A Predictive Model for Early Detection of Concomitant Pulmonary Embolism in Patients with Deep Vein Thrombosis Immediately Upon Hospital Admission

To develop and validate a predictive model for the early identification of high-risk concomitant pulmonary embolism (PE) in patients with deep vein thrombosis (DVT) upon hospital admission. We retrospectively collected data from a cohort of patients diagnosed with DVT, including baseline demographics, clinical characteristics, laboratory parameters, and imaging-based measurements of iliac vein compression (CIV) to develop a predictive model. The least absolute shrinkage and selection operator (LASSO) regression, widely used in clinical decision-making algorithms for its ability to perform variable selection and regularization simultaneously, was used for variables selection. A multivariate logistic regression was then conducted to construct a nomogram. The model's discriminatory ability was assessed using the area under the curve (AUC). Calibration analysis and decision curve analysis (DCA) were performed. Patients were randomly divided into a development dataset (69.8%, 143 with PE and 130 without PE) and a validation dataset (30.2%, 63 with PE and 55 without PE) for model construction and internal validation. Seven predictors, including female gender, hypertension, cardiovascular disease, fracture, age, D-dimer, and CIV percentage were identified by LASSO regression and incorporated into the nomogram. The model achieved an AUC of 0.727 (95% CI, 0.667-0.787) in the training set, and 0.707 (95% CI, 0.611-0.803) in the testing set. The model was well-calibrated, and DCA demonstrated a net benefit for predicting PE at threshold probabilities ranged between 18% and 80%. A novel predictive model with strong calibration and discriminative power was developed for assessing concomitant PE risk in patients with DVT. This model may facilitate early estimating of PE probability before obtaining definitive CT results and support timely management processes.