Assessing Morbidity and Malignancy Risk in Patients Presenting with Pulmonary Embolism in an Ambulatory Care Setting
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objectives: Venous thromboembolism (VTE), especially pulmonary embolism (PE), can be treated in an outpatient setting after appropriate risk assessment. However, front-door physicians may be resistant to follow this practice. The possibility of cancer-associated VTE complicates the outpatient management of PE. This study aimed to assess the morbidity and mortality in patients with PE who were managed in an emergency/acute medicine-led outpatient clinic as per risk stratification as well as detecting diagnoses of new cancer sites (NCSs) in these patients. Methods: This retrospective study included all patients with confirmed PE managed in an acute medicine outpatient setting at Queens Hospital, Burton-on-Trent, United Kingdom, from 2019 to 2022. Biochemical findings, radiological findings, mortality and morbidity rates, treatment administered, simplified Pulmonary Embolism Severity Index (sPESI) scores, complications and NCS diagnosis were obtained. Results: A total of 82 patients were included in this study; their mean age was 63.3 ± 16.7 years and 56.1% (n = 46) were male. The two commonest presenting complaints were new shortness of breath (25.6%, n = 21) and pleuritic chest pain (19.5%, n = 16). Unprovoked PE was observed in 52.4% (n = 43) of patients. Increased sPESI scores were related to increased mortality rates; sPESI scores of 0 and 3 were associated with 0% and 50% mortality rates within 90 days, respectively. NCS was reported in 13.4% (n = 11). Statistical analyses using logistic regression and classification tree methods revealed that NCS can be predicted with 70.6% sensitivity and 97% specificity using 2 variables (history of cancer and age >73 years). Conclusion: The sPESI can help in the risk stratification of patients with PE showing poor outcomes; patients at a high risk of NCS development can be identified using the classification tree method.
Authors: Muhammed Memon, Eram Anwar, Kamran Shakir, Chafik Bouhaddioui, Mueed Akram, Shakeel Awan, Muhammad Khalil, Muhammad Usama, Adnan Agha
Language: English
Subfield: Internal Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414263275
FWCI:
Cites:
Cited by:
Related to:
