RESUMEN
BACKGROUND: Pulmonary hypertension (PH) is a heterogeneous, severe and progressive disease with an impact on quality of life and life-expectancy despite specific therapies. AIMS: (i) to compare prognosis significance of each PH subgroup in a cohort from a referral center, (ii) to identify phenotypically distinct high-risk PH patient using machine learning. METHODS: Patients with PH were included from 2002 to 2019 and routinely followed-up. We collected clinical, laboratory, imaging and hemodynamic variables. Four-year survival rate of each subgroups was then compared. Next, phenotypic domains were imputed with 5 eigenvectors for missing values and filtered if the Pearson correlation coefficient was>0.6. Thereafter, agglomerative hierarchical clustering was used for grouping phenotypic variables and patients: a heat map was generated and participants were separated using Penalized Model-Based Clustering. P<0.05 was considered significant. RESULTS: 328 patients were prospectively included (mean age 63±18 yo, 46% male). PH secondary to left heart disease (PH-LHD) and lung disease (PH-LD) had a significantly increased mortality compared to pulmonary arterial hypertension (PAH) patients: HR=2.43, 95%CI=(1.24-4.73) and 2.95, 95%CI=(1.43-6.07) respectively. 25 phenotypic domains were pinpointed and 3 phenogroups identified. Phenogroup 3 had a significantly increased mortality (log-rank P=0.046) compared to the others and was remarkable for predominant pulmonary disease in older male, accumulating cardiovascular risk factors, and simultaneous three major comorbidities: coronary artery disease, chronic kidney disease and interstitial lung disease. CONCLUSION: PH-LHD and PH-LD has 2-fold and 3-fold increase in mortality, respectively compared with PAH. PH patients with simultaneous kidney-cardiac-pulmonary comorbidities were identified as having high-risk of mortality. Specific targeted therapy in this phenogroup should be prospectively evaluated.
