Giuseppe Galgano

Background: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, therefore, an optimal management is necessary. The study’s aim was to search for PH phenotypes and develop a predictive model of five-year mortality using machine learning (ML) algorithms. Methods: This multicenter study was conducted on 122 PH patients. Clinical and demographic data were collected and then used to identify phenotypes through clustering. Subsequently, a predictive model was performed by different ML algorithms. Results: Three PH clusters were identified: Cluster 1 (mean age 68.57 ± 10.54) includes 57% females, 69% from non-respiratory PH groups, and better cardiac (NYHA class 2.61 ± 0.84) and respiratory function (FEV1% 78.78 ± 21.54); Cluster 2 includes 50% females, mean age of 71.36 ± 8.32 years, 44% from PH group 3, worse respiratory function (FEV 1% 68.12 ± 10.20); intermediate cardiac function (NYHA class 3.18 ± 0.49) and significantly higher mortality (75%); Cluster 3 represents the youngest cluster (mean age 61.11 ± 13.50) with 65% males, 81% from non-respiratory PH groups, intermediate respiratory function (FEV1% 70.51 ± 17.91) and worse cardiac performance (NYHA class 3.22 ± 0.58). After testing ML models, logistic regression showed the best predictive performance (AUC = 0.835 and accuracy = 0.744) and identified three mortality-risk factors: age, NYHA class, and number of medications taken. Conclusions: The results suggest that the integration of ML into clinical practice can improve risk stratification to optimize treatment strategies and improve outcomes for PH patients.

INTRODUCTION: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, so, an optimal management is necessary. OBJECTIVES: The study aim was to search for PH phenotypes and develop a predictive model of five-year mortality using machine learning (ML) algorithms. METHODS: This multicenter study was conducted on 122 PH patients. Clinical and demographic data were collected and, then, used to identify phenotypes through clustering. Subsequently, predictive model was performed by different ML algorithms. RESULTS: Three PH clusters were identified: Cluster 1 (mean age 68,57±10,54) includes 57% females, 69% from non-respiratory PH groups, and better cardiac (NYHA class 2,61±0,84) and respiratory function (FEV1% 78,78±21,54); Cluster 2 includes 50% females, mean age of 71.36±8.32 years, 44% from PH group 3, worse respiratory function(FEV 1% 68,12±10,20);, intermediate cardiac function (NYHA class 3,18±0,49) and significantly higher mortality (75%); Cluster 3 represents the youngest cluster (mean age 61,11±13,50) with 65% males, 81% from non-respiratory PH groups, intermediate respiratory function (FEV1% 70,51±17,91) and worse cardiac performance (NYHA class 3,22±0,58);. After testing ML models, logistic regression showed the best predictive performance (AUC=0.835 and accuracy=0.744.) and identified three mortality-risk factors: age, NYHA class, number of medications taken. CONCLUSIONS: The results suggest that the integration of ML into clinical practice can improve risk stratification to optimize treatment strategies and improve the outcome of PH patients.