Risk stratification and response to therapy in patients with pulmonary arterial hypertension and comorbidities: A COMPERA analysis.

BACKGROUND: A diagnosis of idiopathic pulmonary arterial hypertension (IPAH) is frequently made in elderly patients who present with comorbidities, especially hypertension, coronary heart disease, diabetes mellitus, and obesity. It is unknown to what extent the presence of these comorbidities affects the response to PAH therapies and whether risk stratification predicts outcome in patients with comorbidities. METHODS: We assessed the database of COMPERA, a European pulmonary hypertension registry, to determine changes after initiation of PAH therapy in WHO functional class (FC), 6-minute walking distance (6MWD), brain natriuretic peptide (BNP) or N-terminal fragment of probrain natriuretic peptide (NT-pro-BNP), and mortality risk assessed by a 4-strata model in patients with IPAH and no comorbidities, 1-2 comorbidities and 3-4 comorbidities. RESULTS: The analysis was based on 1,120 IPAH patients (n = 208 [19%] without comorbidities, n = 641 [57%] with 1-2 comorbidities, and n = 271 [24%] with 3-4 comorbidities). Improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk from baseline to first follow-up were significantly larger in patients with no comorbidities than in patients with comorbidities, while they were not significantly different in patients with 1-2 and 3-4 comorbidities. The 4-strata risk tool predicted survival in patients without comorbidities as well as in patients with 1-2 or 3-4 comorbidities. CONCLUSIONS: Our data suggest that patients with IPAH and comorbidities benefit from PAH medication with improvements in FC, 6MWD, BNP/NT-pro-BNP, and mortality risk, albeit to a lesser extent than patients without comorbidities. The 4-strata risk tool predicted outcome in patients with IPAH irrespective of the presence of comorbidities

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024