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Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies. The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease. An observational cohort study. Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort. The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more. Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance. The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/. The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery. [ABSTRACT FROM AUTHOR]

An oilfield has been developed with waterflooding for a long time, and the water cut has reached a high level. Surfactant-polymer (SP) flooding is considered as the following enhanced oil recovery (EOR) technique. To determine the feasibility of SP flooding in this oilfield, the microscopic oil displacement experiments are performed, and the distribution of remaining oil after waterflooding and SP flooding is observed. The recovery rates are measured by the producing liquid amount and nuclear magnetic resonance (NMR) technique. The two methods show similar measurement results, with deviations of about 10%. The contributions of different pore and throat diameters are analyzed with the NMR technique. The results show that SP flooding has a much lower contribution limit of pore and throat diameter than waterflooding, indicating that SP flooding can effectively improve sweep efficiency. The SP flooding can improve by about 30% EOR rate. The frozen slicing technique and ultraviolet fluorescence excitation are applied to observe the status and distribution of remaining oil. The results show that both waterflooding and SP flooding can effectively reduce all types of remaining oil. Comparing the remaining oil proportions in the high and low permeability cores, we can conclude that the high permeability contributes to the performance of waterflooding and SP flooding. This work proves the efficiency of SP flooding in this oilfield development and provides a microscopic explanation of remaining oil distribution after waterflooding and SP flooding. • SP flooding is an effective approach for EOR compared with waterflooding. • Both waterflooding and SP flooding can effectively reduce all types of remaining oil. • High permeability contributes to the performance of waterflooding and SP flooding. • Recovery after waterflooding and SP flooding can reach 43.58% and 8.21% respectively. [ABSTRACT FROM AUTHOR]

• Cement leak is common for percutaneous vertebroplasty in metastatic spinal disease. • A proposed and validated algorithm can screen patients with high cement leak risk. • The algorithm included four significant characteristics. • The validation group AUROC was 0.69, and the goodness-of-fit test P-value was 0.50. To investigate the epidemiology of cement leaks and further develop an algorithm to detect the high risk of cement leaks among advanced cancer patients with metastatic spinal disease treated with percutaneous vertebroplasty. This study retrospectively analyzed 309 patients with metastatic spinal disease treated with percutaneous vertebroplasty. Patients were randomly divided into a training group and a validation group. In the training group, 13 potential characteristics were analyzed for their abilities to predict cement leaks. Discal cement leakage and paravertebral cement leakage were excluded from the analysis. Those characteristics identified as having significant predictive value were used to develop a predictive algorithm. Internal validation of the algorithm was performed based on discrimination and calibration qualities. Overall, cement leaks occurred in 61.17% (189/309) patients. Among the 13 characteristics analyzed, younger age (P = 0.03), extravertebral bone metastases (P = 0.02), increased number of treated vertebrae levels (P < 0.01), and cortical osteolytic destruction in the posterior wall (P = 0.01) were included in the algorithm. This algorithm generates a score between 0 and 16 points, with higher scores indicating a higher risk of cement leakage. The area under the receiver operating characteristic curve (AUROC) value for the algorithm was 0.75 in the training group and 0.69 in the validation group. The mean correct classification rates for the training and validation groups were 73.5% and 64.9%, respectively, and the corresponding P-values of the goodness-of-fit test were 0.70 and 0.50. Cement leaks are common in patients with metastatic spinal disease treated with percutaneous vertebroplasty. The present study proposed and internally validated an algorithm that can be used to screen patients at high risk of cement leakage. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the migration and blocking mechanism of self-aggregated divinylbenzene-co-acrylamide (DCA) microspheres as a novel profile control. Scanning electron microscopy (SEM) and laser diffracting measurement experiments were conducted to examine the shape and size of the microspheres. The optimal matching factor between microsphere diameter and the pore throat was from 0.15 to 0.19. The migration position of DCA microspheres in porous media was studied via SEM. The images revealed that DCA microspheres can exhibit long-distance migration. Moreover, the effects of different matching factors on oil recovery rate have been investigated by the core displacement experiments. The enhanced oil recovery was the highest when the DCA microspheres with the optimum matching factor was injected. Additionally, the oil saturation distribution demonstrates that DCA microspheres with optimal matching factors can enhance sweep efficiency by plugging high-permeability water channels. Image 1 • Self-aggregating microspheres with particle sizes ranging from 50 nm to 102 μm were synthesized. • Self-aggregating microspheres have shell-core structures and can be aggregated by high-valence metal ions. • The optimum matching factor of self-aggregating microspheres is from 0.15 to 0.19. • Self-aggregating microspheres gathered in the pore throat and formed a partial seal inside the core. • Self-aggregating microspheres can block the high-permeability zones and increased sweep efficiency. [ABSTRACT FROM AUTHOR]

