A Prediction Model Based on Blood Biomarker for Mortality Risk in Patients with Acute Venous Thromboembolism

BACKGROUND: Most studies to date have focused on predicting the risk of venous thromboembolism (VTE), but prediction models about mortality risk in VTE are rarely reported. We sought to develop and validate a multivariable model to predict the all-cause mortality risk in patients with acute VTE in emergency settings. METHODS: A total of 700 patients were included from Qilu Hospital of Shandong University and were randomly assigned into training set (n=490) and validation set (n=210) in an 7:3 ratio. Multivariate logistics regression analysis was performed to identify independent variables and develop a prediction model, which was validated internally using bootstrap method. The discrimination, calibration and clinical utility were evaluated by receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow (HL) test, Kaplan-meier (KM) analysis and decision curve analysis (DCA). RESULTS: There were 52 patients (10.6%) dying and 437 (89.4%) surviving in training set. Age (odds ratio [OR]: 4.158, 95% confidence interval [CI]: 2.426–7.127), pulmonary embolism (OR: 1.779, 95% CI: 1.124–2.814), platelet count (OR: 0.507, 95% CI: 0.310–0.830), D-dimer (OR: 1.826, 95% CI: 1.133–2.942) and platelet/lymphocyte ratio (OR: 2.166, 95% CI: 1.259–3.727) were independent risk variables associated with all-cause mortality. The model had good predictive capability with an AUC of 0.746 (95% CI: 0.668,0.825), a sensitivity of 0.769 (95% CI: 0.607,0.889), a specificity of 0.672 (95% CI: 0.634,0.707). The validation model had an AUC of 0.739 (95% CI: 0.685,0.793), a sensitivity of 0.690 (95% CI: 0.580,0.787), a specificity of 0.693 (95% CI: 0.655,0.729). The model is well calibrated and the HL test showed a good fit (χ(2)=5.291, p=0.726, Nagelkerke R(2)=0.137). KM analysis and DCA showed a good clinical utility of the nomogram. CONCLUSION: This study identified independent variables affecting all-cause mortality in patients with acute VTE, and developed a prediction model and provided a nomogram with good prediction capability and clinical utility.