Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT study.
- Coghlan JG Cardiology Department, Royal Free Hospital, London, UK.
- Denton CP Centre for Rheumatology, Royal Free Hospital, London, UK.
- Grünig E Centre for Pulmonary Hypertension, University Hospital, Heidelberg, Germany.
- Bonderman D Medical University of Vienna, Department of Internal Medicine II, Division of Cardiology, Vienna, Austria.
- Distler O Department of Rheumatology, University Hospital Zurich, Zurich, Switzerland.
- Khanna D Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
- Müller-Ladner U Department of Rheumatology and Clinical Immunology, Justus-Liebig-University Giessen, Kerckhoff Clinic Bad Nauheim, Germany.
- Pope JE Department of Medicine, Division of Rheumatology, Western University of Canada, London, Ontario, Canada.
- Vonk MC Department of Rheumatology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
- Doelberg M Global Medical Affairs, Actelion Pharmaceuticals Ltd, Allschwil, Switzerland.
- Chadha-Boreham H Clinical Development, Actelion Pharmaceuticals Ltd, Allschwil, Switzerland.
- Heinzl H Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
- Rosenberg DM Clinical Development, Actelion Pharmaceuticals Ltd, Allschwil, Switzerland.
- McLaughlin VV Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
- Seibold JR Scleroderma Research Consultants LLC, Avon, Connecticut, USA.
- 2013-05-21
Published in:
- Annals of the rheumatic diseases. - 2014
Arterial Hypertension
Epidemiology
Systemic Sclerosis
Adult
Aged
Algorithms
Breath Tests
Cardiac Catheterization
Cross-Sectional Studies
Early Diagnosis
Echocardiography
Evidence-Based Medicine
Familial Primary Pulmonary Hypertension
Female
Humans
Hypertension, Pulmonary
Logistic Models
Male
Middle Aged
Multivariate Analysis
Nomograms
Pulmonary Diffusing Capacity
Pulmonary Wedge Pressure
Risk Assessment
Scleroderma, Systemic
Sensitivity and Specificity
Severity of Illness Index
English
OBJECTIVE
Earlier detection of pulmonary arterial hypertension (PAH), a leading cause of death in systemic sclerosis (SSc), facilitates earlier treatment. The objective of this study was to develop the first evidence-based detection algorithm for PAH in SSc.
METHODS
In this cross-sectional, international study conducted in 62 experienced centres from North America, Europe and Asia, adults with SSc at increased risk of PAH (SSc for >3 years and predicted pulmonary diffusing capacity for carbon monoxide <60%) underwent a broad panel of non-invasive assessments followed by diagnostic right heart catheterisation (RHC). Univariable and multivariable analyses selected the best discriminatory variables for identifying PAH. After assessment for clinical plausibility and feasibility, these were incorporated into a two-step, internally validated detection algorithm. Nomograms for clinical practice use were developed.
RESULTS
Of 466 SSc patients at increased risk of PAH, 87 (19%) had RHC-confirmed PAH. PAH was mild (64% in WHO functional class I/II). Six simple assessments in Step 1 of the algorithm determined referral to echocardiography. In Step 2, the Step 1 prediction score and two echocardiographic variables determined referral to RHC. The DETECT algorithm recommended RHC in 62% of patients (referral rate) and missed 4% of PAH patients (false negatives). By comparison, applying European Society of Cardiology/European Respiratory Society guidelines to these patients, 29% of diagnoses were missed while requiring an RHC referral rate of 40%.
CONCLUSIONS
The novel, evidence-based DETECT algorithm for PAH detection in SSc is a sensitive, non-invasive tool which minimises missed diagnoses, identifies milder disease and addresses resource usage.
Earlier detection of pulmonary arterial hypertension (PAH), a leading cause of death in systemic sclerosis (SSc), facilitates earlier treatment. The objective of this study was to develop the first evidence-based detection algorithm for PAH in SSc.
METHODS
In this cross-sectional, international study conducted in 62 experienced centres from North America, Europe and Asia, adults with SSc at increased risk of PAH (SSc for >3 years and predicted pulmonary diffusing capacity for carbon monoxide <60%) underwent a broad panel of non-invasive assessments followed by diagnostic right heart catheterisation (RHC). Univariable and multivariable analyses selected the best discriminatory variables for identifying PAH. After assessment for clinical plausibility and feasibility, these were incorporated into a two-step, internally validated detection algorithm. Nomograms for clinical practice use were developed.
RESULTS
Of 466 SSc patients at increased risk of PAH, 87 (19%) had RHC-confirmed PAH. PAH was mild (64% in WHO functional class I/II). Six simple assessments in Step 1 of the algorithm determined referral to echocardiography. In Step 2, the Step 1 prediction score and two echocardiographic variables determined referral to RHC. The DETECT algorithm recommended RHC in 62% of patients (referral rate) and missed 4% of PAH patients (false negatives). By comparison, applying European Society of Cardiology/European Respiratory Society guidelines to these patients, 29% of diagnoses were missed while requiring an RHC referral rate of 40%.
CONCLUSIONS
The novel, evidence-based DETECT algorithm for PAH detection in SSc is a sensitive, non-invasive tool which minimises missed diagnoses, identifies milder disease and addresses resource usage.
- Language
-
- English
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1136/annrheumdis-2013-203301
- PMID 23687283
- Persistent URL
- https://folia.unifr.ch/global/documents/236325
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