A great deal of oil field is currently produced using water flooding and the water cut has reached a high level, which requires enhanced oil recovery (EOR) techniques to improve the recovery. Surfactant-polymer (SP) flooding is the combination of surfactant flooding and polymer flooding. The polymer is added to increase the viscosity and viscoelasticity of the fluid while the surfactant is included to decrease the oil–water interfacial tension and change the wettability. Although the mechanism of SP flooding has been deeply investigated, the application of SP flooding on a specific oil field requires the selection of optimal SP system. Therefore, for the first time, we performed a series of experiments to determine the optimal SP system for high water-cut oil field. In the injection capability experiment, we observe that the SP system is injected into the core with less resistance compared with single polymer solution. In the flooding experiment, regardless of the polymer types, the improvement of recovery becomes significant when the injected PVs increases. SP flooding shows the higher improvement of recovery compared with single polymer flooding or surfactant flooding. Based on the performance of recovery improvement, we recommend the optimal SP system for the studied case is the combination of DQ-TSRP01 polymer and surfactant. The optimal injection parameters of this SP system are 1500 mg/L polymer concentration, 0.3 injection PV and the injection timing of 96% water cut. The finding of this study can help for better understanding of the application of SP flooding in high water-cut oil field production. • SP system (DQ-TSRP01 + surfactant) significantly improves the oil recovery. • Optimal injection parameters are 1500 mg/L polymer concentration, 0.3 injection PV. • Existence of surfactant in the SP system reduces the residual resistance coefficient. • Improvement of recovery becomes significant when the injected PVs increases. [ABSTRACT FROM AUTHOR]

Background: The study aimed to identify the risk factors of cement leakage following percutaneous vertebroplasty for spinal metastases.Methods: 230 consecutive patients with 530 vertebrae were retrospectively reviewed. Characteristics including age, primary cancer, location, pathological fracture, the integrity of the posterior wall, and the volume of bone cement were considered as potential risk factors. Cement leakage was evaluated by postoperative imaging examination and classified into three subtypes with different potential sequelae: spinal canal leakage, intravascular leakage around vertebrae, intradiscal and paravertebral leakage. Univariate and multivariate analyses were used to assess the risk factors.Results: Leakage was detected in 185 vertebrae (34.9%), 18.3% for intradiscal and paravertebral, 13.2% for intravascular around vertebrae, and 7.0% for spinal canal. Multivariate analysis showed that incomplete posterior wall (P = 0.001) and breast cancer (P = 0.015) were strong predictive factors for spinal canal leakage, incomplete posterior wall (P = 0.024) was for intravascular leakage around vertebrae, thoracic (P = 0.010) and pathological fracture (P = 0.000) were for intradiscal and paravertebral leakage.Conclusions: Our findings suggest that cement leakage is common following percutaneous vertebroplasty for spinal metastases. The incomplete posterior wall is an unfavourable factor for intravascular leakage around vertebrae. Vertebrae with incomplete posterior wall and breast cancer metastases are more likely to develop spinal canal leakage. [ABSTRACT FROM AUTHOR]