Development and validation of a predictive nomogram for lower extremity deep vein thrombosis dislodgement in orthopedic patients.

Objective: To analyze the risk factors of lower extremity deep venous thrombosis (DVT) detachment in orthopedic patients, and to establish a risk nomogram prediction model. Methods: The clinical data of 334 patients with orthopedic DVT admitted to the Third Hospital of Hebei Medical University from January 2020 to July 2021 were retrospectively analyzed. General statistics included gender, age, BMI, thrombus detachment, inferior vena cava filter window type, filter implantation time, medical history, trauma history, operation, use of tourniquet, thrombectomy, anesthesia mode, anesthesia grade, operative position, blood loss during operation, blood transfusion, immobilization, use of anticoagulants, thrombus side, thrombus range, D-dimer content before filter implantation and during removal of inferior vena cava filter. Logistic regression was used to perform univariate and multivariate analysis on the possible factors of thrombosis detachment, screen out independent risk factors, establish a risk nomogram prediction model by variables, and internally verify the predictability and accuracy of the model. Results: Binary logistic regression analysis showed that Short time window filter (OR = 5.401, 95% CI = 2.338-12.478), lower extremity operation (OR = 3.565, 95% CI = 1.553-8.184), use of tourniquet (OR = 3.871, 95% CI = 1.733-8.651), non-strict immobilization (OR = 3.207, 95% CI = 1.387-7.413), non-standardized anticoagulation (OR = 4.406, 95% CI = 1.868-10.390), distal deep vein thrombosis (OR = 2.212, 95% CI = 1.047-4.671) were independent risk factors for lower extremity DVT detachment in orthopedic patients (P < 0.05). Based on these six factors, a prediction model for the risk of lower extremity DVT detachment in orthopedic patients was established, and the risk prediction ability of the model was verified. The C-index of the nomogram model was 0.870 (95% CI: 0.822-0.919). The results indicate that the risk nomogram model has good accuracy in predicting the loss of deep venous thrombosis in orthopedic patients. Conclusion: The nomogram risk prediction model based on six clinical factors, including filter window type, operation condition, tourniquet use, braking condition, anticoagulation condition, and thrombosis range, has good predictive performance.

Facile Fabrication of a Continuous ZIF-67 Membrane for Efficient Azeotropic Organic Solvent Mixture Separation.

Azeotropic organic solvent mixture separation is common in the chemical industry but extremely difficult. Zeolitic imidazolate framework-67 (ZIF-67) shows great potential in organic solvent mixture separation due to its rigid micropores and excellent stability. However, due to the fast nucleation rate, it is a great challenge to prepare continuous ZIF-67 membrane layers with ultrathin thickness. In this study, a hydroxy salt layer with high inducible activity was synthesized as a precursor on different porous substrates to prepare ZIF-67 membranes at room temperature. The precursor layer enables an intact ZIF-67 membrane with an ultrathin thickness of 176±12 nm. The experimental and simulation results confirmed that the size sieving through the pore windows and the preferential adsorption of polar solvent molecules provide the ZIF-67 membrane an unprecedented separation performance such as high separation factors and fluxes, for four types of azeotropic organic solvent mixtures.

Constructing rapid water vapor transport channels within mixed matrix membranes based on two-dimensional mesoporous nanosheets.

Membrane technology is an effective strategy for gas dehumidification and fuel cell humidification. In this study, cerium fluoride oxide (F-Ce) two-dimensional (2D) mesoporous nanosheets and their composite with 1-ethyl-3-methylimidazolium dicyanamide ([Emim][DCA]) ionic liquids (ILs) (IL@F-Ce) are introduced as fillers into polyether block amide (PEBAX® 1074) to fabricate mixed matrix membranes (MMMs). The slit-shaped mesoporous structure of the nanosheets facilitates the construction of water vapor rapid transport channels in MMMs. The permeability and selectivity of water vapor for MMMs loaded with F-Ce nanosheets are greatly improved, and the performance of MMMs loaded with IL@F-Ce nanosheets are much better than the former. Particularly, the MMM with IL@F-Ce content of 4 wt.% achieves the highest H2O permeability of 4.53 × 105 Barrer, which is more than twice that of the pure PEBAX membrane, and the selectivity is increased by 83%. Thus, the MMMs based on 2D mesoporous nanosheets have considerable potential application in industrial-scale dehydration and humidification processes.