AIM: To evaluate the protective effects on kidney tissue of frequently used intravenous anesthetics (ketamine, propofol, thiopental, and fentanyl) in rats with obstructive jaundice.
AIM: To study possible gynecological organ pathologies in the differential diagnosis of acute right lower abdominal pain in patients of reproductive age.
BACKGROUND Laparoscopic radical gastrectomy is widely used,and perioperative complications have become a highly concerned issue.AIM To develop a predictive model for complications in laparoscopic radical gastrectomy f...BACKGROUND Laparoscopic radical gastrectomy is widely used,and perioperative complications have become a highly concerned issue.AIM To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery,guide perioperative treatment strategies for gastric cancer patients,and prevent serious complications.METHODS In total,998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model,and 398 patients were included in the validation group.The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected.Three machine learning methods,lasso regression,random forest,and artificial neural networks,were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy,and their prediction efficacy and accuracy were evaluated.RESULTS The constructed complication model,particularly the random forest model,could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy.It exhibited stable performance in external validation and is worthy of further promotion in more centers.CONCLUSION Using the risk factors identified in multicenter datasets,highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established.We hope to facilitate the diagnosis and treatment of preoperative and postoperative decision-making by using these models.展开更多
文摘AIM: To evaluate the protective effects on kidney tissue of frequently used intravenous anesthetics (ketamine, propofol, thiopental, and fentanyl) in rats with obstructive jaundice.
文摘AIM: To study possible gynecological organ pathologies in the differential diagnosis of acute right lower abdominal pain in patients of reproductive age.
基金Natural Science Foundation of Fujian Province,No.2021J011360,and No.2020J011230Natural Science Foundation of Xiamen,China,No.3502Z20214ZD1018,and No.3502Z20227096+2 种基金Medical Innovation Project of Fujian Provincial Health Commission,No.2021CXB019Youth Scientific Research Project of Fujian Provincial Health Commission,No.2022QNB013Bethune Charitable Foundation,No.HZB-20190528-10.
文摘BACKGROUND Laparoscopic radical gastrectomy is widely used,and perioperative complications have become a highly concerned issue.AIM To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery,guide perioperative treatment strategies for gastric cancer patients,and prevent serious complications.METHODS In total,998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model,and 398 patients were included in the validation group.The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected.Three machine learning methods,lasso regression,random forest,and artificial neural networks,were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy,and their prediction efficacy and accuracy were evaluated.RESULTS The constructed complication model,particularly the random forest model,could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy.It exhibited stable performance in external validation and is worthy of further promotion in more centers.CONCLUSION Using the risk factors identified in multicenter datasets,highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established.We hope to facilitate the diagnosis and treatment of preoperative and postoperative decision-making by using these models.
