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.
Abstract Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients that included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25) and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loading in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue (with lipid deposition being favored extracellularly), an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, is highly correlated with leptin signaling and was correlated with metabolic inflammation. Finally, this study revealed an interesting gene expression pattern where M1 and M2 macrophage markers were correlated with each other, and leptin, in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.
BackgroundAchievement 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 QuestionCan a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH be developed and validated?Study Design and MethodsREVEAL 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).ResultsREVEAL 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.InterpretationREVEAL 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 RegistryClinicalTrials.gov; No.: NCT00370214; URL: www.clinicaltrials.gov; 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. Can a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH be developed and validated? 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). 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. 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. ClinicalTrials.gov; No.: NCT00370214; URL: www.clinicaltrials.gov; Take-home PointsStudy Question: To develop and validate a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH.Results: REVEAL Lite 2, an abridged version of REVEAL 2.0 that uses six rather than 13 variables, approximates REVEAL 2.0 at discriminating low, intermediate, and high risk for 1-year mortality in patients in the REVEAL Registry.Interpretation: REVEAL Lite 2 provides a simplified and robust method of risk assessment for implementation in routine clinical practice.FOR EDITORIAL COMMENT, SEE PAGE 14Despite advances in the treatment of pulmonary arterial hypertension (PAH; World Health Organization [WHO] group 1 pulmonary hypertension), no cure exists for this progressive and ultimately fatal disease. However, with timely and effective clinical intervention, clinical status and survival are improved. The current goal of PAH treatment is to enable patients to achieve a low mortality risk status, which has been associated with improved outcomes.1Galiè N. Humbert M. Vachiery J.L. et al.2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT).Eur Heart J. 2016; 37: 67-119Crossref PubMed Scopus (4388) Google Scholar, 2Benza R.L. Farber H.W. Selej M. Gomberg-Maitland M. Assessing risk in pulmonary arterial hypertension: what we know, what we don't.Eur Respir J. 2017; 50: 1701353Crossref PubMed Scopus (22) Google Scholar, 3Farber H.W. Benza R.L. Risk assessment tools in pulmonary arterial hypertension. Prognosis for prospective trials?.Am J Respir Crit Care Med. 2018; 197: 843-845Crossref PubMed Scopus (12) Google Scholar, 4Weatherald J. Boucly A. Sahay S. Humbert M. Sitbon O. The low-risk profile in pulmonary arterial hypertension. Time for a paradigm shift to goal-oriented clinical trial endpoints?.Am J Respir Crit Care Med. 2018; 197: 860-868Crossref PubMed Scopus (39) Google Scholar, 5Galiè N. Channick R.N. Frantz R.P. et al.Risk stratification and medical therapy of pulmonary arterial hypertension.Eur Respir J. 2019; 53: 1801889Crossref PubMed Scopus (521) Google Scholar To enable such outcomes, assessments of mortality risk should be made at PAH diagnosis and at regular intervals during follow-up. The results of these assessments should be used to guide management, including proactive adjustment of treatment if a low mortality risk status is not achieved.1Galiè N. Humbert M. Vachiery J.L. et al.2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT).Eur Heart J. 2016; 37: 67-119Crossref PubMed Scopus (4388) Google Scholar,5Galiè N. Channick R.N. Frantz R.P. et al.Risk stratification and medical therapy of pulmonary arterial hypertension.Eur Respir J. 2019; 53: 1801889Crossref PubMed Scopus (521) Google Scholar Study Question: To develop and validate a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH. Results: REVEAL Lite 2, an abridged version