Screening strategies for pulmonary arterial hypertension

Pulmonary arterial hypertension: Navigating the pathways of progress in diagnosis, treatment, and patient care

Pulmonary arterial hypertension (PAH) is a form of precapillary pulmonary hypertension caused by a complex process of endothelial dysfunction and vascular remodeling. If left untreated, this progressive disease presents with symptoms of incapacitating fatigue causing marked loss of quality of life, eventually culminating in right ventricular failure and death. Patient management is complex and based on accurate diagnosis, risk stratification, and treatment initiation, with close monitoring of response and disease progression. Understanding the underlying pathophysiology has enabled the development of multiple drugs directed at different targets in the pathological chain. Vasodilator therapy has been the mainstay approach for the last few years, significantly improving quality of life, functional status, and survival. Recent advances in therapies targeting dysfunctional pathways beyond endothelial dysfunction may address the fundamental processes underlying the disease, raising the prospect of increasingly effective options for this high-risk group of patients with a historically poor prognosis.

Individualized survival predictions using state space model with longitudinal and survival data

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Pulmonary Hypertension in Connective Tissue Diseases Other than Systemic Sclerosis

Pulmonary hypertension (PH) is a known complication of certain connective tissue diseases (CTDs), with systemic sclerosis (SSc) being the most common in the Western world. However, PH in association with non-SSc CTD such as systemic lupus erythematous, mixed connective tissue disease, and primary Sjögren's syndrome constitutes a distinct subset of patients with inherently different epidemiologic profiles, pathophysiologic mechanisms, clinical features, therapeutic options, and prognostic implications. The purpose of this review is to inform a practical approach for clinicians evaluating patients with non-SSc CTD-associated PH.The development of PH in these patients involves a complex interplay between genetic factors, immune-mediated mechanisms, and endothelial cell dysfunction. Furthermore, the broad spectrum of CTD manifestations can contribute to the development of PH through various pathophysiologic mechanisms, including intrinsic pulmonary arteriolar vasculopathy (pulmonary arterial hypertension, Group 1 PH), left-heart disease (Group 2), chronic lung disease (Group 3), chronic pulmonary artery obstruction (Group 4), and unclear and/or multifactorial mechanisms (Group 5). The importance of diagnosing PH early in symptomatic patients with non-SSc CTD is highlighted, with a review of the relevant biomarkers, imaging, and diagnostic procedures required to establish a diagnosis.Therapeutic strategies for non-SSc PH associated with CTD are explored with an in-depth review of the medical, interventional, and surgical options available to these patients, emphasizing the CTD-specific considerations that guide treatment and aid in prognosis. By identifying gaps in the current literature, we offer insights into future research priorities that may prove valuable for patients with PH associated with non-SSc CTD.

Identification of Serum Interleukin-22 as Novel Biomarker in Pulmonary Hypertension: A Translational Study

Growing evidence suggests the crucial involvement of inflammation in the pathogenesis of pulmonary hypertension (PH). The current study analyzed the expression of interleukin (IL)-17a and IL-22 as potential biomarkers for PH in a preclinical rat model of PH as well as the serum levels in a PH patient collective. PH was induced by monocrotalin (60 mg/kg body weight s.c.) in 10 Sprague Dawley rats (PH) and compared to 6 sham-treated controls (CON) as well as 10 monocrotalin-induced, macitentan-treated rats (PH_MAC). Lung and cardiac tissues were subjected to histological and immunohistochemical analysis for the ILs, and their serum levels were quantified using ELISA. Serum IL levels were also measured in a PH patient cohort. IL-22 expression was significantly increased in the lungs of the PH and PH_MAC groups (p = 0.002), whereas increased IL17a expression was demonstrated only in the lungs and RV of the PH (p < 0.05) but not the PH_MAC group (p = n.s.). The PH group showed elevated serum concentrations for IL-22 (p = 0.04) and IL-17a (p = 0.008). Compared to the PH group, the PH_MAC group demonstrated a decrease in IL-22 (p = 0.021) but not IL17a (p = n.s.). In the PH patient collective (n = 92), increased serum levels of IL-22 but not IL-17a could be shown (p < 0.0001). This elevation remained significant across the different etiological groups (p < 0.05). Correlation analysis revealed multiple significant relations between IL-22 and various clinical, laboratory, functional and hemodynamic parameters. IL-22 could serve as a promising inflammatory biomarker of PH with potential value for initial diagnosis, functional classification or even prognosis estimation. Its validation in larger patients' cohorts regarding outcome and survival data, as well as the probability of promising therapeutic target structures, remains the object of further studies.

