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Prevalence of PAH in SSc

Prevalence of PAH in SSc. 1. Mukerjee D, et al. Ann Rheum Dis 2003; 62:1088-93. 2. Hachulla E, et al. Arthritis Rheum 2005; 52:3792-800. 3. Phung S, et al. Intern Med J 2009; 39:682-91, 4. Vonk MC, et al. Ann Rheum Dis 2009 ; 68:961-5. RHC: Right heart catheterization.

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Prevalence of PAH in SSc

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  1. Prevalence of PAH in SSc 1. Mukerjee D, et al. Ann Rheum Dis 2003; 62:1088-93. 2. Hachulla E, et al. Arthritis Rheum 2005; 52:3792-800. 3. Phung S, et al. Intern Med J 2009; 39:682-91, 4. Vonk MC, et al. Ann Rheum Dis 2009; 68:961-5 RHC: Right heart catheterization.

  2. Diagnosis of PAH • PAH is defined as • mPAP ≥ 25 mmHg at rest, assessed by right heart catheterization • Pulmonary capillary wedge pressure ≤ 15 mmHg • PVR > 3 Wood units (240 dyn∙sec/cm5) Galiè N, et al. Eur Heart J 2009; 30:2493-537. McLaughlin VV, et al. Circulation2009; 119:2250-94. mPAP: Mean pulmonary arterial pressure.

  3. Existing screening recommendations 1Galiè et al. Eur Heart J 2009; 2McLaughlin et al. J Am CollCardiol 2009; 3Badesch et al. Chest 2007; 4Badesch et al. J Am CollCardiol 2009. TR, tricuspid regurgitant jet; sPAP, pulmonary artery systolic pressure; PH, pulmonary hypertension

  4. Background • PAH is the leading cause of death in SSc patients1,2 • Screening may lead to earlier diagnosis and intervention and thus to improved outcomes • Current screening is based on consensus rather than robust evidence • No single laboratory test is shown to be adequate 1Steen and Medsger. Ann Rheum Dis 2007; 2Tyndall et al. Ann Rheum Dis 2010. PAH, pulmonary arterial hypertension; SSc, systemic sclerosis

  5. Rationale • TR velocity is main basis of ESC / ERS screening recommendations but • Does not accurately reflect invasive pressures1,2 • Is not present in all patients1 • PAH symptoms as additional criteria are open to interpretation • False positives are common particularly in ILD • No systematic right heart catheterization (RHC) in any screening study to date • Missed diagnoses rate (false negatives) could never be calculated 1Fisher et al. Am J RespirCrit Care Med 2009; 2Parent et al. N Engl J Med 2011. ILD, interstitial lung disease; RHC, right heart catheterization

  6. Objective of DETECT • To develop an evidence-based screening algorithm for PAH in SScpatients • Minimize the number of missed PAH diagnoses • Optimize the use of screening modalities • Optimize the use of diagnostic RHC

  7. Design and methodology • Patient population • Aged ≥ 18 years • SSc of > 3 years’ duration from first non-Raynaud feature • DLCO < 60% of predicted • Prospective cross-sectional study design • RHC performed in all patients following collection of all other data • Demographics, medical history, physical exam, serum lab, pulmonary function tests, ECG, echocardiography DLCO, diffusing capacity of the lung for carbon monoxide; ECG, electrocardiography

  8. Patient disposition SSc patients screenedn = 646 Screen failures (n = 158) No RHC (n = 22) RHC analysis setn = 466 Non-PH: n = 321 (69%) mPAP< 25 mmHg PH:n = 145* (31%) mPAP ≥ 25 mmHg WHO group 1 PH (PAH): n = 87 (19%)PCWP ≤ 15 mmHg WHO group 2 PH (left heart disease): n = 30 (6%)PCWP > 15 mmHg WHO group 3 PH (lung disease / hypoxia): n = 27 (6%)PCWP ≤ 15 mmHg FVC < 60% or 6070% + HRCT not available or ‘moderatesevere’ *PH classification not possible in one patient due to a missing pulmonary capillary wedge pressure (PCWP). mPAP, mean pulmonary arterial pressure; WHO, World Health Organization; FVC, forced vital capacity; HRCT, high resolution computed tomography

  9. Patient demographics and characteristics Data are mean ± standard deviation or %; *n = 243 / 66. 6MWD, 6-minute walk distance; PVR, pulmonary vascular resistance

  10. DETECT: statistical analyses – variable selection 8 variables Demographic and clinical parameters(n = 68) Serum tests(n = 13) ECG parameters(n = 3) Echocardiographic parameters(n = 28) 112 variables

  11. DETECT: The 8 variables in the final screening algorithm Data are mean ± standard deviation or %; *Wald 2 test; #QRS axis ≥ 90; ROC AUC, area under the receiver operating characteristic curve; ACA, anticentromere antibody; NTproBNP, N-terminal pro-brain natriuretic peptide

  12. DETECT: The 8 variables in the final screening algorithm Data are mean ± standard deviation or %; *Wald 2 test; #QRS axis ≥ 90; ROC AUC, area under the receiver operating characteristic curve; ACA, anticentromere antibody; NTproBNP, N-terminal pro-brain natriuretic peptide

  13. DETECT: two-step screening algorithm PAH and non-PH patients(n = 408) Step 1 6 non-echo variables Total risk points > 300? Missing data(n = 52) ROC AUC = 0.844(95% CI, 0.795, 0.898) No referral to echo True PAH negative (n = 50) False PAH negative (n = 2) NO (n = 52) YES (n = 304) Step 2 Total risk points from Step 1 2 echo variables Total risk points > 35? Missing data(n = 37) ROC AUC = 0.881(95% CI, 0.824, 0.923) No referral to RHC True PAH negative (n = 68) False PAH negative (n = 1) NO (n = 69) YES (n = 198) The DETECT algorithm PPV: 69/198 = 35% Missed PAH: 3/72 = 4% RHC referral: 198/319 = 62% RHC True PAH positive (n = 69) False PAH positive (n = 129)

  14. DETECT: comparison of two-step algorithm with current guidelines *Evaluated in 371 DETECT patients with available data for screening variables defined in the guidelines PPV, positive predictive value; NPV, negative predictive value

  15. DETECT: limitations • Results obtained in a high-risk SScpopulation • SSc of > 3 years’ duration • Inclusion criterion DLCO < 60% • More RHC compared to ESC guidelines (62%-40%) • Cross-sectional design • Frequency of screening cannot be recommended

  16. DETECT: conclusions • The two-step evidence-based DETECT algorithm • Is a sensitive, non-invasive screening tool for detection of PAH in SSc patients • Minimizes missed diagnoses • Identifies PAH earlier in a mildly symptomatic population • Addresses resource utilization of RHC • The DETECT algorithm has the potential to revise standards of care in SSc patients

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