of REVEAL 2.0 that uses six rather than 13 variables, approximates REVEAL 2.0 at discriminating low, intermediate, and high risk for 1-year mortality in patients in the REVEAL Registry. Interpretation: REVEAL Lite 2 provides a simplified and robust method of risk assessment for implementation in routine clinical practice. FOR EDITORIAL COMMENT, SEE PAGE 14 Current best practice is for risk assessments to be made using multiparameter risk assessment tools, such as the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) risk calculator versions 1.0 or 2.0,6Benza R.L. Gomberg-Maitland M. Miller D.P. et al.The REVEAL Registry risk score calculator in patients newly diagnosed with pulmonary arterial hypertension.Chest. 2012; 141: 354-362Abstract Full Text Full Text PDF PubMed Scopus (396) Google Scholar,7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) method,8Hoeper M.M. Kramer T. Pan Z. et al.Mortality in pulmonary arterial hypertension: prediction by the 2015 European pulmonary hypertension guidelines risk stratification model.Eur Respir J. 2017; 50: 1700740Crossref PubMed Scopus (427) Google Scholar the Swedish PAH Register method,9Kylhammar D. Kjellström B. Hjalmarsson C. et al.A comprehensive risk stratification at early follow-up determines prognosis in pulmonary arterial hypertension.Eur Heart J. 2018; 39: 4175-4181Crossref PubMed Scopus (339) Google Scholar the French Pulmonary Hypertension Registry (FPHR) method,10Boucly A. Weatherald J. Savale L. et al.Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension.Eur Respir J. 2017; 50: 1700889Crossref PubMed Scopus (451) Google Scholar and the Bologna strategy.11Dardi F, Palazzini M, Gotti E, et al. Simplified table for risk stratification in patients with different types of pulmonary arterial hypertension. Poster presented at: European Society of Cardiology Annual Meeting; August 25-29, 2018; Munich, Germany. Poster P4538.Google Scholar REVEAL 1.0 and 2.0 estimate PAH mortality risk by assigning scores using up to 12 or 13 variables, respectively. The scores are used to categorize patients into specific risk strata.6Benza R.L. Gomberg-Maitland M. Miller D.P. et al.The REVEAL Registry risk score calculator in patients newly diagnosed with pulmonary arterial hypertension.Chest. 2012; 141: 354-362Abstract Full Text Full Text PDF PubMed Scopus (396) Google Scholar,7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar REVEAL 2.0 incorporates new variables and expanded thresholds from REVEAL 1.0 to improve risk discrimination. The COMPERA, FPHR, and Bologna methods use data from up to six variables and assign mortality risk based on thresholds published in the European Society of Cardiology/European Respiratory Society (ESC/ERS) pulmonary hypertension guidelines.8Hoeper M.M. Kramer T. Pan Z. et al.Mortality in pulmonary arterial hypertension: prediction by the 2015 European pulmonary hypertension guidelines risk stratification model.Eur Respir J. 2017; 50: 1700740Crossref PubMed Scopus (427) Google Scholar,10Boucly A. Weatherald J. Savale L. et al.Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension.Eur Respir J. 2017; 50: 1700889Crossref PubMed Scopus (451) Google Scholar,11Dardi F, Palazzini M, Gotti E, et al. Simplified table for risk stratification in patients with different types of pulmonary arterial hypertension. Poster presented at: European Society of Cardiology Annual Meeting; August 25-29, 2018; Munich, Germany. Poster P4538.Google Scholar Of clinical importance, REVEAL 2.0, when compared with COMPERA and FPHR, showed greater risk discrimination than either of the two ESC/ERS-based risk assessment strategies.7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar The need for timely and regular risk assessment in PAH is acknowledged widely1Galiè N. Humbert M. Vachiery J.L. et al.2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT).Eur Heart J. 2016; 37: 67-119Crossref PubMed Scopus (4388) Google Scholar,5Galiè N. Channick R.N. Frantz R.P. et al.Risk stratification and medical therapy of pulmonary arterial hypertension.Eur Respir J. 2019; 53: 1801889Crossref PubMed Scopus (521) Google Scholar,10Boucly A. Weatherald J. Savale L. et al.Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension.Eur Respir J. 2017; 50: 1700889Crossref PubMed Scopus (451) Google Scholar,12Benza R.L. Lohmueller L.C. Kraisangka J. Kanwar M. Risk assessment in pulmonary arterial hypertension patients: the long and short of it.Adv Pulm Hyperten. 