Systemic scleroderma: Review and updated approach and case description to addressing pulmonary arterial hypertension and idiopathic pulmonary fibrosis: A dual challenge in treatment

Pulmonary arterial hypertension (PAH), idiopathic pulmonary fibrosis (IPF), and scleroderma (SSc) are three interrelated medical conditions that can result in significant morbidity and mortality. Pulmonary hypertension, a condition marked by high blood pressure in the lungs, can lead to heart failure and other complications. Idiopathic pulmonary fibrosis, a progressive lung disease characterised by scarring of lung tissue, can cause breathing difficulties and impaired oxygenation. Scleroderma, an autoimmune disease, can induce thickening and hardening of the skin and internal organs, including the lungs, leading to pulmonary fibrosis and hypertension. Currently, there is no cure for any of these conditions. However, early detection and proper management can improve the quality of life and prognosis of a patient. This review focusses on PH and IPF in patients with SSc, providing information on the causes, symptoms, and treatment of these conditions, together with illustrative images. It also provides an overview of interrelated medical conditions: PH, IPF, and SSc. It emphasises the importance of early detection and proper management to improve patient quality of life and prognosis.

Two prospective, multicenter studies for the identification of biomarker signatures for early detection of pulmonary hypertension (PH): The CIPHER and CIPHER-MRI studies

A blood test identifying patients at increased risk of pulmonary hypertension (PH) could streamline the investigative pathway. The prospective, multicenter CIPHER study aimed to develop a microRNA-based signature for detecting PH in breathless patients and enrolled adults with a high suspicion of PH who had undergone right heart catheterization (RHC). The CIPHER-MRI study was added to assess the performance of this CIPHER signature in a population with low probability of having PH who underwent cardiac magnetic resonance imaging (cMRI) instead of RHC. The microRNA signature was developed using a penalized linear regression (LASSO) model. Data were modeled both with and without N-terminal pro-brain natriuretic peptide (NT-proBNP). Signature performance was assessed against predefined thresholds (lower 98.7% CI bound of ≥0.73 for sensitivity and ≥0.53 for specificity, based on a meta-analysis of echocardiographic data), using RHC as the true diagnosis. Overall, 926 CIPHER participants were screened and 888 were included in the analysis. Of 688 RHC-confirmed PH cases, approximately 40% were already receiving PH treatment. Fifty microRNA (from 311 investigated) were algorithmically selected to be included in the signature. Sensitivity [97.5% CI] of the signature was 0.85 [0.80-0.89] for microRNA-alone and 0.90 [0.86-0.93] for microRNA+NT-proBNP, and the corresponding specificities were 0.33 [0.24-0.44] and 0.28 [0.20-0.39]. Of 80 CIPHER-MRI participants with evaluable data, 7 were considered PH-positive by cMRI whereas 52 were considered PH-positive by the microRNA signature. Due to low specificity, the CIPHER miRNA-based signature for PH (either with or without NT-proBNP in model) did not meet the prespecified diagnostic threshold for the primary analysis.

Electrocardiogram Parameters Associated With the Diagnosis of Pulmonary Hypertension in High-Altitude Tibetan Populations: A Retrospective Single-Centre Study

BACKGROUND

Electrocardiogram (ECG) is a commonly used diagnostic method for pulmonary hypertension (PH) in Tibetan areas, but its sensitivity and specificity are not good enough. This study aimed to investigate the ECG parameters associated with the diagnosis of PH in Tibetan areas.

METHODS

Ninety-four PH patients of Tibetan ethnicity who were treated at the hospital between March 2019 and October 2020, and 52 Tibetan individuals as controls, were included. The ECG parameters were compared between groups. Multivariate logistic analysis was performed to identify the ECG parameters that can be used for the diagnosis of PH. The univariate significances of ECG parameters were included in the multivariate analyses, whereas those exhibiting opposite trends between different PH subtypes were excluded.

RESULTS

Two ECG parameters were significant in multivariate analysis. The final model included S wave amplitude in lead V3 (OR 5.81; 95% CI 2.79-12.11; p<0.001) and a negative T wave in leads V1-V3 (OR 0.05; 95% CI 0.01-0.41; p=0.005). The ROC curve analysis on the final model yielded an AUC of 0.830 (95% CI 0.766-0.894; p<0.001), indicating good diagnostic performance. A nomogram for diagnosis of PH was also established using S wave amplitude in lead V3 and a negative T wave in leads V1-V3.

CONCLUSION

The ECG parameters S wave amplitude in lead V3 and a negative T wave in leads V1-V3 were independent factors associated with the diagnosis of PH in high-altitude Tibetan populations.

Symptom-related screening programme for early detection of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism: the SYSPPE study

BACKGROUND

Chronic thromboembolic pulmonary hypertension (CTEPH) is the most severe long-term complication of acute pulmonary embolism (PE). We aimed to evaluate the impact of a symptom screening programme to detect CTEPH in PE survivors.

METHODS

This was a multicentre cohort study of patients diagnosed with acute symptomatic PE between January 2017 and December 2018 in 16 centres in Spain. Patients were contacted by phone 2 years after the index PE diagnosis. Those with dyspnoea corresponding to a New York Heart Association (NYHA)/WHO scale≥II, visited the outpatient clinic for echocardiography and further diagnostic tests including right heart catheterisation (RHC). The primary outcome was the new diagnosis of CTEPH confirmed by RHC.

