Application of the DETECT Algorithm in a Cohort of Patients With Systemic Sclerosis

Abstract

[Abstract] Background: Pulmonary hypertension (PH) is a severe complication of systemic sclerosis (SSc), with significant prognostic implications. The DETECT algorithm, is a two-step tool developed to facilitate early PH identification in high-risk SSc patients, although its performance in routine clinical practice, especially among patients with relatively preserved diffusing capacity for carbon monoxide (DLCO) remains underexplored.

Objective: To evaluate the clinical performance of the DETECT algorithm in a real-world cohort of SSc patients without a prior diagnosis of PH, and to identify variables associated with PH in this population.

Methods: We conducted a cross-sectional study including SSc patients meeting ACR/EULAR 2013 criteria. Patients with known PH, advanced chronic kidney disease, or severe heart failure were excluded. The DETECT algorithm was applied prospectively. Right heart catheterization (RHC) was performed in patients who met Step 2 criteria. Clinical, laboratory, functional and echocardiographic variables were collected. Logistic regression analyses were conducted to identify factors independently associated with PH.

Results: 85 patients with SSc were included (90.58% women; mean age 67.36±11.75 years; mean disease duration 15.69±9.17 years). 31 patients (36.47%) met criteria for transthoracic echocardiography (TTE), and 21 (24.70%) underwent RHC. PH was confirmed in 11 patients (12.94%). Higher tricuspid regurgitation velocity (TRV) (OR=11.57; 95% CI: 1.29-103.98; p=0.029) was independently associated with PH. Conversely, higher DLCO was inversely associated with PH (OR=0.887; 95% CI: 0.797-0.987; p=0.028). PH was detected even in patients with DLCO>60%.

Conclusion: The DETECT algorithm is a valuable tool for PH screening in SSc patients, with good correlation between its components and confirmed PH. Its applicability may be relevant even in patients with DLCO>60%, broadening its clinical utility. Further research is warranted to validate its performance across diverse populations and to evaluate its long-term prognostic impact.[Resumen] Introducción. La hipertensión pulmonar (HP) es una complicación grave de la esclerosis sistémica (ES), con importantes implicaciones pronósticas. El algoritmo DETECT es una herramienta diseñada para facilitar la detección de HP en pacientes con ES de alto riesgo. Sin embargo, su rendimiento en la práctica habitual, especialmente en pacientes con capacidad de difusión de monóxido de carbono (DLCO) relativamente conservada, sigue sin estar completamente establecido.

Objetivo. Evaluar la utilidad del algoritmo DETECT en una cohorte de pacientes con ES, sin diagnóstico previo de HP, e identificar variables asociadas con su presencia. Métodos. Estudio transversal con pacientes que cumplían los criterios ACR/EULAR 2013. Se excluyeron casos de HP conocida, enfermedad renal crónica avanzada o insuficiencia cardíaca grave. Los pacientes que superaron el umbral del paso 2 fueron derivados a cateterismo cardíaco derecho (CCD). Se recogieron variables clínicas, analíticas, funcionales y ecocardiográficas. Se emplearon análisis de regresión logística para identificar factores asociados con HP.

Resultados. Se incluyeron 85 pacientes (90,58% mujeres; media de edad: 67,36 ± 11,75 años; duración media de la enfermedad: 15,69 ± 9,17 años). Treinta y un pacientes (36,47%) fueron derivados a ecocardiograma y 21 (24,70%) a CCD. Se confirmó HP en 11 pacientes (12,94%). La velocidad de regurgitación tricuspídea se asoció de forma independiente con HP (OR = 11,57; IC 95%: 1,29-103,98; p = 0,029), mientras que un mayor DLCO se asoció inversamente (OR = 0,887; IC 95%: 0,797-0,987; p = 0,028). Se detectó HP incluso en pacientes con DLCO > 60%.

Conclusión. El algoritmo DETECT es útil para el cribado de HP en pacientes con ES. Se necesitan estudios adicionales para validar estos hallazgos y su impacto pronóstico.