Enrichment Benefits of Risk Algorithms for Pulmonary Arterial Hypertension Clinical Trials

Scott, J.V., Garnett, C.E., Kanwar, M.K., Stockbridge, N.L., Benza, R.L. Enrichment Benefits of Risk Algorithms for Pulmonary Arterial Hypertension Clinical Trials Am J Respir Crit Care Med. Sep 16, 2020 Abstract 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 […]

Risk stratification in pulmonary arterial hypertension using Bayesian analysis

Kanwar MK, Gomberg-Maitland M, Hoeper M, Pausch C, Pittow D, Strange G, et al. Risk stratification in pulmonary arterial hypertension using Bayesian analysis. Eur Respir J. Accepted April 21, 2020. Abstract Background Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical […]

ATS: Bayesian Network Modeling: The Future of Pulmonary Arterial Hypertension Risk Stratification Through the PHORA Initiative

Scott JV, Kraisangka J, Kanwar M, Druzdzel M, Antaki J, Vizza D, et al. B97. WHAT’S NEW IN CLINICAL RESEARCH IN PULMONARY HYPERTENSION: LESSONS FROM THE BEST ABSTRACTS: American Thoracic Society; 2020:A4245-A. Introduction: Pulmonary arterial hypertension (PAH) is a fatal and difficult to treat disease due to patient inter-variability. Accurate patient risk stratification is necessary […]

ATS: Pulmonary Arterial Registry Risk Algorithms Improve Upon Clinical Trial Enrichment Methodology

Scott JV, Kanwar M, Garnett C, Stockbridge N, Benza RL. B27. UP-TO-DATE PAH ASSESSMENT AND MANAGEMENT: American Thoracic Society; 2020:A2920-A. Introduction: Pulmonary arterial hypertension (PAH) is a rare disease with poor prognosis. Clinical trials for PAH now use time to morbidity/mortality event (clinical worsening) as the primary endpoint instead of six-minute walk distance (6MWD). These […]


J.V. Scott, L.C. Lohmueller, Kraisangka J, M.K. Kanwar , R.L. Benza. Chest, Volume 156, Issue 4, Supplement, 2019, Pages A1173-A1174, ISSN0012-3692 PURPOSE: Invasive hemodynamics are a valuable component of risk assessment in pulmonary arterial hypertension (PAH). Although several commonly obtained variables are highly associated with survival, the full breath of alternative hemodynamic assessments (HA) and […]

Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison

Kraisangka J., Druzdzel M.J., Lohmueller L.C., Kanwar M.K., Antaki J.F., Benza R.L. (2019) Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison. In: Riaño D., Wilk S., ten Teije A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science, vol 11526. Springer, Cham Abstract Pulmonary arterial hypertension (PAH) is […]

ATS: Upfront Combination Versus Monotherapy in Pulmonary Arterial Hypertension (PAH): Can Advanced Risk Stratification Tell Us the Real Benefit?

J.V. Scott, L.C. Lohmueller , C.G. Garnett , M.K. Kanwar , R.L. Benza. A105 GLORY DAYS: THE LATEST CLINICAL RESEARCH IN PAH / Poster Discussion Session / American Thoracic Society 2019;199:A7346 Rationale: The Registry to Evaluate Early and Long-Term PAH Disease Management recently updated their linear multivariable risk-stratification tool (REVEAL 2.0). Our ongoing study aims […]

The Use of Risk Assessment Tools and Prognostic Scores in Managing Patients with Pulmonary Arterial Hypertension.

Kanwar M, Raina A, Lohmueller L, Kraisangka J, Benza R. Current Hypertension Reports. 2019 Apr 25;21(6):45. doi: 10.1007/s11906-019-0950-y. Review. PMID: 31025123 Abstract PURPOSE OF REVIEW: Pulmonary arterial hypertension (PAH) is a chronic, progressive, and incurable disease with significant morbidity and mortality. Despite increasingly available treatment options, PAH patients continue to experience disease progression and increased […]

ISHLT: Derivation of a Bayesian Network Model from an Existing Risk Score Calculator for Pulmonary Arterial Hypertension

Purpose: We propose an alternative approach to the extensively validated REVEAL risk score calculator using Bayesian network (BN) modeling. We derived a BN model with the same variables and discretization cut points as the REVEAL risk score calculator and data from the REVEAL registry. This study compared the performance and relative impact of the variables […]