RESULTS

Out of 1077 patients with acute PE, 646 were included in the symptom screening. At 2 years, 21.8% (n=141) reported dyspnoea NYHA/WHO scale≥II. Before symptom screening protocol, five patients were diagnosed with CTEPH following routine care. In patients with NYHA/WHO scale≥II, after symptom screening protocol, the echocardiographic probability of pulmonary hypertension (PH) was low, intermediate and high in 76.6% (n=95), 21.8% (n=27) and 1.6% (n=2), respectively. After performing additional diagnostic test in the latter 2 groups, 12 additional CTEPH cases were confirmed.

CONCLUSIONS

The implementation of this simple strategy based on symptom evaluation by phone diagnosed more than doubled the number of CTEPH cases. Dedicated follow-up algorithms for PE survivors help diagnosing CTEPH earlier.

TRIAL REGISTRATION NUMBER

NCT03953560.

Prevalence, risk factors and echocardiographic predictors of pulmonary hypertension in systemic lupus erythematosus: towards a screening protocol

BACKGROUND

Systemic lupus erythematosus (SLE) significantly affects the lungs and heart, and pulmonary hypertension (PH) is a severe manifestation that leads to considerable morbidity and mortality.

OBJECTIVES

We aimed to determine the prevalence and risk factors of probable SLE-PH, assess the main echocardiographic predictors and develop a potential screening strategy.

METHODS

A prospective single-centre study was conducted on 201 patients with SLE who underwent transthoracic echocardiography. Patients meeting PH criteria were referred for right heart catheterisation (RHC).

RESULTS

Among patients, 88.56% were women, 85.57% were of Spanish origin and 43.78% had structural heart disease. Out of these, 16 (7.96%) had intermediate or high probability criteria for PH according to European Society of Cardiology (ESC) 2022. Six RHCs confirmed PH with a prevalence of 2.99% for SLE-PH and 1.99% for SLE-pulmonary arterial hypertension (PAH).

KEY RISK FACTORS

Key risk factors included age, cardiorespiratory symptoms, serositis, anti-Ro, cardiac biomarkers and altered pulmonary function tests (PFTs). PH was linked to a higher Systemic Lupus International Collaborative Clinics/American College of Rheumatology Damage Index (SDI) (mean SDI 4.75 vs 2.05, p<0.001) and increased mortality risk in a 2-year follow-up (12.50% vs 1.08%, p=0.002).

CONCLUSION

In our cohort, 7.96% of patients with SLE had an intermediate or high PH probability. By RHC, six patients (2.99%) met the ESC/European Respiratory Society criteria for PH and four (1.99%) for PAH. The main risk factors were older age, cardiorespiratory symptoms, serositis, anti-Ro, cardiac biomarkers and altered PFTs. PH was a severe SLE complication, suggesting the need for earlier diagnosis through data-driven screening to reduce associated morbidity and mortality.

The Early Detection of Pulmonary Hypertension

BACKGROUND

Up to 1% of the world population and 10% of all persons over age 65 suffer from pulmonary hypertension (PH). The latency from the first symptom to the diagnosis is more than one year on average, and more than three years in 20% of patients. 40% seek help from more than four different physicians until their condition is finally diagnosed.

METHODS

This review is based on publications retrieved by a selective literature search on pulmonary hypertension.

RESULTS

The most common causes of pulmonary hypertension are left heart diseases and lung diseases. Its cardinal symptom is exertional dyspnea that worsens as the disease progresses. Additional symptoms of right heart failure are seen in advanced stages. Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) are rare, difficult to diagnose, and of particular clinical relevance because specific treatments are available. For this reason, strategies for the early detection of PAH and CTEPH have been developed. The clinical suspicion of PH arises in a patient who has nonspecific symptoms, electrocardiographic changes, and an abnormal (NT-pro-)BNP concentration. Once the suspicion of PH has been confirmed by echocardiography and, if necessary, differential-diagnostic evaluation with a cardiopulmonary stress test, and after the exclusion of a primary left heart disease or lung disease, the patient should be referred to a PH center for further diagnostic assessment, classification, and treatment.

CONCLUSION

If both the (NT-pro-)BNP and the ECG are normal, PH is unlikely. Knowledge of the characteristic clinical manifestations and test results of PH is needed so that patients can be properly selected for referral to specialists and experts in PH.

What changed after the 2022 guidelines for pulmonary hypertension?

With an estimated prevalence of around 1%, pulmonary hypertension (PH) presents a relevant burden worldwide. In this review, we aim to give an overview of the novelties from the revised European Society of Cardiology (ESC) /European Respiratory Society (ERS) guidelines for the diagnosis and treatment of PH and their implication for the everyday clinical practice.

[Diagnostic Algorithm and Screening of Pulmonary Hypertension]

The new guidelines for the diagnosis and treatment of pulmonary hypertension include a new diagnostic algorithm and provide specific recommendations for the required diagnostic procedures, including screening methods. These recommendations are commented on by national experts under the auspices of the DACH. These comments provide additional decision support and background information, serving as a further guide for the complex diagnosis of pulmonary hypertension.

Pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a rare and progressive disease characterised by remodelling of the pulmonary arteries and progressive narrowing of the pulmonary vasculature. This leads to a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure and, if left untreated, to right ventricular failure and death. A correct diagnosis requires a complete work-up including right heart catheterisation performed in a specialised centre. Although our knowledge of the epidemiology, pathology and pathophysiology of the disease, as well as the development of innovative therapies, has progressed in recent decades, PAH remains a serious clinical condition. Current treatments for the disease target the three specific pathways of endothelial dysfunction that characterise PAH: the endothelin, nitric oxide and prostacyclin pathways. The current treatment algorithm is based on the assessment of severity using a multiparametric risk stratification approach at the time of diagnosis (baseline) and at regular follow-up visits. It recommends the initiation of combination therapy in PAH patients without cardiopulmonary comorbidities. The choice of therapy (dual or triple) depends on the initial severity of the condition. The main treatment goal is to achieve low-risk status. Further escalation of treatment is required if low-risk status is not achieved at subsequent follow-up assessments. In the most severe patients, who are already on maximal medical therapy, lung transplantation may be indicated. Recent advances in understanding the pathophysiology of the disease have led to the development of promising emerging therapies targeting dysfunctional pathways beyond endothelial dysfunction, including the TGF-β and PDGF pathways.

Scleroderma pulmonary arterial hypertension: the same as idiopathic pulmonary arterial hypertension?

PURPOSE OF REVIEW

Pulmonary arterial hypertension (PAH) is a common complication of systemic sclerosis (SSc), which confers significant morbidity and mortality. The current therapies and treatment strategies for SSc-associated PAH (SSc-PAH) are informed by those used to treat patients with idiopathic PAH (IPAH). There are, however, important differences between these two diseases that impact diagnosis, treatment, and outcomes.

RECENT FINDINGS

Both SSc-PAH and IPAH are incompletely understood with ongoing research into the underlying cellular biology that characterize and differentiate the two diseases. Additional research seeks to improve identification among SSc patients in order to diagnose patients earlier in the course of their disease. Novel therapies specifically for SSc-PAH such as rituximab and dimethyl fumarate are under investigation.

SUMMARY

Although patients with SSc-PAH and IPAH present with similar symptoms, there are significant differences between these two forms of PAH that warrant further investigation and characterization of optimal detection strategies, treatment algorithms, and outcomes assessment.

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning

BACKGROUND

Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes?

METHODS AND RESULTS

Adults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012-2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients' 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater.

CONCLUSIONS

A deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.

Cardiac amyloidosis presenting as pulmonary arterial hypertension: A case report

BACKGROUND

Pulmonary hypertension is a rare cardiopulmonary disease, with an insidious onset that usually worsens rapidly. Amyloid light chain (AL) amyloidosis is a rare systemic disease caused by extracellular deposition of pathologic, insoluble, and proteinaceous fibrils in organs and tissues; however, it is difficult to diagnose given its varied and nonspecific symptoms. To date, rare cases of amyloidosis with pulmonary hypertension have been reported. Of note, the optimal treatments for cardiac amyloidosis complicated with pulmonary hypertension remain unclear.

CASE SUMMARY

We report a case of a 51-year-old woman who presented with progressively worsening dyspnea. Transthoracic echocardiography indicated severe pulmonary hypertension. Twenty-seven months after first admission, the patient returned with symptoms of progressive heart failure. A myocardial tissue sample stained with Congo red was positive, and the patient was ultimately diagnosed with AL amyloidosis with cardiac involvement.

CONCLUSION

Although pulmonary hypertension may be idiopathic, it is frequently associated with other conditions. In rare cases, pulmonary hypertension can be a complication of AL amyloidosis, which should be seriously considered in any adult presenting with nonspecific signs or symptoms of cardiac distress.

A claims-based, machine-learning algorithm to identify patients with pulmonary arterial hypertension

Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine-learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de-identified data from the US-based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non-PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non-PAH at diagnosis and at 6 months prediagnosis. The PAH and non-PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non-PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non-PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH-specific screening and/or earlier specialist referral.

Time to diagnosis of pulmonary hypertension and diagnostic burden: A retrospective analysis of nationwide US healthcare data