2018; 16: 125-135Crossref Google Scholar,13Raina A. Humbert M. Risk assessment in pulmonary arterial hypertension.Eur Respir Rev. 2016; 25: 390-398Crossref PubMed Scopus (36) Google Scholar; however, real-world evidence indicates that risk assessment in the clinical setting is suboptimal.14Simons J.E. Mann E.D. Pierozynski A. Assessment of risk of disease progression in pulmonary arterial hypertension: insights from an international survey of clinical practice.Adv Ther. 2019; 36: 2351-2363Crossref PubMed Scopus (13) Google Scholar Several barriers to practical implementation have been documented, including the complexity of tools,15Wilson M, Keeley J, Kingman M, Rogers F. Risk assessment tools in pulmonary arterial hypertension (PAH): a survey of real-world practices and barriers to use. Paper presented at: PAH PHPN Symposium; September 5-7, 2019; Washington, DC. Abstract 1001.Google Scholar the number of parameters that need to be included (with a reported 41% of patients excluded from risk calculation analysis because of insufficient measurements), and a desire to avoid potentially unnecessary invasive procedures.14Simons J.E. Mann E.D. Pierozynski A. Assessment of risk of disease progression in pulmonary arterial hypertension: insights from an international survey of clinical practice.Adv Ther. 2019; 36: 2351-2363Crossref PubMed Scopus (13) Google Scholar To expedite risk assessment in the clinic, where comprehensive data for all patients may be lacking and time constrained, risk assessment tools using fewer variables may be preferable. To this end, we developed two simplified risk calculators, REVEAL Lite 1 and REVEAL Lite 2. Both are based on the recently developed and validated REVEAL 2.0 risk calculator,7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar,16Anderson J.J. Lau E.M. Lavender M. et al.Retrospective validation of the REVEAL 2.0 risk score with the Australian and New Zealand Pulmonary Hypertension Registry Cohort.Chest. 2020; 157: 162-172Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar,17Kanwar M.K. Gomberg-Maitland M. Hoeper M. et al.Risk stratification in pulmonary arterial hypertension using Bayesian analysis.Eur Respir J. 2020; 56: 2000008Crossref Scopus (27) Google Scholar but in an abridged format. REVEAL Lite 1 uses only nine noninvasive variables, whereas REVEAL Lite 2 uses only six modifiable and noninvasive variables.18Benza RL, Kanwar M, Raina A, et al. Comparison of risk discrimination between the REVEAL 2.0 calculators, the French Pulmonary Registry algorithm, and the Bologna Method in patients with pulmonary arterial hypertension. Poster presented at: American Thoracic Society International Conference; May 17-22, 2019; Dallas, TX. Poster 11945.Google Scholar Herein, we present results from analyses conducted during development and internal validation of REVEAL Lite 2. REVEAL Lite 2 was developed using data from patients enrolled in the REVEAL (final database lock, February 4, 2013). The same patient population used for REVEAL 2.0 development was used for the current REVEAL Lite 2 analysis. Patients enrolled in REVEAL were eligible if they were 18 years of age or older at diagnosis, met hemodynamic criteria for PAH (ie, pulmonary capillary wedge pressure ≤ 15 mm Hg), and had ≥ 12 months of follow-up data available. This enabled the capture of all-cause hospitalization data from the previous 6 months for development of the REVEAL 2.0 tool. One year after enrollment was considered baseline for these analyses. Patients were excluded from the analyses if they were participating in a blinded clinical trial at enrollment or if they received a lung transplant within 1 year of enrollment (e-Appendix 1; e-Fig 1). The relevant parameters and variables and associated scoring included in the REVEAL 2.0 and REVEAL Lite 2 risk calculators are presented in Table 1. REVEAL Lite 2 is based on REVEAL 2.0, but includes only six noninvasive and modifiable parameters: New York Heart Association (NYHA) or WHO 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), renal insufficiency (if estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m2 or reported as "renal insufficiency," as assessed by the principal investigator when eGFR was unavailable). For both REVEAL 2.0 and