Combination of age-adjusted d-dimer, platelet distribution width and other factors predict preoperative deep venous thrombosis in elderly patients with femoral neck fracture - Yunsong Li; Pengkai Cao; Tianyi Zhu; Yaqi Wang; Fengkai Wang; Liang Li; Xiangdong Liu; Yanrong Zhang (BMC Surgery)
Abstract Purpose This retrospective cohort study aimed to identify factors associated with preoperative deep venous thrombosis (DVT) in elderly patients with femoral neck fractures, and to investigate whether combining these factors could improve the ability to predict DVT. Method Medical records and laboratory test results were reviewed patients presenting with a femoral neck fracture and receiving routine chemoprophylaxis for DVT between January 2020 and December 2023 in a tertiary referral, university-affiliated hospital. Preoperative DVT was confirmed by Doppler ultrasound or CT venography. Demographic, injury, comorbidity, and laboratory variables were analyzed using univariate and multivariate approaches. The performance of combined predictive factors was evaluated using receiver operating characteristic (ROC) curve analysis. Results Among the 499 patients included, 47 (9.4%) were diagnosed with a preoperative DVT. In the univariate analysis, five variables were found to be statistically significant, including alcohol consumption (P = 0.017), history of renal disease (P < 0.001), elevated D-dimer level (both traditional and age-adjusted cut-off used) (P = 0.007 or < 0.003), increased platelet distribution width (PDW) (P < 0.001) and reduced albumin in continuous or categorical variable (P = 0.027, P = 0.002). Multivariate analysis confirmed all except alcohol consumption as independent predictors (all P < 0.05). ROC curve analysis showed that combining these four significant variables with age improved the ability to predict preoperative DVT, with an area under the curve of 0.749 (95% CI: 0.676–0.822, P < 0.001), sensitivity of 0.617, and specificity of 0.757. Conclusion This study identified several factors associated with preoperative DVT, and combining them demonstrated improved performance in predicting DVT, which can facilitate risk assessment, stratification and improved management in clinical practice.