Medical treatment of pulmonary hypertension in adults with congenital heart disease : updated and extended results from the International COMPERA-CHD Registry

Funding Information: The authors are indebted to the COMPERA investigators and their staff. We explicitly thank Dr. Claudia S. Copeland for the professional editing of the final draft of the manuscript. Funding: COMPERA is funded by unrestricted grants from Acceleron, Actelion Pharmaceuticals (Janssen), Bayer, OMT and GSK. These companies were not involved in data analysis or the writing of this manuscript. Funding Information: ICMJE uniform disclosure form (available at https:// dx.doi.org/10.21037/cdt-21-351). The series “Current Management Aspects in Adult Congenital Heart Disease (ACHD): Part IV” was commissioned by the editorial office without any funding or sponsorship. Dr. DH reports non-financial support from Actelion, Boehringer-Ingelheim, and Shire, outside the submitted work; Dr. DP reports personal fees from Actelion, Biogen, Aspen, Bayer, Boehringer Ingelheim, Daiichi Sankyo, and Sanofi, outside the submitted work; Dr. MD reports personal fees from Actelion, Bayer, GSK and MSD, outside the submitted work; Dr. HAG reports personal fees from Actelion, Bayer, Gilead, GSK, MSD, Pfizer and United Therapeutics, outside the submitted work; Dr. MG reports personal fees from Actelion, Bayer and GSK, outside the submitted work; Dr. MMH reports personal fees from Acceleron, Actelion, Bayer, MSD and Pfizer, outside the submitted work; Dr. CDV reports personal fees from Actelion, Bayer, GSK, MSD, Pfizer, and United Therapeutics, outside the submitted work; Dr. RE reports personal fees from Actelion, Boehringer Ingelheim, OMT, Bayer, and Berlin Chemie; grants from Actelion and Boehringer Ingelheim, outside the submitted work; Dr. MH reports grants and personal fees from Actelion, personal fees from Bayer, Berlin Chemie, Boehringer Ingelheim, GSK, Janssen, Novartis and MSD, outside the submitted work; Dr. MH reports personal fees from Acceleron, Actelion, AstraZeneca, Bayer, BERLIN CHEMIE, GSK, MSD, Novartis and OMT, outside the submitted work; Dr. HW reports personal fees from Action, Bayer, Biotest, Boehringer, GSK, Pfizer, and Roche, outside the submitted work; Dr. DS reports personal fees from Actelion, Bayer, and GSK, outside the submitted work; Dr. LS reports personal fees from Actelion, Bayer, and MSD, outside the submitted work; Dr. SU reports grants from Swiss National Science Foundation, Zurich Lung, Swiss Lung, and Orpha Swiss, grants and personal fees from Actelion SA/Johnson & Johnson, Switzerland, and MSD Switzerland, outside the submitted work; Dr. TJL reports personal fees from Actelion, Janssen-Cilag, BMS, MSD, and OMT GmbH, outside the submitted work; Dr. LB reports personal fees from Actelion, outside the submitted work; Dr. MC reports personal fees from Boehringer Ingelheim Pharma GmbH, Roche Pharma, and Boehringer Ingelheim, outside the submitted work; Dr. HW reports personal fees from Boehringer Ingelheim, and Roche, outside the submitted work. Dr. EG reports personal fees from Actelion, Janssen, Bayer, MSD, Bial, OrPha Swiss GmbH, OMT and Medscape, outside the submitted work; Dr. SR reports personal fees from Actelion, Bayer, GSK, Pfizer, Novartis, Gilead, MSD, and United Therapeutics, outside the submitted work. The authors have no other conflicts of interest to declare. Publisher Copyright: © Cardiovascular Diagnosis and Therapy. All rights reserved.Background: Pulmonary arterial hypertension (PAH) is common in congenital heart disease (CHD). Because clinical-trial data on PAH associated with CHD (PAH-CHD) remain limited, registry data on the long-term course are essential. This analysis aimed to update information from the COMPERA-CHD registry on management strategies based on real-world data. Methods: The prospective international pulmonary hypertension registry COMPERA has since 2007 enrolled more than 10,000 patients. COMPERA-CHD is a sub-registry for patients with PAH-CHD Results: A total of 769 patients with PAH-CHD from 62 specialized centers in 12 countries were included into COMPERA-CHD from January 2007 through September 2020. At the last follow-up in 09/2020, patients [mean age 45.3±16.8 years; 512 (66%) female] had either post-tricuspid shunts (n=359; 46.7%), pre-tricuspid shunts (n=249; 32.4%), complex CHD (n=132; 17.2%), congenital left heart or aortic valve or aortic disease (n=9; 1.3%), or miscellaneous CHD (n=20; 2.6%). The mean 6-minute walking distance was 369±121 m, and 28.2%, 56.0%, and 3.8% were in WHO functional class I/II, III or IV, respectively (12.0% unknown). Compared with the previously published COMPERA-CHD data, after 21 months of followup, the number of included PAH-CHD patients increased by 91 (13.4%). Within this group the number of Eisenmenger patients rose by 39 (16.3%), the number of “Non-Eisenmenger PAH” patients by 45 (26.9%). Currently, among the 674 patients from the PAH-CHD group with at least one follow-up, 450 (66.8%) received endothelin receptor antagonists (ERA), 416 (61.7%) PDE-5 inhibitors, 85 (12.6%) prostacyclin analogues, and 36 (5.3%) the sGC stimulator riociguat. While at first inclusion in the COMPERA-CHD registry, treatment was predominantly monotherapy (69.3%), this has shifted to favoring combination therapy in the current group (53%). For the first time, the nature, frequency, and treatment of significant comorbidities requiring supportive care and medication are described. Conclusions: Analyzing “real life data” from the international COMPERA-CHD registry, we present a comprehensive overview about current management modalities and treatment concepts in PAH-CHD. There was an trend towards more aggressive treatment strategies and combination therapies. In the future, particular attention must be directed to the “Non-Eisenmenger PAH” group and to patients with complex CHD, including Fontan patients.publishersversionPeer reviewe