REVEAL Lite 2, patients were grouped into three risk categories according to ESC/ERS guidelines.1Galiè N. Humbert M. Vachiery J.L. et al.2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT).Eur Heart J. 2016; 37: 67-119Crossref PubMed Scopus (4388) Google Scholar,7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar For the REVEAL 2.0 assessment, and based on the 1-year mortality outcomes in the REVEAL derivation data (with scores ranging from 0 to 23), a score between 0 and 6 was considered low risk, a score of 7 or 8 was considered intermediate risk, and a score of 9 or higher was considered high risk.7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar For the REVEAL Lite 2 assessment (with scores ranging from 1 to 14), a score between 1 and 5 was considered low risk, a score of 6 or 7 was considered intermediate risk, and a score of 8 or higher was considered high risk. Risk was calculated for both risk calculators using data from a subpopulation of the REVEAL who had survived ≥ 1 year after enrollment. To provide proper reference for REVEAL Lite 2 to REVEAL 2.0, the same dataset used for REVEAL 2.0 was also used for REVEAL Lite 2. REVEAL Lite 2 scores at time of enrollment were recalculated for patients included in this analysis. A correction factor of 6 was used for REVEAL Lite 2 calculations.Table 1Variables Included in the REVEAL 2.0 and REVEAL Lite 2 Risk Calculators and Associated Risk ScoresParameterREVEAL 2.0 (13 Variables)REVEAL Lite 2 (6 Variables)CauseConnective tissue disease: +1Portopulmonary hypertension: +3Heritable: +2—DemographicsMen > 60 y: +2—Renal insufficiencyeGFR < 60 mL/min/1.73 m2 or defined by clinical judgment if eGFR is not available: +1NYHA or WHO FCFC I: −1FC III: +1FC IV: +2All-cause hospitalization within the previous 6 mo+1—Vital signsSBP < 110 mm Hg: +1HR > 96 bpm: +16MWD≥ 440 min: −2320-< 440 min: −1< 165 min: +1BNP/NT-proBNPBNP < 50 pg/mL OR NT-proBNP < 300 pg/mL: −2BNP 200-< 800 pg/mL: +1BNP ≥800 pg/mL OR NT-proBNP ≥1100 pg/mL: +2EchocardiogramPericardial effusion: +1—Pulmonary function test% predicted Dlco < 40%: +1—RHC within 1 ymRAP > 20 mm Hg: +1PVR < 5 Wood units: −1—Total scoreSum of above scores +6Sum of above scores +6Em dashes denote parameter not included in REVEAL Lite 2.6MWD = 6-min walk distance; BNP = brain natriuretic peptide; bpm = beats per minute; Dlco = diffusing capacity of the lungs for carbon monoxide; eGFR = estimated glomerular filtration rate; FC = functional class; HR = heart rate; mRAP = mean right atrial pressure; NT-proBNP = N-terminal prohormone of brain natriuretic peptide; NYHA = New York Heart Association; PAH = pulmonary arterial hypertension; PVR = pulmonary vascular resistance; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management; RHC = right heart catheterization; SBP = systolic BP; WHO = World Health Organization. Open table in a new tab Em dashes denote parameter not included in REVEAL Lite 2. 6MWD = 6-min walk distance; BNP = brain natriuretic peptide; bpm = beats per minute; Dlco = diffusing capacity of the lungs for carbon monoxide; eGFR = estimated glomerular filtration rate; FC = functional class; HR = heart rate; mRAP = mean right atrial pressure; NT-proBNP = N-terminal prohormone of brain natriuretic peptide; NYHA = New York Heart Association; PAH = pulmonary arterial hypertension; PVR = pulmonary vascular resistance; REVEAL = Registry to Evaluate Early and Long-Term PAH Disease Management; RHC = right heart catheterization; SBP = systolic BP; WHO = World Health Organization. REVEAL Lite 2 is based on earlier versions of the REVEAL risk assessment tools 1.0 and 2.0. Detailed descriptions of the statistical methods used in their development have been described previously for REVEAL 1.06 and REVEAL 2.0.7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar Patient data, definitions, and algorithm of derivations from the development of REVEAL 2.0 were used in the current analysis, in which baseline risk was calculated based on the last available assessment at 12 months' follow-up or an earlier time point, starting from enrollment. A score of zero was assigned for missing individual assessments. The six noninvasive and modifiable parameters were classified onto categorical values according to the REVEAL 2.0 risk calculator: NYHA FC (−2, 0, 1, 2), SBP (0, 1), heart rate (0, 1), 6MWD (−2, −1, 0, 