Pulmonary embolism (PE) is a life-threatening disease with high morbidity and mortality in patients with cancer. To enhance medical treatment and management, this study aimed to create a nomogram to accurately predict PE risk in patients in tumor wards. In this retrospective study, we obtained information on medical history, complications, clinical characteristics, and laboratory biomarkers from patients with suspected PE admitted to the Oncology Department at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2012 and December 2021. A total of 512 patients were randomly divided into training and validation groups at a 6:4 ratio. A nomogram-based scoring model was developed using least absolute shrinkage and selection operator and multivariate logistic regressions, and its performance was evaluated using receiver operating characteristic, calibration, and clinical decision curves. Analysis of over 50 features from the patients led to a model based on 5 predictive features: neutrophil count, sex, systolic blood pressure, surgical status, and D-dimer levels. The model achieved area under the receiver operating characteristic curve values of 0.758 and 0.702 in the training and validation cohorts, respectively. It demonstrated a sensitivity of 85.58%, a specificity of 35.78%, a positive predictive value of 72.44%, and a negative predictive value of 55.71%. The calibration curve showed strong consistency between the predicted and actual probabilities, and decision curve analysis confirmed a favorable net clinical benefit. In conclusion, we successfully developed a novel numerical model that can predict PE risk in oncology patients, enabling the appropriate selection of prevention strategies and helping to reduce unnecessary computed tomography pulmonary angiography scans and their associated adverse effects.
Liuyi et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: