Background Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms. Methods We retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient’s electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported. Results The Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799). Conclusion The Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance.
Background: It remains unclear whether nutritional support can reduce the mortality and infection rate of patients with traumatic brain injury (TBI), improve their gastrointestinal function, and shorten the length of stay in the intensive care unit (ICU). The purpose of this study is to evaluate the effect of nutritional support on the clinical outcome of TBI patients. Methods: A computer search was conducted of the PubMed, Cochrane Library, Embase, Wanfang, and China National Knowledge Infrastructure (CNKI) databases for randomized controlled trials investigating the impact of nutritional support on the clinical outcomes of patients with TBI. The search included the period from the establishment of the database to July 2021. Two researchers independently screened the literature, extracted the data, and evaluated the risk of bias in the included studies. RevMan 5.3 statistical software (Cochrane Collaboration) was used to analyze the effect size, and a funnel plot was used to detect publication bias.Results: Seven articles reporting on 260 patients receiving nutritional support therapy compared with 252 standard nutrition control patients were included in the study. Meta-analysis showed that there was no significant difference in mortality between the nutritional support and standard nutrition treatments (RR =0.74; 95% CI: 0.34-1.60; P=0.44). However, there were significant differences in total serum protein levels
Objective To observe the clinical effect of Liu-He-Dan on limb pain and swelling in patients with closed traumatic limb fractures in orthopedic perioperative period. Methods A total of 79 patients who received orthopedic surgery were divided into the control group undergoing routine orthopedic nursing and the experimental group undergoing the Infrared lamp and Liu-He-Dan external application. Results Compared with the control group, patients in the Liu-He-Dan group had shorter average time of hospital stay (p < 0.05). The Visual Analogue Scale (VAS) pain scores was much lower (p < 0.05). The levels of cellular inflammatory factors included C-reactive protein and white blood cell count were much lower (p < 0.001). The swelling of the affected limb was eliminated faster (p < 0.01). Conclusion External application of Liu-He-Dan can effectively relieve and eliminate limb pain and swelling after closed traumatic fracture of limbs.
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