Chronic thromboembolism phenotypes after acute pulmonary embolism: identification with the SEARCH algorithm
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Chronic dyspnoea and exercise impairment are common after acute pulmonary embolism (PE) but poorly characterised. We performed a prospective observational study to validate an algorithm to diagnose discrete post-PE outcomes, including chronic thromboembolism phenotypes. Methods: Six pulmonologists independently reviewed records from 150 consecutive patients ≥3 months after acute PE who had undergone a stepwise diagnostic algorithm (SEARCH), which incorporates Symptom screening, cardiopulmonary Exercise testing, pulmonary Arterial perfusion scans, Resting echocardiography, Confirmatory imaging, and Haemodynamic measurements by rest and exercise right heart catheterisation (RHC). Clinical data were reviewed after the first post-PE evaluation and then 6 months later. Results: Symptomatic recovery was most common and occurred in 66.2% of patients during the final evaluation. Dyspnoea without discernible physiological defects occurred in 2.1% and dyspnoea from alternative diagnoses in 19.7%. Chronic thromboembolism phenotypes were diagnosed in 12.0% of patients. Chronic thromboembolism with ventilatory inefficiency and/or small stroke volume augmentation during exercise was seen in 6.3%: 2.1% without pulmonary hypertension on RHC and 4.2% in whom RHC was not performed. Chronic thromboembolic pulmonary hypertension and chronic thromboembolism with exercise-induced pulmonary hypertension occurred in 2.1% and 3.5%, respectively. Krippendorff's α among the readers was 0.984 (95% CI 0.9663–0.9960), signifying very high diagnostic reliability. No patient developed chronic thromboembolism between the first and final evaluations. Conclusions: Dyspnoea from chronic thromboembolism is common after acute PE, most of which is not associated with pulmonary hypertension. The SEARCH protocol reliably identifies chronic thromboembolism phenotypes after acute embolism. Validation of the SEARCH algorithm offers clinicians a straightforward method to diagnose post-PE conditions that are rarely distinguished clinically. Improved categorisation and definition of post-PE conditions will enhance clinical follow-up and allow more precise end-point adjudication in clinical trials of acute PE treatment.
Source: UNC Libraries
DOI: 10.17615/q4ed-r487
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Internal Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Research to Prevent Blindness, Northwestern University, University of Minnesota, Qatar Foundation, University of Alabama at Birmingham, Prince Sattam bin Abdulaziz University, Wellcome Trust, Advanced Accelerator Applications, Genome British Columbia, University of Leicester, European Commission, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, ZonMw, VINNOVA, Svenska Forskningsrådet Formas, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Conselho Nacional de Desenvolvimento Científico e Tecnológico, National Research Foundation of Korea, Natural Science Foundation of Hubei Province, Princess Nourah Bint Abdulrahman University, Stichting Astma Bestrijding, China Scholarship Council, Natural Science Foundation of Jiangsu Province, Natural Science Foundation of Ningxia Province, Russian Science Foundation, Natural Science Foundation of Shandong Province, Binzhou Medical University, Vancouver Coastal Health Research Institute, Innovatiefonds Zorgverzekeraars, Deutsches Zentrum für Lungenforschung, Social Science Foundation of Shaanxi Province, National Science and Technology Major Project, Unicancer, National Institutes of Health, Agency for Healthcare Research and Quality, Kaiser Foundation Research Institute, Roche Diagnostics, Engineering and Physical Sciences Research Council, National Health and Medical Research Council, Japan Society for the Promotion of Science, Instituto de Salud Carlos III, Beijing Nova Program, European Regional Development Fund, National Key Research and Development Program of China, Science and Technology Planning Project of Guangdong Province, National Research, Development and Innovation Office, NIHR Cambridge Biomedical Research Centre, National Institute of Biomedical Imaging and Bioengineering, Division of Graduate Education, European Social Fund, Peabody Museum of Natural History, Yale University, Digital Health CRC, Morrison Institute for Population and Resource Studies, School of Humanities and Sciences, Stanford University, Health Data Research UK, Inari Medical
OpenAlex ID: W7203865756
