Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT study

By Gerry Coghlan, C. Denton, E. Grünig, D. Bonderman, O. Distler, D. Khanna, U. Müller-Ladner, Janet Pope, M. Vonk, M. Doelberg and 5 more. Annals of the Rheumatic Diseases, 2013

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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.

Subfields: Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/def71197f2def10f0960316db5b084af3f825a84

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  6. H. Bogaard
  7. F. Torres
  8. D. Khanna

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