Updated cost-effectiveness analysis of adebrelimab plus chemotherapy for extensive-stage small cell lung cancer in China.

OBJECTIVE: The CAPSTONE-1 trial demonstrated that adebrelimab-based immunotherapy yielded a favourable survival benefit compared with chemotherapy for patients with extensive-stage small cell lung cancer (ES-SCLC). This study aims to evaluate the cost-effectiveness of this immunotherapy in the treatment of ES-SCLC from a healthcare system perspective in China. DESIGN: The TreeAge Pro software was used to establish a three-state partitioned survival model. Survival data came from the CAPSTONE-1 trial (NCT03711305), and only direct medical costs were included. Utility values were obtained from the published literature. Sensitivity analysis was performed to explore the robustness of the model. The cost-effectiveness of immunotherapy was investigated through scenario and exploratory analyses in various settings. OUTCOME MEASURES: Total costs, incremental costs, life years, quality-adjusted life-years (QALYs), incremental QALYs and incremental cost-effectiveness ratio (ICER). RESULTS: The basic analysis revealed that the adebrelimab group achieved a total of 1.1 QALYs at a cost of US$65 385, while the placebo group attained 0.78 QALYs at a cost of US$12 741. ICER was US$163 893/QALY. Sensitivity analysis confirmed that the model was robust. Results from scenario and exploratory analyses indicated that the combination of adebrelimab and chemotherapy did not demonstrate cost-effectiveness in any scenario. CONCLUSIONS: From the perspective of the Chinese healthcare system, adebrelimab in combination with chemotherapy for the treatment of ES-SCLC was not economical compared with chemotherapy.

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.

OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.

BACKGROUND: Early identification of elderly patients undergoing non-cardiac surgery who may be at high risk for postoperative cognitive dysfunction (POCD) can increase the chances of prevention for them, as extra attention and limited resources can be allocated more to these patients. AIM: We performed this analysis with the aim of developing a simple, clinically useful machine learning (ML) model to predict the probability of POCD at 3 months in elderly patients after non-cardiac surgery. METHODS: We collected information on patients who received surgical treatment at Nanjing First Hospital from May 2020 to May 2021. We used LASSO regression to select key features and built 5 ML models to assess the risk of POCD at 3 months in elderly patients after non-cardiac surgery. The Shapley Additive exPlanations (SHAP) and methods were introduced to interpret the best model. RESULTS: A total of 415 patients with non-cardiac surgery were included. The support vector machine (SVM) was the best-performing model of the five ML models. The model showed excellent performance compared to the other four models. The SHAP results showed that VAS score, age, intraoperative hypotension, and preoperative hemoglobin were the four most important features, indicating that the SVM model had good interpretability and reliability. The website of the web-based calculator was https://modricreagan-non-3-pocd-9w2q78.streamlit.app/ . CONCLUSION: Based on six important perioperative variables, we successfully established a series of ML models for predicting POCD occurrence at 3 months after surgery in elderly non-cardiac patients, with SVM model being the best-performing model. Our models are expected to serve as decision aids for clinicians to monitor screened high-risk patients more closely or to consider further interventions.

Novel Bedside Dynamic Nomograms to Predict the Probability of Postoperative Cognitive Dysfunction in Elderly Patients Undergoing Noncardiac Surgery: A Retrospective Study.

Purpose: Early and accurate prediction of elderly patients at high risk of postoperative cognitive dysfunction (POCD) after non-cardiac surgery will provide favorable evidence for rational perioperative management and long-term postoperative recovery. This study aimed to develop bedside dynamic nomograms to provide accurately an individualized prediction of the risk of POCD at 6-month postoperatively with patients undergoing non-cardiac surgery and to guide clinical decision-making and postoperative management. Patients and Methods: We retrospectively collected patients undergoing surgical treatment at the Nanjing First Hospital between May 2020 and May 2021. We collected the data on preoperative, intraoperative, and postoperative variables. Clinical and laboratory data on admission and intraoperative variables and postoperative variables were used. We measured the performances of the nomograms using sensitivity, specificity of the receiver operating characteristic (ROC), the area under the ROC curves (AUC), the 10-fold cross-validation, and decision curve analysis (DCA). Results: POCD was observed in 23 of 415 patients (5.6%) at 6-month postoperatively. The preoperative and postoperative models obtained 91.6% and 94.0% accuracy rates on the data. Compared to the preoperative model, the postoperative model had an area under the receiver characteristic curve (AUC) of 0.973 vs 0.947, corresponding to a specificity of 0.941 vs 0.918 and a sensitivity of 0.913 vs 0.870. The overall performance of the postoperative model was better than the preoperative model. Conclusion: In this study, we developed novel bedside dynamic nomograms with reasonable clinical utility that can provide individualized prediction of POCD risk at 6-month postoperatively in elderly patients undergoing non-cardiac surgery at different time points based on patient admission and postoperative data. External validations are needed to ensure their value in predicting POCD in elderly patients.

Circ_0072995 Promotes Proliferation and Invasion via Regulating miR-1253/EIF4A3 Signaling in HCC.

BACKGROUND: Hepatocellular carcinoma (HCC) is a major threat for human health. This work aimed to determine the potential function of circ_0072995 in HCC progression and its molecular mechanism. METHODS: qRT-PCR was conducted to analyze circ_0072995 expression. CCK8 and colony formation assays were utilized to detect cell proliferation. Transwell assay was performed to determine migration and invasion. Interactions among circ_0072995, miR-1253 and EIF4A3 (Eukaryotic Translation Initiation Factor 4A3) were predicted through bioinformatics methods and confirmed via luciferase reporter assay and RNA pulldown assay. RESULTS: circ_0072995 expression was upregulated in HCC tissues. Circ_0072995 high level was associated with poor prognosis. Circ_0072995 knockdown impaired proliferation, migration, invasion and survival. MiR-1253 was sponged by circ_0072995 and targeted EIF4A3 directly. Circ_0072995 inhibited miR-1253 to upregulate EIF4A3 level. CONCLUSION: Circ_0072995 exerted tumorigenic roles to enhance HCC progression through activating EIF4A3 by sponging miR-1253.