The main aim of this analysis was to investigate time from symptom onset (chronic unexplained dyspnoea [CUD]) to diagnosis of Group 1 pulmonary hypertension (PH)-pulmonary arterial hypertension (PAH)-and to characterize healthcare resource utilization leading up to diagnosis using a nationwide US claims and an electronic health record (EHR) database from Optum©. Eligible patients were ≥18 years old at first CUD diagnosis (index event) and had a PAH diagnosis on or after index date. Based on administrative codes, PAH was defined as right heart catheterization (RHC), ≥ 2 PAH diagnoses (1 within a year of RHC), and ≥1 post-RHC prescription for PAH treatment. All values are median (1st quartile-3rd quartile) unless otherwise stated. Of 854,722 patients with CUD in the claims database, 582 (0.1%) had PAH. Time from CUD to PAH diagnosis was 2.26 (0.73-4.22) years. PAH patients experienced 3 (2-4) transthoracic echocardiograms (TTEs), 6 (3-12) specialist visits, and 2 (1-4) hospitalizations during the diagnostic interval. Almost one-third of patients (29%) waited 10 months or more to have a TTE. Findings from the EHR database were broadly similar. Resource utilization during the diagnostic interval was also analyzed in an overall PH cohort: findings were generally similar to the PAH cohort (2 [1-3] TTEs, 4 [2-9] specialist visits and 2 [1-4] hospitalizations). These data indicate a delay in the diagnostic pathway for PAH, and illustrate the burden associated with PAH diagnosis.

Diagnosis and management of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive disease of the pulmonary vasculature that is characterised by elevated pressures within the pulmonary vascular tree. Recent decades have witnessed a dramatic expansion in our understanding of the pathobiology and the epidemiology of PAH, and improvements in treatment options and outcomes. The prevalence of PAH is estimated to be between 48 and 55 cases per million adults. The definition was recently amended and a diagnosis of PAH now requires evidence of a mean pulmonary artery pressure >20 mmHg, a pulmonary vascular resistance >2 Wood units and a pulmonary artery wedge pressure ≤15 mmHg at right heart catheterisation. Detailed clinical assessment and a number of additional diagnostic tests are required to assign a clinical group. Biochemistry, echocardiography, lung imaging and pulmonary function tests provide valuable information to assist in the assignment of a clinical group. Risk assessment tools have been refined, and these greatly facilitate risk stratification and enhance treatment decisions and prognostication. Current therapies target three therapeutic pathways: the nitric oxide, prostacyclin and endothelin pathways. While lung transplantation remains the only curative intervention for PAH, there are a number of promising therapies under investigation which may further reduce morbidity and improve outcomes. This review describes the epidemiology, pathology and pathobiology of PAH and introduces important concepts regarding the diagnosis and risk stratification of PAH. The management of PAH is also discussed, with a special focus on PAH specific therapy and key supportive measures.

Professional expectations and patient expectations concerning the development of Artificial Intelligence (AI) for the early diagnosis of Pulmonary Hypertension (PH)

The expectations of professionals working on the development of healthcare Artificial Intelligence (AI) technologies and the patients who will be affected by them have received limited attention. This paper reports on a Foresight Workshop with professionals involved with pulmonary hypertension (PH) and a Focus Group with members of a PH patient group, to discuss expectations of AI development and implementation. We show that while professionals and patients had similar expectations of AI, with respect to the priority of early diagnosis; data risks of privacy and reuse; and responsibility, other expectations differed. One important point of difference was in the attitude toward using AI to point up other potential health problems (in addition to PH). A second difference was in the expectations regarding how much clinical professionals should know about the role of AI in diagnosis. These findings allow us to better prepare for the future by providing a frank appraisal of the complexities of AI development with foresight, and the anxieties of key stakeholders.

A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic sclerosis-associated pulmonary hypertension

Background: Systemic sclerosis-associated pulmonary hypertension (SSc-PH) is one of the most common causes of death in patients with systemic sclerosis (SSc). The complexity of SSc-PH and the heterogeneity of clinical features in SSc-PH patients contribute to the difficulty of diagnosis. Therefore, there is a pressing need to develop and optimize models for the diagnosis of SSc-PH. Signal recognition particle (SRP) deficiency has been found to promote the progression of multiple cancers, but the relationship between SRP and SSc-PH has not been explored. Methods: First, we obtained the GSE19617 and GSE33463 datasets from the Gene Expression Omnibus (GEO) database as the training set, GSE22356 as the test set, and the SRP-related gene set from the MSigDB database. Next, we identified differentially expressed SRP-related genes (DE-SRPGs) and performed unsupervised clustering and gene enrichment analyses. Then, we used least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) to identify SRP-related diagnostic genes (SRP-DGs). We constructed an SRP scoring system and a nomogram model based on the SRP-DGs and established an artificial neural network (ANN) for diagnosis. We used receiver operating characteristic (ROC) curves to identify the SRP-related signature in the training and test sets. Finally, we analyzed immune features, signaling pathways, and drugs associated with SRP and investigated SRP-DGs' functions using single gene batch correlation analysis-based GSEA. Results: We obtained 30 DE-SRPGs and found that they were enriched in functions and pathways such as "protein targeting to ER," "cytosolic ribosome," and "coronavirus disease-COVID-19". Subsequently, we identified seven SRP-DGs whose expression levels and diagnostic efficacy were validated in the test set. As one signature, the area under the ROC curve (AUC) values for seven SRP-DGs were 0.769 and 1.000 in the training and test sets, respectively. Predictions made using the nomogram model are likely beneficial for SSc-PH patients. The AUC values of the ANN were 0.999 and 0.860 in the training and test sets, respectively. Finally, we discovered that some immune cells and pathways, such as activated dendritic cells, complement activation, and heme metabolism, were significantly associated with SRP-DGs and identified ten drugs targeting SRP-DGs. Conclusion: We constructed a reliable SRP-related ANN model for the diagnosis of SSc-PH and investigated the possible role of SRP in the etiopathogenesis of SSc-PH by bioinformatics methods to provide a basis for precision and personalized medicine.

