Development of machine learning models to predict perioperative blood transfusion in hip surgery.

BACKGROUND: Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to develop machine learning models for predicting perioperative blood transfusion in hip surgery and identify significant risk factors. METHODS: Data of patients undergoing hip surgery between January 2013 and October 2021 in the Peking Union Medical College Hospital were collected to train and test predictive models. The primary outcome was perioperative red blood cell (RBC) transfusion within 72 h of surgery. Fourteen machine learning algorithms were established to predict blood transfusion risk incorporating patient demographic characteristics, preoperative laboratory tests, and surgical information. Discrimination, calibration, and decision curve analysis were used to evaluate machine learning models. SHapley Additive exPlanations (SHAP) was performed to interpret models. RESULTS: In this study, 2431 hip surgeries were included. The Ridge Classifier performed the best with an AUC = 0.85 (95% CI, 0.81 to 0.88) and a Brier score = 0.21. Patient-related risk factors included lower preoperative hemoglobin, American Society of Anesthesiologists (ASA) Physical Status > 2, anemia, lower preoperative fibrinogen, and lower preoperative albumin. Surgery-related risk factors included longer operation time, total hip arthroplasty, and autotransfusion. CONCLUSIONS: The machine learning model developed in this study achieved high predictive performance using available variables for perioperative blood transfusion in hip surgery. The predictors identified could be helpful for risk stratification, preoperative optimization, and outcomes improvement.

Multiobjective Evolutionary Learning for Multitask Quality Prediction Problems in Continuous Annealing Process.

In industrial production processes, the mechanical properties of materials will directly determine the stability and consistency of product quality. However, detecting the current mechanical property is time-consuming and labor-intensive, and the material quality cannot be controlled in time. To achieve high-quality steel materials, developing a novel intelligent manufacturing technology that can satisfy multitask predictions for material properties has become a new research trend. This article proposes a multiobjective evolutionary learning method based on a two-stage model with topological sparse autoencoder (TSAE) and ensemble learning. For the structure characteristics of a typical autoencoder (AE), a topology-related constraint is incorporated into the loss function of the AE, thus maintaining the global relationship among multistage input data to improve the data reconstruction quality. Then, a sparse representation of the data is added to the AE to achieve dimensionality reduction. Moreover, the extreme gradient boosting (XGBoost) method is applied to predict the mechanical properties of steel materials through collaboration learning mechanisms. To enhance the model accuracy, a multiobjective evolutionary algorithm (MOEA) with a knee solution strategy is used to optimize the network structure and hyperparameters of the two-stage model. Experiments are conducted using real steel production data from a continuous annealing process (CAP). The results verify that the proposed method obtains a higher prediction accuracy than other state-of-the-art methods and can guide practical production and new material design.

Design, Synthesis, and Biological Evaluation of N,N-Disubstituted-4-Arylthiazole-2-Methylamine Derivatives as Cholesteryl Ester Transfer Inhibitors.

Cholesteryl ester transfer protein (CETP) has been identified as a potential target for cardiovascular disease (CVD) for its important role in the reverse cholesteryl transfer (RCT) process. In our previous work, compound 5 was discovered as a moderate CETP inhibitor. The replacement of the amide linker by heterocyclic aromatics and then a series of N,N-substituted-4-arylthiazole-2-methylamine derivatives were designed by utilizing a conformational restriction strategy. Thirty-six compounds were synthesized and evaluated for their CETP inhibitory activities. Structure-activity relationship studies indicate that electron donor groups substituted ring A, and electron-withdrawing groups at the 4-position of ring B were critical for potency. Among these compounds, compound 30 exhibited excellent CETP inhibitory activity (IC50 = 0.79 ± 0.02 µM) in vitro and showed an acceptable metabolic stability.

Design, Synthesis and Biological Evaluation of N,N-Substituted Amine Derivatives as Cholesteryl Ester Transfer Protein Inhibitors.

N,N-Substituted amine derivatives were designed by utilizing a bioisosterism strategy. Consequently, twenty-two compounds were synthesized and evaluated for their inhibitory activity against CETP. Structure-activity relationship (SAR) studies indicate that hydrophilic groups at the 2-position of the tetrazole and 3,5-bistrifluoromethyl groups on the benzene ring provide important contributions to the potency. Among these compounds, compound 17 exhibited excellent CETP inhibitory activity (IC50 = 0.38 ± 0.08 µM) in vitro. Furthermore, compound 17 was selected for an in vitro metabolic stability study.