Development and validation of deep vein thrombosis diagnostic model based on machine learning methods.

Abstract

The D-Dimer testing has limited diagnostic value for patients with a deep venous thrombosis (DVT) probability based on clinical prediction rules. There are still patients with normal D-Dimer levels (< 500 ng/mL) diagnosed with DVT. Some new predictive marker may improve the predictive power of D-Dimer, especially in DVT patients with normal levels of D-Dimer. All subjects were from Nanyang Central Hospital. The demographic data and laboratory test data were collected. Multiple models were used to evaluate and calculate the importance rank. Multivariate logistics was used to establish a DVT diagnostic model. Compared to D-Dimer and other markers, this combined model has better performance. The von Willebrand factor Gain-of-function mutant GPIb binding assays (VWF: GPIbM) can improve the diagnostic capability of D-Dimer, which has higher diagnostic value and clinical benefits. In addition, the model still has good diagnostic capability in DVT patients with normal D-Dimer levels. The combined model has better diagnostic performance than D-Dimer, and it is valuable for some patients whose clinical prediction rules cannot be evaluated due to difficulties in obtaining medical history information. VWF: GPIbM can be used to assist in the diagnosis of DVT in the future.