IMproving PULmonary hypertension Screening by Echocardiography: IMPULSE

BACKGROUND

The world symposium on pulmonary hypertension (PH) has proposed that PH be defined as a mean pulmonary artery pressure (mPAP) > 20 mmHg as assessed by right heart catheterisation (RHC). Transthoracic echocardiography (TTE) is an established screening tool used for suspected PH. International guidelines recommend a multi-parameter assessment of the TTE PH probability although effectiveness has not been established using real world data.

STUDY AIMS

To determine accuracy of the European Society of Cardiology (ESC) and British Society of Echocardiography (BSE) TTE probability algorithm in detecting PH in patients attending a UK PH centre. To identify echocardiographic markers and revised algorithms to improve the detection of PH in those with low/intermediate BSE/ESC TTE PH probability.

METHODS

TTE followed by RHC (within 4 months after) was undertaken in patients for suspected but previously unconfirmed PH. BSE/ESC PH TTE probabilities were calculated alongside additional markers of right ventricular (RV) longitudinal and radial function, and RV diastolic function. A refined IMPULSE algorithm was devised and evaluated in patients with low and/or intermediate ESC/BSE TTE PH probability.

RESULTS

Of 310 patients assessed, 236 (76%) had RHC-confirmed PH (average mPAP 42.8 ± 11.7). Sensitivity and specificity for detecting PH using the BSE/ESC recommendations was 89% and 68%, respectively. 36% of those with low BSE/ESC TTE probability had RHC-confirmed PH and BSE/ESC PH probability parameters did not differ amongst those with and without PH in the low probability group. Conversely, RV free wall longitudinal strain (RVFWLS) was lower in patients with vs. without PH in low BSE/ESC probability group (- 20.6 ± 4.1% vs - 23.8 ± 3.9%) (P < 0.02). Incorporating RVFWLS and TTE features of RV radial and diastolic function (RVFAC and IVRT) within the IMPULSE algorithm reduced false negatives in patients with low BSE/ESC PH probability by 29%. The IMPULSE algorithm had excellent specificity and positive predictive value in those with low (93%/80%, respectively) or intermediate (82%/86%, respectively) PH probability.

CONCLUSION

Existing TTE PH probability guidelines lack sensitivity to detect patients with milder haemodynamic forms of PH. Combining additional TTE makers assessing RV radial, longitudinal and diastolic function enhance identification of milder forms of PH, particularly in those who have a low BSE/ESC TTE PH probability.

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension

BACKGROUND

Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension.

OBJECTIVES

This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications.

METHODS

From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan.

RESULTS

Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort.

CONCLUSIONS

The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Physical Examination for the Detection of Pulmonary Hypertension: A Systematic Review

We performed a systematic review to determine whether the physical examination can reliably assist in the diagnostic approach for patients suspected of having pulmonary hypertension (PH). Using dual extraction, two investigators independently searched PubMed, Ovid MEDLINE, Cochrane Library, and Embase for studies that compared physical examination findings with a right heart catheterization, from inception until July 10, 2021. We obtained data from four studies that evaluated physical examination findings in patients receiving a right heart catheterization to diagnose PH. Pooled diagnostic odds ratios (DOR) were calculated for right ventricular heave, a loud pulmonic component of the second heart sound (P2), jugular venous pressure (JVP) 3 cm above sternal angle, and a palpable P2. Three physical examination findings had DOR that supports the diagnosis of PH: the JVP > 3 cm above the sternal angle (5.90, 95% CI 2.57, 13.57), a loud P2 (2.91, 95% CI 1.38, 6.10), and a right ventricular heave (2.78, 95% CI 1.12, 6.89). The palpable P2 had a DOR less than one and was not able to be conclusive in diagnosing PH. Our systematic review found a small body of evidence supporting the use of physical examination tests in the diagnostic evaluation of pulmonary hypertension. The JVP > 3 cm above the sternal angle was the most accurate physical examination sign for the diagnosis of PH. Larger cohort studies using a combination of tests may shed more light on the role of the physical examination in the diagnosis and early detection of pulmonary hypertension.

Update on the Treatment of Pulmonary Arterial Hypertension

In the last decades, important advances have been made in the treatment of pulmonary arterial hypertension (PAH), a severe, progressive, incurable, and potentially fatal disease. For an adequate therapy, correct hemodynamic diagnosis and etiology classification are fundamental. Many etiologies - rheumatic disease, portal hypertension, congenital heart diseases, schistosomiasis - require specific measures, in addition to drug therapy for PAH. The specific therapy for PAH is based on medications that act on three pathophysiological pathways - prostacyclin, endothelin, and nitric oxide pathways. These drugs have multiple presentations (oral, intravenous, subcutaneous, and inhaled) and have changed the history of PAH. This review presents an overview of drug therapy strategies and different forms and peculiarities of PAH.

