beneficial effects on liver health compared to placebo. The independent role of liver disease (often subclinical) in determining outcomes warrants prospective studies of the clinical utility of liver phenotyping for PAH prognosis and contribution to clinical disease.

Right ventricular myocardial energetic model for evaluating right heart function in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) increases right ventricular (RV) workload and decreases myocardial oxygen reserve, eventually leading to poor cardiac output. This study created and assessed a novel model of RV work output based on RV hemodynamics and oxygen supply, allowing new insight into causal mechanisms of RV dysfunction. METHODS: The RV function model was built upon an earlier, left ventricular model and further adjusted for more accurate clinical use. The model assumes that RV total power output (1) is the sum of isovolumic and stroke power and (2) is linearly related to its right coronary artery oxygen supply. Thus, when right coronary artery flow is limited or isovolumic power is elevated, less energy is available for producing cardiac output. The original and adjusted models were validated via data from patients with idiopathic PAH (n = 14) and large animals (n = 6) that underwent acute pulmonary banding with or without hypoxia. RESULTS: Both models demonstrated strong, significant correlations between RV oxygen consumption rate and RV total power output for PAH patients (original model, R2  = 0.66; adjusted model, R2  = 0.78) and sheep (original, R2  = 0.85; adjusted, R2  = 0.86). Furthermore, the models demonstrate a significant inverse relationship between required oxygen consumption and RV efficiency (stroke power/total power) (p < 0.001). Lastly, higher NYHA class was indicative of lower RV efficiency and higher oxygen consumption (p = 0.013). CONCLUSION: Right ventricular total power output can be accurately estimated directly from pulmonary hemodynamics and right coronary perfusion during PAH. This model highlights the increased vulnerability of PAH patients with compromised right coronary flow coupled with high afterload.

Therapeutic Ultrasound Triggered Silk Fibroin Scaffold Degradation.

A patient's capacity for tissue regeneration varies based on age, nutritional status, disease state, lifestyle, and gender. Because regeneration cannot be predicted prior to biomaterial implantation, there is a need for responsive biomaterials with adaptive, personalized degradation profiles to improve regenerative outcomes. This study reports a new approach to use therapeutic ultrasound as a means of altering the degradation profile of silk fibroin biomaterials noninvasively postimplantation. By evaluating changes in weight, porosity, surface morphology, compressive modulus, and chemical structure, it is concluded that therapeutic ultrasound can trigger enhanced degradation of silk fibroin scaffolds noninvasively. By removing microbubbles on the scaffold surface, it is found that acoustic cavitation is the mechanism responsible for changing the degradation profile. This method is proved to be safe for human cells with no negative effects on cell viability or metabolism. Sonication through human skin also effectively triggers scaffold degradation, increasing the clinical relevance of these results. These findings suggest that silk is an ultrasound-responsive biomaterial, where the degradation profile can be adjusted noninvasively to improve regenerative outcomes.

Enrichment Benefits of Risk Algorithms for Pulmonary Arterial Hypertension Clinical Trials.

Rationale: Event-driven primary endpoints are increasingly used in pulmonary arterial hypertension clinical trials, substantially increasing required sample sizes and trial lengths. The U.S. Food and Drug Administration advocates the use of prognostic enrichment of clinical trials by preselecting a patient population with increased likelihood of experiencing the trial's primary endpoint.Objectives: This study compares validated clinical scales of risk (Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension, the French score, and Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension Disease Management [REVEAL] 2.0) to identify patients who are likely to experience a clinical worsening event for trial enrichment.Methods: Baseline data from three pulmonary arterial hypertension clinical trials (AMBITION [a Study of First-Line Ambrisentan and Tadalafil Combination Therapy in Subjects with Pulmonary Arterial Hypertension], SERAPHIN [Study of Macitentan on Morbidity and Mortality in Patients with Symptomatic Pulmonary Arterial Hypertension], and GRIPHON [Selexipag in Pulmonary Arterial Hypertension]) were pooled and standardized. Receiver operating curves were used to measure each algorithm's performance in predicting clinical worsening within the pooled placebo cohort. Power simulations were conducted to determine sample size and treatment time reductions for multiple enrichment strategies. A cost analysis was performed to illustrate potential financial savings by applying enrichment to GRIPHON.Measurements and Main Results: All risk algorithms were compared using area under the receiver operating curve and substantially outperformed prediction per New York Heart Association Functional Class. The REVEAL 2.0's risk grouping provided the greatest time and sample size savings in AMBITION and GRIPHON for all enrichment strategies but lacked appropriate inputs (i.e., N-terminal-proB-type natriuretic peptide) to perform as well in SERAPHIN. Cost analysis applied to GRIPHON demonstrated the greatest financial benefit by enrolling patients with a REVEAL score ≥8.Conclusions: This preliminary study demonstrates the feasibility of risk algorithms for pulmonary arterial hypertension trial enrichment and a need for further investigation.

Development and Validation of an Abridged Version of the REVEAL 2.0 Risk Score Calculator, REVEAL Lite 2, for Use in Patients With Pulmonary Arterial Hypertension.

BACKGROUND: Achievement of low-risk status is a treatment goal in pulmonary arterial hypertension (PAH). Risk assessment often is performed using multiparameter tools, such as the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) risk calculator. Risk calculators that assess fewer variables without compromising validity may expedite risk assessment in the routine clinic setting. We describe the development and validation of REVEAL Lite 2, an abridged version of REVEAL 2.0. RESEARCH QUESTION: Can a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH be developed and validated? STUDY DESIGN AND METHODS: REVEAL Lite 2 includes six noninvasive variables-functional class (FC), vital signs (systolic BP [SBP] and heart rate), 6-min walk distance (6MWD), brain natriuretic peptide (BNP)/N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and renal insufficiency (by estimated glomerular filtration rate [eGFR])-and was validated in a series of analyses (Kaplan-Meier, concordance index, Cox proportional hazard model, and multivariate analysis). RESULTS: REVEAL Lite 2 approximates REVEAL 2.0 at discriminating low, intermediate, and high risk for 1-year mortality in patients in the REVEAL registry. The model indicated that the most highly predictive REVEAL Lite 2 parameter was BNP/NT-proBNP, followed by 6MWD and FC. Even if multiple, less predictive variables (heart rate, SBP, eGFR) were missing, REVEAL Lite 2 still discriminated among risk groups. INTERPRETATION: REVEAL Lite 2, an abridged version of REVEAL 2.0, provides a simplified method of risk assessment that can be implemented routinely in daily clinical practice. REVEAL Lite 2 is a robust tool that provides discrimination among patients at low, intermediate, and high risk of 1-year mortality. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT00370214; URL: www.clinicaltrials.gov.

Risk stratification in pulmonary arterial hypertension using Bayesian analysis.

BACKGROUND: Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0. METHODS: We derived a tree-augmented naïve Bayes model (titled PHORA) to predict 1-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in the COMPERA and PHSANZ registries). Patients were classified as low-, intermediate- and high-risk (<5%, 5-20% and >10% 12-month mortality, respectively) based on the 2015 European Society of Cardiology/European Respiratory Society guidelines. RESULTS: PHORA had an area under the curve (AUC) of 0.80 for predicting 1-year survival, which was an improvement over REVEAL 2.0 (AUC 0.76). When validated in the COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80, respectively. 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p<0.001), with excellent separation between low-, intermediate- and high-risk groups in all three registries. CONCLUSION: Our Bayesian network-derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of the ability of Bayesian network-based models to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.