This retrospective study aimed to investigate the preoperative incidence and locations of deep venous thrombosis (DVT) in patients undergoing surgeries for ankle fractures and identify the associated risk factors. From January 2016 to June 2019, 1,532 patients undergoing surgery of ankle fractures were included. Their inpatient medical records were inquired for data collection, including demographics, comorbidities, injury-related data and preoperative laboratory biomarkers. DVT of bilateral lower extremities was diagnosed by routine preoperative Doppler examination. Univariate analyses and multivariate logistic regression analyses were used to identify the independent risk factors. Totally, 98 patients had a preoperative DVT, indicating an incidence rate of 6.4%. A total of 164 clots for 6 veins were found, representing an average of 1.7 for each patient. The detailed DVTs involving veins were as follows: 2 in femoral common vein, 7 in superficial femoral vein, 2 in deep femoral vein, 16 in popliteal vein, 49 in posterior tibial vein, and 88 in peroneal vein. In the multivariate model, 5 risk factors were identified to be associated with DVT, including age (10-year increase), gender, lower ALB level, reduced LYM count and elevated D-dimer level. There was a tendency for diabetes mellitus to increase the risk of DVT, although there was no statistical significance (p = 0.063). These epidemiologic data on DVT may help counsel patients about the risk of DVT, individualized risk assessment and accordingly the risk stratification.

Objective: To investigate the incidence of postoperative deep venous thrombosis (DVP) in patients undergoing surgeries for ankle fractures and identify the associated risk factors. Methods: This was a retrospective study. A total of 1451 patients undergoing surgery of ankle fractures from January 2016 to June 2019 were included. The inpatient medical record system was inquired for data collection, including demographics, comorbidities, injury, and surgery-related data, and laboratory biomarkers. DVT of the lower extremity was diagnosed by routine Doppler examination. Univariate analyses and multivariate logistic regression analyses were used to identify the independent risk factors. Results: Among the 1451 patients, DVT was confirmed in 38 cases, indicating an incidence of 2.6%. DVT involved both the operated and non-operated limbs in 8 patients (21.1%). DVT involved superficial femoral vein in 4 cases (6.6%), deep femoral vein in 2 (3.3%), popliteal vein in 5 (8.2%), posterior tibial vein in 11 (18.0%), and peroneal vein in 39 (63.9%). The median interval between operation and diagnosis of DVT was 7 days. Six risk factors were identified to be independently associated with DVT, including age (10-year increase) (OR, 1.44), preoperative stay (delay of each day) (OR, 1.11), anesthesia (general vs regional) (OR, 3.51), lower hemoglobin level (OR, 2.02), total cholesterol > 5.2 mmol/L (OR, 3.20), and reduced lymphocyte count (OR, 3.16). Conclusion: These identified factors, although not easily modifiable, do help counsel patients about the risk of DVT and help individualized assessment of the risk factors and accordingly the risk stratification.

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SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.