1), BNP/NT-proBNP (−2, 0, 1, 2), renal insufficiency (0, 1). The Cox proportional hazard model with the six parameters as independent variables and survival time as the dependent variable was used to derive the prognostic equation, in which stepwise selection was used to rank the impact of these prognostic parameters. The Cox proportional hazard model was used to compare the survival rate between risk groups, Harrell's concordance statistic (c-index) was used as a goodness-of-fit measure, and the associated 95% CIs were used to evaluate the discrimination of the risk assessment tools. The Kaplan-Meier method was used to estimate 1-year survival from baseline for each of the risk score groups for both risk calculators (REVEAL 2.0 and REVEAL Lite 2). Simple κ values were calculated to examine the agreement between REVEAL 2.0 and REVEAL Lite 2 on risk group classifications. C-indexes were used to evaluate the impact of missing factors when one or more individual factors were missing from the model on the discrimination of REVEAL Lite 2. All analyses were conducted using SAS version 9.4 software (SAS Institute). In total, 2,529 of the 3,515 patients enrolled in REVEAL Registry were eligible for inclusion in our analyses (e-Fig 1). Proportions of patients with available data for each variable and handling of missing data were reported previously.7Benza R.L. Gomberg-Maitland M. Elliott C.G. et al.Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies.Chest. 2019; 156: 323-337Abstract Full Text Full Text PDF PubMed Scopus (342) Google Scholar Patient demographics and clinical characteristics for these patients at 1 year after enrollment are presented in Table 2. Approximately 50% of patients had idiopathic PAH (IPAH) and 25% had connective tissue-associated PAH (CTD-PAH). Most patients (approximately 87%) were classified as NYHA FC II/III.Table 2Patient Demographics and Clinical Characteristics at 1 Year After EnrollmentCharacteristicPatients With 1 y of Follow-up (N = 2,529)Age, mean (SD), y53.6 (14.3)Sex, No. (%)… Male505 (20.0) Female2,024 (80.0)Race, No. (%)… White1,809 (71.5) Black330 (13.0) Hispanic228 (9.0) Asian or Pacific Islander85 (3.4) Native American or Native Alaskan16 (0.6) Other22 (0.9) Unknown39 (1.5)WHO group I PAH subgroup, No. (%)… Idiopathic1,171 (46.3) HeritableaSome, but not all, had confirmed BMPR2 or ALK1 mutations.74 (2.9) Other18 (0.7)PAH associated with… Connective tissue disease649 (25.7) Congenital heart disease244 (9.6) Portal hypertension139 (5.5) HIV48 (1.9) Other186 (7.4)Modified NYHA or WHO FC, No. (%)bData were missing for 99 patients.… I203 (8.4) II1,003 (41.3) III1,116 (45.9) IV108 (4.4)FC = functional class; NYHA = New York Heart Association; PAH = pulmonary arterial hypertension; WHO = World Health Organization.a Some, but not all, had confirmed BMPR2 or ALK1 mutations.b Data were missing for 99 patients. Open table in a new tab FC = functional class; NYHA = New York Heart Association; PAH = pulmonary arterial hypertension; WHO = World Health Organization. Kaplan-Meier survival curves to 5 years by REVEAL 2.0 and REVEAL Lite 2 are shown in Figure 1A and 1B, respectively. Both demonstrate clear separation of risk between each risk stratum. The results of the Kaplan-Meier, hazard ratio, and c-index calculations for 1-year survival are presented in Table 3. These data show that REVEAL Lite 2 approximates the "parent" REVEAL 2.0 risk calculator at discriminating among patients at low, intermediate, or high risk for 1-year mortality (based on c-index). This was the case regardless of whether the data were compared using categorical or numerical values. The c-indexes using categorical values were 0.73 (95% CI, 0.71-0.75) and 0.70 (95% CI, 0.68-0.72) for REVEAL 2.0 and REVEAL Lite 2, respectively. The c-indexes, using the original numerical values, were 0.76 (95% CI, 0.74-0.78) and 0.73 (95% CI, 0.71-0.75) for REVEAL 2.0 and REVEAL Lite 2, respectively. Because REVEAL 2.0 was developed based on data at 12 months of follow-up (as baseline), we also examined whether REVEAL Lite 2 provides consistent discrimination at the time of enrollment. When we applied REVEAL Lite 2 to value at enrollment (N = 3,046 PAH patients), the c-index was 0.71 (95% CI, 0.69-0.73), indicating good discrimination. We calculated the c-index for IPAH (n = 1,171) and CTD-PAH (n = 649) subgroups