Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection: A Retrospective Clinical Study.

Objective: Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. Methods: We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. Results: The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768-0.820), 0.823 (95% CI 0.799-0.847) and 0.817 (95% CI 0.792-0.842) in the training cohort and 0.702 (95% CI 0.655-0.748), 0.705 (95% CI 0.659-0.752) and 0.747 (95% CI 0.703-0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications.

Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

Knowledge mapping of chronic insomnia: a bibliometric analysis (2000-2023).

BACKGROUND: Recently, significant scientific research breakthroughs have been witnessed in the treatment of chronic insomnia. However, it seems that there is currently no bibliometric analysis of this. Therefore, we hope to comprehensively review and analyze the scholarly system and research focus in the field of chronic insomnia treatment through bibliometric methods. METHODS: Between 2000 and 2023, we explored various papers in relation to the treatment of chronic insomnia in the Web of Science Core Collection(WOSCC) database. Subsequently, the collected papers were subjected to bibliometric analysis utilizing CiteSpace, VOSviewer, and the "bibliometric" package in R language. RESULTS: With China and the United States(USA) among them, a total of 2937 papers were published across 49 countries. Publications related to the treatment of chronic insomnia were increasing year by year. The Laval University, Washington University, Pittsburgh University, and Stanford University were key research institutions. The journal Sleep was widely popular in the field and was also one of the most cited journals. These papers came from 148 authors, with Morin, Charles M., Roth, Thomas, Espie, Colin A., Harvey, Allison G., and Buysse, Daniel J. publishing the most papers and Morin, cm being co-cited the most. The treatment process of chronic insomnia can be divided into three main stages: drug intervention, diseases related to chronic insomnia, and cognitive behavioral therapy and mental health. Keywords such as "children and adolescents", "novel coronavirus pneumonia" (COVID-19), "mental health" and "heart failure" have become the focus of current research. CONCLUSIONS: We carried out a detailed bibliometric review of the development trends and research results of chronic insomnia research through this study for the first time. The information it provides reveals recent research hotspots and cutting-edge issues, providing valuable reference materials for researchers focusing on the treatment of chronic insomnia.