Reduced diffusing capacity for carbon monoxide predicts borderline pulmonary arterial pressure in patients with systemic sclerosis.
Keita NinagawaMasaru KatoHiroyuki NakamuraNobuya Abe DrMichihito KonoYuichiro FujiedaKenji OkuShinsuke YasudaHiroshi OhiraIchizo TsujinoTatsuya AtsumiPublished in: Rheumatology international (2019)
Early intervention in pulmonary arterial hypertension associated with systemic sclerosis (SSc) may improve its prognosis. We aimed to establish an algorithm to detect mean pulmonary artery pressure (mPAP) > 20 mmHg using non-invasive examinations in SSc patients by modifying the DETECT algorithm. This study included SSc patients who underwent right heart catheterization (RHC) in our hospital during 2010-2018. Following variables were assessed for performance to predict mPAP ≥ 25 mmHg or > 20 mmHg; anti-centromere or U1-RNP antibody, plasma BNP level, serum urate level, right axis deviation, forced vital capacity (FVC)/diffusing capacity for carbon monoxide (DLCO) ratio, and tricuspid regurgitation velocity. Of 58 patients enrolled in this study, 24 had mPAP of ≥ 25 mmHg and 9 had mPAP of 21-24 mmHg. Among variables tested, only FVC/DLCO elevated similarly in patients with mPAP of ≥ 25 mmHg (median 2.5) and those with mPAP of 21-24 mmHg (median 2.5) compared to those with mPAP of ≤ 20 mmHg (median 1.5). Given the particularly good correlation between DLCO and mPAP of > 20 mmHg, each variable was weighted according to its odds ratio and the total weighted score was calculated. The total weighted score exhibited a good predictive performance for mPAP of > 20 mmHg with its sensitivity of 87.5% and specificity of 92%. Among conventional risk factors for PAH, decreased DLCO may predict mPAP > 20 mmHg with priority in SSc patients. Weighting DLCO may improve the performance of screening algorithm for early SSc-PAH.
Keyphrases
- systemic sclerosis
- end stage renal disease
- pulmonary arterial hypertension
- pulmonary artery
- newly diagnosed
- ejection fraction
- pulmonary hypertension
- prognostic factors
- interstitial lung disease
- chronic kidney disease
- machine learning
- healthcare
- coronary artery
- peritoneal dialysis
- magnetic resonance
- heart failure
- randomized controlled trial
- computed tomography
- coronary artery disease
- deep learning
- aortic valve
- patient reported outcomes
- atrial fibrillation
- mitral valve
- left ventricular
- transcatheter aortic valve replacement