Building and Verifying a Prediction Model for Deep Vein Thrombosis Among Spinal Cord Injury Patients Undergoing Inpatient Rehabilitation.

Abstract

To explore the relevant variables that contribute to deep vein thrombosis (DVT) among spinal cord injury (SCI) patients undergoing inpatient rehabilitation and to build and validate a nomogram model that predicts DVT risk. By convenience sampling, 558 SCI patients who were hospitalized at a tertiary-level Grade A general hospital in Anhui Province, China between January 2017 and March 2022 were chosen as the study subjects. They were split into 2 groups at random, one for training (n = 446) and the other for validation (n = 112). The ratio was 8:2. The clinical information of patients was gathered, including sociodemographic characteristics, data about disease characteristics, and examinations pertaining to laboratories. The related factors of DVT among SCI patients undergoing inpatient rehabilitation were analyzed using both univariate and multivariate logistic regression. Using the variables identified by the multivariate logistic regression analysis, we constructed a predictive nomogram model with the aid of the R software. The model's predictive accuracy for assessing the risk of DVT was validated through the use of receiver operating characteristic curves and calibration plots. Prothrombin time, D-dimer, age, and Caprini score were independent related factors for DVT among SCI patients undergoing inpatient rehabilitation, according to multivariate logistic regression analysis (odds ratio > 1, P < 0.05). These 4 variables selected by the multivariate logistic regression analysis were used to build a nomogram risk model, which was found to have strong predictive capacity for predicting the risk of DVT among SCI patients undergoing inpatient rehabilitation. The nomogram model's area under the receiver operating characteristic curve in the training group and validation group was 0.793 and 0.905, and the 95% confidence intervals were 0.750∼0.837 and 0.830∼0.980, separately, indicating good discrimination of the nomogram model. A good calibration of the model was shown by the calibration curve, which was well consistent between the model's predicted probability and the actual frequency of DVT in both the training and validation groups. Prothrombin time, D-dimer level, age, and Caprini score are independent related factors for DVT among SCI patients undergoing inpatient rehabilitation. According to the variables mentioned previously, a nomogram model was constructed that can accurately and easily predict DVT risk among SCI patients undergoing inpatient rehabilitation. This facilitates the early identification of high-risk groups and the timely implementation of prevention, treatment, rehabilitation, and nursing strategies by clinical medical staff.