Advances in the management of pulmonary arterial hypertension

The management of pulmonary arterial hypertension (PAH) has significantly evolved over the last decades in the wake of more sensitive diagnostics and specialized clinical programs that can provide focused medical care. In the current era of PAH care, 1-year survival rates have increased to 86%–90% from 65% in the 1980s, and average long-term survival has increased to 6 years from 2.8 years. The heterogeneity in the etiology and disease course has opened doors to focusing research in phenotyping the disease and understanding the pathophysiology at a cellular and genetic level. This may eventually lead to precision medicine and the development of medications that may prevent or reverse pulmonary vascular remodeling. With more insight, clinical trial designs and primary end-points may change to identify the true survival benefit of pharmacotherapy. Identifying responders from non-responders to therapy may help provide individualized patient-centered care rather than an algorithm-based approach. The purpose of this review is to highlight the latest advances in screening, diagnosis, and management of PAH.

Impact of Three Different Algorithms for the Screening of SSc-PAH and Comparison with the Decisions of a Multidisciplinary Team

Background: to compare three existing screening algorithms of pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) with the results of a multidisciplinary team (MDT) meeting from a tertiary center. Methods: we conducted a monocentric longitudinal study from 2015 to 2018. All patients with SSc according to LeRoy’s classification were eligible. Patients were excluded in the case of missing data required by any of the three screening algorithms. The algorithms were applied for each patient at inclusion. Right heart catheterization (RHC) was performed based on the MDT decision. MDT members were all blinded from the results of the three algorithms regarding RHC recommendations. The RHC recommendations of each algorithm were compared with the MDT decision, and the impact on diagnosis and management was evaluated. Results: 117 SSc patients were consecutively included in the study, and 99 had follow-up data over the three-year duration of the study (10 deaths). Among the 117 patients, the MDT suggested RHC for 16 patients (14%), DETECT algorithm for 28 (24%), ASIG for 48 (41%) and ESC/ERS 2015 for 20 (17%). Among the 16 patients who had RHC, SSc-PAH was diagnosed in seven. Among patients with an initial recommendation of RHC based on at least one algorithm but not according to the MDT meeting, no SSc-PAH was diagnosed during the three-year follow-up. Results were unchanged when the new 2018 definition of PAH was applied instead of the previous definition. Conclusion: a MDT approach appears interesting for the screening of SSc-PAH, with a significant reduction of RHC performed in comparison with dedicated algorithms. The specific relevance of a MDT for the management and follow-up of patients with RHC recommended by existing algorithms but with no PAH warrants further studies.

A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach

BACKGROUND

Pulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models.

METHODS

Plasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis.

FINDINGS

20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells.

INTERPRETATION

This consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets.

FUNDING

This work was supported by a National Institute for Health Research Rare Disease Translational Research Collaboration (R29065/CN500) and British Heart Foundation Project Grant (PG/11/116/29288).

Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: machine learning on proteomics from the DETECT cohort

Pulmonary arterial hypertension (PAH) is a devastating complication of systemic sclerosis (SSc). Screening for PAH in SSc has increased detection, allowed early treatment for PAH and improved patient outcomes. Blood-based biomarkers that reliably identify SSc patients at risk of PAH, or with early disease, would significantly improve screening, potentially leading to improved survival, and provide novel mechanistic insights into early disease. The main objective of this study was to identify a proteomic biomarker signature that could discriminate SSc patients with and without PAH using a machine learning approach and to validate the findings in an external cohort.

Serum samples from patients with SSc and PAH (n=77) and SSc without pulmonary hypertension (non-PH) (n=80) were randomly selected from the clinical DETECT study and underwent proteomic screening using the Myriad RBM Discovery platform consisting of 313 proteins. Samples from an independent validation SSc cohort (PAH n=22 and non-PH n=22) were obtained from the University of Sheffield (Sheffield, UK).

Random forest analysis identified a novel panel of eight proteins, comprising collagen IV, endostatin, insulin-like growth factor binding protein (IGFBP)-2, IGFBP-7, matrix metallopeptidase-2, neuropilin-1, N-terminal pro-brain natriuretic peptide and RAGE (receptor for advanced glycation end products), that discriminated PAH from non-PH in SSc patients in the DETECT Discovery Cohort (average area under the receiver operating characteristic curve 0.741, 65.1% sensitivity/69.0% specificity), which was reproduced in the Sheffield Confirmatory Cohort (81.1% accuracy, 77.3% sensitivity/86.5% specificity).

This novel eight-protein biomarker panel has the potential to improve early detection of PAH in SSc patients and may provide novel insights into the pathogenesis of PAH in the context of SSc.

