Clinical Probability Tools for Deep Venous Thrombosis, Pulmonary Embolism, and Bleeding

Metabolomic profiling of deep vein thrombosis

Deep vein thrombosis (DVT) of the lower extremities is one of the most common peripheral vascular diseases, with significant complications and sequelae. Metabolomics aims to identify small molecules in biological samples. It can serve as a promising method for screening compounds that can be used for early disease detection, diagnosis, treatment response prediction, and prognosis. In addition, high-throughput metabolomics screening can yield significant insights into the pathophysiological pathways of DVT. Currently, the metabolomic profiles of DVT have yielded inconsistent expression patterns. This article examines the recent advancements in metabolomic studies of DVT and analyzes the factors that may influence the results.

Analysis of Related Influencing Factors of Deep Vein Thrombosis after Lumbar Internal Fixation and Treatment Strategy

Lumbar internal fixation is a traditional surgical method for the treatment of degenerative diseases of the lumbar spine. However, due to its large surgical trauma, it easily causes complications such as deep venous thrombosis (DVT) after the operation. DVT refers to the abnormal coagulation of blood in deep veins, blocking the lumen, causing venous blood return disorder, causing venous blood return disorder to cause swelling and pain, which affects the recovery of the patient's lumbar spine function. In severe cases, even complicated pulmonary embolism endangers the life and health of the patient. Therefore, it is extremely important to explore the related influencing factors and effective treatment of DVT. The purpose of this study was to investigate the influencing factors and effective treatment of DVT after lumbar internal fixation. Univariate analysis and multivariate a logistic regression model were used to analyze the related factors affecting DVT after lumbar internal fixation. Conventional treatments such as anticoagulation, promotion of venous blood return, and improvement of limb circulation were given to patients with DVT, and functional exercise was guided to compare the hypercoagulability and hyperviscosity of blood in patients with DVT before and after treatment. The results showed that the incidence of DVT after lumbar internal fixation was related to age, BMI, and bed time. Getting out of bed for functional exercise in time after surgery can effectively prevent the formation of DVT. Preoperative grading examination, intraoperative intervention, postoperative physical exercise, and other preventive guidance can be carried out according to different individuals during the perioperative period to prevent the formation of postoperative DVT.

An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.