A comparison of the predictive accuracy of three screening models for pulmonary arterial hypertension in systemic sclerosis - PubMed (original) (raw)
Comparative Study
doi: 10.1186/s13075-015-0517-5.
Vivek Thakkar 3 4 5, Wendy Stevens 6, Kathleen Morrisroe 7, David Prior 8, Candice Rabusa 9, Peter Youssef 10, Eli Gabbay 11, Janet Roddy 12, Jennifer Walker 13, Jane Zochling 14, Joanne Sahhar 15, Peter Nash 16, Susan Lester 17, Maureen Rischmueller 18 19, Susanna M Proudman 20 21, Mandana Nikpour 22 23
Affiliations
- PMID: 25596924
- PMCID: PMC4332896
- DOI: 10.1186/s13075-015-0517-5
Comparative Study
A comparison of the predictive accuracy of three screening models for pulmonary arterial hypertension in systemic sclerosis
Yanjie Hao et al. Arthritis Res Ther. 2015.
Abstract
Introduction: There is evidence that early screening for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) improves outcomes. We compared the predictive accuracy of two recently published screening algorithms (DETECT 2013 and Australian Scleroderma Interest Group (ASIG) 2012) for SSc-associated PAH (SSc-PAH) with the commonly used European Society of Cardiology/European Respiratory Society (ESC/ERS 2009) guidelines.
Methods: We included 73 consecutive SSc patients with suspected PAH undergoing right heart catheterization (RHC). The three screening models were applied to each patient. For each model, contingency table analysis was used to determine sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values for PAH. These properties were also evaluated in an 'alternate scenario analysis' in which the prevalence of PAH was set at 10%.
Results: RHC revealed PAH in 27 (36.9%) patients. DETECT and ASIG algorithms performed equally in predicting PAH with sensitivity and NPV of 100%. The ESC/ERS guidelines had sensitivity of 96.3% and NPV of only 91%, missing one case of PAH; these guidelines could not be applied to three patients who had absent tricuspid regurgitant (TR) jet. The ASIG algorithm had the highest specificity (54.5%). With PAH prevalence set at 10%, the NPV of the models was unchanged, but the PPV dropped to less than 20%.
Conclusions: In this cohort, the DETECT and ASIG algorithms out-perform the ESC/ERS guidelines, detecting all patients with PAH. The ESC/ERS guidelines have limitations in the absence of a TR jet. Ultimately, the choice of SSc-PAH screening algorithm will also depend on cost and ease of application.
Figures
Figure 1
Summary of screening algorithms. Ab, antibody; ASIG, Australian Scleroderma Interest Group; DLCO, diffusing capacity for carbon monoxide; ESC/ERS, European Society of Cardiology/European Respiratory Society; FVC, forced vital capacity (percentage predicted); HRCT, high-resolution computed tomography (chest); PAH, pulmonary arterial hypertension; PH, pulmonary hypertension; RA, right atrium; RHC, right heart catheterization; SSc, systemic sclerosis; TR, tricuspid regurgitation; TRV, tricuspid regurgitant velocity; TTE, transthoracic echocardiography; WHO, World Health Organization.
Figure 2
Study cohort. *World Health Organization (WHO) group 1 pulmonary hypertension (PH) means pulmonary arterial hypertension (PAH). **WHO group 2 PH means left heart disease-associated PH. This group was excluded for validating the performance of all models for PAH but included in validating the performance for PH. ***WHO group 3 PH means lung disease/hypoxia-associated PH. This group was excluded for validating the performance of all models for PAH but included in validating the performance for precapillary PH and PH. ASIG, Australian Scleroderma Interest Group; ESC/ERS, European Society of Cardiology/European Respiratory Society; FVC, forced vital capacity (percentage predicted); RHC, right heart catheterization; SSc, systemic sclerosis; TRV, tricuspid regurgitant velocity.
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