Registry of Patients With chrOnic thromboemboLic Pulmonary hypErTension

Data from international CTEPH registries indicate variable utilization rates of available treatment options. Considering the presence of national specificities in the management of CTEPH patients, coupled with the relatively low prevalence of the disease, which poses challenges for data accumulation and research, new registry POLET (Registry of Patients with chrOnic thombomboLic pumonary hypErTension) is established to investigate CTEPH patients in real-world clinical practice. Registry Aim: To investigate the prognosis of patients with chronic thromboembolic pulmonary hypertension based on demographic, anamnestic, clinical characteristics, and treatment regimens. Registry Objectives: To describe the key demographic, anamnestic, and clinical characteristics of CTEPH patients in Russia. To identify factors influencing the frequency of hospitalizations due to clinical deterioration in CTEPH patients. To determine the impact of anamnestic data and treatment regimens on patient prognosis (time to clinical deterioration/death). To determine the impact of demographic and clinical characteristics on patient survival and prognosis (time to clinical deterioration/death). Currently, preliminary retrospective data have been collected on 300 CTEPH patients, each with 1 to 15 hospitalizations at the National Medical Research Center of Cardiology named after Academician E.I. Chazov of the Ministry of Health of Russia from 2012-2024, and who are eligible for inclusion in the registry. 200 patients will be enrolled prospectively. The following data to be extracted from electronic medical records: Medical History Data; Clinical Characteristics and Results of Investigations and Treatments During Each Patient Hospitalization; Operability Assessment Results; PAH-Specific Therapy, Anticoagulant Therapy, Supportive Therapy: Therapy Adjustment, Doses, Presence of Side Effects; Details of Pulmonary endarterectomy; Details of BPA performed; Patient outcomes including survival. Statistical data processing will be performed using RStudio and the R programming language. Missing data will be handled with imputation approach.