Deep vein thrombosis (DVT) is known as the development of thrombi in deep veins, most frequently the major veins in the legs or pelvis[1], [2]. Within 3 months, patients with untreated proximal DVT have a 50% likelihood of experiencing symptomatic pulmonary embolism (PE), which can be fatal[3]. The presence of thrombus in adjacent branches (either ipsilateral or contralateral), or the identification of pulmonary embolism through angiography, all of which are crucial markers denoting the progression of deep vein thrombosis (DVTp)[4]. Studies have shown that during the first week after trauma, the blood presents a hypercoagulable state, which leads to a higher incidence of DVT events in post-trauma patients, even up to 60%[5], [6], [7]. Moreover, trauma patients with DVT have higher mortality rates than those without. [8]. Trauma contributes significantly to the patients requiring spinal surgery, with the majority resulting from vehicular accidents, falls, and high-impact incidents. DVTp in such patients undergoing spinal surgery is often easily underestimated.

The Caprini risk score (CRS), originally formulated in 1991 and subsequently subjected to revisions in 2013 and 2019[9], [10], holds a ubiquitous stature in the evaluation of DVT. The CRS is widely used to assess the risk of deep vein thrombosis in patients undergoing surgery across various surgical specialties[11], [12], [13], [14]. Some authors have confirmed its effectiveness in reducing DVT incidence[11], [15], [16], [17]. Nonetheless, CRS does bear inherent limitations, notably encompassing an extensive array of variables. In addition, discrepancies might arise due to divergent interpretations of certain variables among clinicians. Furthermore, the inclusion of certain variables that are not routine components of laboratory screenings introduces further complexities[17], [18]. This article focused on assess CRS as a predictor of postoperative DVTp in spinal trauma patients. Using multivariate logistic regression models, nomograms are derived from complex statistical prediction models and represent numerical estimates of likelihood. It is important to note that nomograms provide a visual representation of the results of multivariate logistic regression models, which assists clinicians in better understanding the implications of logistic regression.[19].

Hence, our study had two primary aims: first, to assess the predictive capability of a multivariate logistic regression model based on CPR for postoperative DVTp in spinal trauma patients, and second, to explore the utility of a more efficient prediction model for identifying high-risk patients in clinical practice, accompanied by the enhancement of model interpretability through nomograms.