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase

Risk stratification and response to therapy in patients with pulmonary arterial hypertension and comorbidities: A COMPERA analysis.

BACKGROUND: A diagnosis of idiopathic pulmonary arterial hypertension (IPAH) is frequently made in elderly patients who present with comorbidities, especially hypertension, coronary heart disease, diabetes mellitus, and obesity. It is unknown to what extent the presence of these comorbidities affects the response to PAH therapies and whether risk stratification predicts outcome in patients with comorbidities. METHODS: We assessed the database of COMPERA, a European pulmonary hypertension registry, to determine changes after initiation of PAH therapy in WHO functional class (FC), 6-minute walking distance (6MWD), brain natriuretic peptide (BNP) or N-terminal fragment of probrain natriuretic peptide (NT-pro-BNP), and mortality risk assessed by a 4-strata model in patients with IPAH and no comorbidities, 1-2 comorbidities and 3-4 comorbidities. RESULTS: The analysis was based on 1,120 IPAH patients (n = 208 [19%] without comorbidities, n = 641 [57%] with 1-2 comorbidities, and n = 271 [24%] with 3-4 comorbidities). Improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk from baseline to first follow-up were significantly larger in patients with no comorbidities than in patients with comorbidities, while they were not significantly different in patients with 1-2 and 3-4 comorbidities. The 4-strata risk tool predicted survival in patients without comorbidities as well as in patients with 1-2 or 3-4 comorbidities. CONCLUSIONS: Our data suggest that patients with IPAH and comorbidities benefit from PAH medication with improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk, albeit to a lesser extent than patients without comorbidities. The 4-strata risk tool predicted outcome in patients with IPAH irrespective of the presence of comorbidities

Risk stratification and response to therapy in patients with pulmonary arterial hypertension and comorbidities: A COMPERA analysis

Background: A diagnosis of idiopathic pulmonary arterial hypertension (IPAH) is frequently made in elderly patients who present with comorbidities, especially hypertension, coronary heart disease, diabetes mellitus, and obesity. It is unknown to what extent the presence of these comorbidities affects the response to PAH therapies and whether risk stratification predicts outcome in patients with comorbidities. Methods: We assessed the database of COMPERA, a European pulmonary hypertension registry, to determine changes after initiation of PAH therapy in WHO functional class (FC), 6-minute walking distance (6MWD), brain natriuretic peptide (BNP) or N-terminal fragment of probrain natriuretic peptide (NT-pro-BNP), and mortality risk assessed by a 4-strata model in patients with IPAH and no comorbidities, 1-2 comorbidities and 3-4 comorbidities. Results: The analysis was based on 1,120 IPAH patients (n = 208 [19%] without comorbidities, n = 641 [57%] with 1-2 comorbidities, and n = 271 [24%] with 3-4 comorbidities). Improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk from baseline to first follow-up were significantly larger in patients with no comorbidities than in patients with comorbidities, while they were not significantly different in patients with 1-2 and 3-4 comorbidities. The 4-strata risk tool predicted survival in patients without comorbidities as well as in patients with 1-2 or 3-4 comorbidities. Conclusions: Our data suggest that patients with IPAH and comorbidities benefit from PAH medication with improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk, albeit to a lesser extent than patients without comorbidities. The 4-strata risk tool predicted outcome in patients with IPAH irrespective of the presence of comorbidities

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care