Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2025
Analysis of risk factors for reflux esophagitis after proximal gastrectomy in patients with proximal gastric cancer and construction of a nomogram prediction model.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Jinjin, Gu Qiuping, Li Zhonghua
Аннотация
OBJECTIVE: To identify the risk factors for reflux esophagitis (RE) after proximal gastrectomy (PG) in patients with proximal gastric cancer and the construct of a nomogram prediction model.
METHODS: This retrospective study included 421 patients with proximal gastric cancer who underwent PG at our hospital between January 2022 and April 2024. Patients were randomly grouped into a modeling group (295 cases) and a validation group (126 cases). The occurrence of postoperative RE was the primary outcome. Logistic regression was used to analyze potential risk factors. R software was used to construct the nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate discrimination, the Hosmer-Lemeshow (H-L) test for calibration, and decision curve analysis (DCA) to evaluate its clinical utility.
RESULTS: Among the 421 patients, 88 cases (20.90%) developed RE, while among 295 patients in the modeling group, 62 cases (22.02%) developed RE. Multivariate logistic regression identified BMI, surgical time, intraoperative hemorrhage, length of esophagectomy, residual stomach volume, and stapler size were risk factors for postoperative RE (P<0.05). The AUC of the nomogram in the modeling group was 0.883, and in the validation group it was 0.851. H-L test showed good calibration in the modeling group (χ²=7.305, P=0.728) and the validation group (χ²=6.896, P=0.701). The DCA curve indicated that the nomogram offered clinical benefit when the threshold probability was between 0.08 and 0.97.
CONCLUSION: BMI, surgical time, intraoperative blood loss, length of esophagectomy, residual stomach volume, and stapler size are significant factors for postoperative RE. The nomogram model constructed based on these factors can effectively predict the risk of postoperative RE in patients with proximal gastric cancer, potentially aiding in personalized risk stratification and preventive strategies.
METHODS: This retrospective study included 421 patients with proximal gastric cancer who underwent PG at our hospital between January 2022 and April 2024. Patients were randomly grouped into a modeling group (295 cases) and a validation group (126 cases). The occurrence of postoperative RE was the primary outcome. Logistic regression was used to analyze potential risk factors. R software was used to construct the nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate discrimination, the Hosmer-Lemeshow (H-L) test for calibration, and decision curve analysis (DCA) to evaluate its clinical utility.
RESULTS: Among the 421 patients, 88 cases (20.90%) developed RE, while among 295 patients in the modeling group, 62 cases (22.02%) developed RE. Multivariate logistic regression identified BMI, surgical time, intraoperative hemorrhage, length of esophagectomy, residual stomach volume, and stapler size were risk factors for postoperative RE (P<0.05). The AUC of the nomogram in the modeling group was 0.883, and in the validation group it was 0.851. H-L test showed good calibration in the modeling group (χ²=7.305, P=0.728) and the validation group (χ²=6.896, P=0.701). The DCA curve indicated that the nomogram offered clinical benefit when the threshold probability was between 0.08 and 0.97.
CONCLUSION: BMI, surgical time, intraoperative blood loss, length of esophagectomy, residual stomach volume, and stapler size are significant factors for postoperative RE. The nomogram model constructed based on these factors can effectively predict the risk of postoperative RE in patients with proximal gastric cancer, potentially aiding in personalized risk stratification and preventive strategies.