separately using REVEAL Lite 2. C-indexes, using the original numerical values, were 0.74 (95% CI, 0.71-0.77) and 0.76 (95% CI, 0.73-0.79) and, using categorical values, were 0.71 (95% CI, 0.68-0.74) and 0.72 (95% CI, 0.69-0.75) for IPAH and CTD-PAH, respectively.Table 3Hazard Ratios and Concordance Indexes for Estimation of 1-Year MortalityRisk Assessment Strategy and Risk GroupNo. of Patients (%)Kaplan-Meier Estimated Mortality at 1 y, % (95% CI)HR (95% CI) Compared With Low-Risk GroupC-Index (95% CI), Three-Category/OriginalREVEAL 2.0 (N = 2,529) Low (score, ≤ 6)1,073 (42.4)1.9 (1.1-2.7)NA0.73 (0.71-0.75)/0.76 (0.74-0.78) Intermediate (score, 7-8)692 (27.4)6.5 (4.7-8.4)2.73 (2.2-3.4) High (score, ≥ 9)764 (30.2)25.8 (22.7-28.9)8.09 (6.6-9.9)REVEAL Lite 2 (N = 2,529) Low (score, ≤ 5)960 (38.0)2.9 (1.8-3.9)NA0.70 (0.68-0.72)/0.73 (0.71-0.75) Intermediate (score, 6-7)883 (34.9)7.1 (5.4-8.8)2.27 (1.8-2.8) High (score, ≥ 8)686 (27.1)25.1 (21.9-28.4)6.35 (5.2-7.8)c-index = Harrell's concordance statistic; HR, hazard ratio; NA = not applicable. See Table 1 legend for expansion of other abbreviation. Open table in a new tab c-index = Harrell's concordance statistic; HR, hazard ratio; NA = not applicable. See Table 1 legend for expansion of other abbreviation. Cox proportional hazard multivariate analysis showed that all variables and scores were independent prognosticators of survival and that higher risk score was associated with higher risk of death (e-Table 1). Predicted 1-year survival was computed as follows: S0(1)exp(Z′βγ), where S0(1) is the baseline survivor function (0.925), Z′β is the linear component, and γ is the shrinkage coefficient (0.976). The core of the prognostic equation is Z, the linear component of the Cox model (e-Table 2). All parameters were found to be highly predictive (based on χ 2 value for individual variables), with the exceptions of heart rate and NYHA or WHO FC I (e-Table 1). However, it is important to note that only 203 patients (8.4%) in the analysis sample had NYHA or WHO FC I disease. The model indicated that the most hig
Abstract 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.
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.
Natalizumab, a monoclonal α–4–integren receptor antibody, is an effective immunomodulator in highly active relapsing remitting Multiple Sclerosis (HARRMS). Natalizumab has been associated with PML (progressive multifocal leucoencephaopathy), due to opportunistic reactivation of JC polyomavirus (JCv). PML risk may influence decisions to continue therapy. Risk relates to 3 identified risk factors: serum anti–JCv antibodies, prior immunosuppression and prolonged natalizumab therapy (>2 years). Recent commercial availability of a serum JCv–antibody screening test (STRATIFY JCV TM) has been incorporated into a risk–stratification algorithm, presented to patients to help guide treatment. Influence of antibody testing on risk perception and decision to proceed with treatment has not been widely established.
Aim
To examine treatment decisions of patients receiving natalizumab based on their JCv–antibody status.
Patients and methods
Serum JCv–antibodies tested annually in patients receiving natalizumab for HARRMS. Clinical data and decisions to stop natalizumab based on JCv status recorded.
Results
JCv antibody status was available in 112 natalizumab patients. Mean natalizumab duration 27.4 months (2–72). Antibodies detected in 55 (49.1%): 2 (3.6%) stopped due to JCv+ve alone, 14 (25.5%) due to prolonged therapy (>2years), 1 (1.8%) due to prolonged therapy and past immuosuppression, 3 (5.5%) disability progression, 1 (1.8%) to conceive. (12.3% of JCv negative patients stopped due to prolonged therapy). No significant difference in mean natalizumab duration or disease–modifying therapy history between JCv Ab+ve and Ab–ve groups (p>0.05). Other PML risks (>2 years, past immunosuppression) did not differ significantly between JCv+ve patients who opted to continue compared to those choosing to stop (p>0.05).
Discussion
JCv antibody status had little influence on this cohort9s decision to discontinue or remain on natalizumab therapy. It is important that patients understand therapeutic benefits and potential risks of alternative treatments before discontinuing due to JCv status alone. Further validation of this risk stratification measure is important.