Early screening for pulmonary arterial hypertension in patients with systemic sclerosis improves patient outcome. This study identified a novel eight-protein biomarker panel that has the potential to assist early detection of PAH in this patient group.https://bit.ly/373BNkL

Predicting the Risk of Pulmonary Arterial Hypertension in Systemic Lupus Erythematosus: A Chinese Systemic Lupus Erythematosus Treatment and Research Group Cohort Study

OBJECTIVE

Pulmonary arterial hypertension (PAH) is a life-threatening complication of systemic lupus erythematosus (SLE). However, there is no algorithm to identify those at high risk. This study was undertaken to develop a prediction model for PAH in patients with lupus that provides individualized risk estimates.

METHODS

A multicenter, longitudinal cohort study was undertaken from January 2003 to January 2020. The study collected data on 3,624 consecutively evaluated patients diagnosed as having SLE. The diagnosis of PAH was confirmed by right-sided heart catheterization. Cox proportional hazards regression and least absolute shrinkage and selection operator were used to fit the model. Model discrimination, calibration, and decision curve analysis were performed for validation.

RESULTS

Ninety-two lupus patients (2.54%) developed PAH during a median follow-up of 4.84 years (interquartile range 2.42-8.84). The final prediction model included 5 clinical variables (acute/subacute cutaneous lupus, arthritis, renal disorder, thrombocytopenia, and interstitial lung disease) and 3 autoantibodies (anti-RNP, anti-Ro/SSA and anti-La/SSB). A 10-year PAH probability-predictive nomogram was established. The model was internally validated by Harrell's concordance index (0.78), the Brier score (0.03), and a satisfactory calibration curve. According to the net benefit and predicted probability thresholds, we recommend annual screening in high-risk (>4.62%) lupus patients.

CONCLUSION

We developed a risk stratification model using routine clinical assessments. This new tool may effectively predict the future risk of PAH in patients with SLE.

Pulmonary arterial hypertension in systemic sclerosis

Pulmonary arterial hypertension (PAH) is a frequent and severe complication of systemic sclerosis (SSc) due to combined vasculopathy and fibrogenesis. Early diagnosis and treatment are highly challenging in SSc-PAH and require referral to an expert PAH centre. Diagnostic algorithms evolved in the last decade. Novel therapeutic options notably targeting pulmonary vascular remodeling are needed.

The revised definition of pulmonary hypertension: exploring the impact on patient management

At the 6th World Symposium on Pulmonary Hypertension (PH), it was proposed that the mean pulmonary arterial pressure (mPAP) threshold used to define PH should be lowered from ≥25 mmHg to >20 mmHg. The rationale for this change is that the ≥25 mmHg threshold is arbitrary, whereas the revised threshold is based on scientific evidence. For the definition of all forms of pre-capillary PH, the inclusion of a pulmonary vascular resistance (PVR) ≥3 Wood Units was also proposed, placing greater emphasis on an elevated PVR to identify pulmonary vascular disease. Here, we discuss the possible impact of the revised definition of PH on future clinical management. This change may facilitate earlier PH detection, particularly in at-risk patient groups that are already undergoing screening programmes, e.g. those with systemic sclerosis or mutations associated with PH. As an mPAP above the upper limit of normal (>20 mmHg) but <25 mmHg is associated with increased risk of morbidity and mortality compared with a normal mPAP, early identification of patients in this group is important to enable close monitoring and timely treatment initiation once clinically indicated. Treatments currently approved for PH are not necessarily suitable for patients with an mPAP 21-24 mmHg, as the management of this group has not been widely examined. The revised definition may facilitate inclusion of these patients in prospective trials, allowing the evaluation of appropriate management strategies.

The burden of comorbidities in pulmonary arterial hypertension

Patients with comorbidities are often excluded from clinical trials, limiting the evidence base for pulmonary arterial hypertension (PAH)-specific therapies. This review aims to discuss the effect of comorbidities on the diagnosis and management of PAH. The comorbidities discussed in this review (systemic hypertension, obesity, sleep apnoea, clinical depression, obstructive airway disease, thyroid disease, diabetes, and ischaemic cardiovascular event) were chosen based on their prevalence in patients with idiopathic PAH in the REVEAL registry (Registry to EValuate Early and Long-term PAH disease management). Comorbidities can mask the symptoms of PAH, leading to delays in diagnosis and also difficulty evaluating disease progression and treatment effects. Due to the multifactorial pathophysiology of pulmonary hypertension (PH), the presence of comorbidities can lead to difficulties in distinguishing between Group 1 PH (PAH) and the other group classifications of PH. Many comorbidities contribute to the progression of PAH through increased pulmonary artery pressures and cardiac output, therefore treatment of the comorbidity may also reduce the severity of PAH. Similarly, the development of one comorbidity can be a risk factor for the development of other comorbidities. The management of comorbidities requires consideration of drug interactions, polypharmacy, adherence and evidence-based strategies. A multidisciplinary team should be involved in the management of patients with PAH and comorbidities, with appropriate referral to supportive services when necessary. The treatment goals and expectations of patients must be managed in the context of comorbidities.