Comparison of the characteristics of the CF-H290I and PCF-Q260JI colonoscopes in non-sedated patients with a history of abdominal or pelvic surgery: a randomized controlled study.

BACKGROUND AND AIM: The purpose of this randomized controlled study was to compare the characteristics of the CF-H290I (high-definition) colonoscope with those of the PCF-Q260JI (high-resolution) colonoscope in non-sedated patients with a history of abdominal or pelvic surgery in an effort to help endoscopists to select more effectively and objectively between the various colonoscopes. METHODS: A total of 397 patients who underwent colonoscopy at the Affiliated Wuxi People's Hospital of Nanjing Medical University, between August 2022 and October 2022 were randomized to a CF-H290I group (n = 198) or a PCF-Q260JI group (n = 199) using a computer-generated allocation method. We compared the adenoma detection rate (ADR), patient satisfaction with the examination, discomfort associated with colonoscopy including abdominal distension and pain, cecal intubation time, and patient willingness to undergo colonoscopy in the future between the CF-H290I and PCF-Q260JI groups. RESULTS: There was no statistically significant difference in the overall ADR between the CF-H290I and PCF-Q260JI groups (81 [40.9%] vs 63 [31.7%], Z = 3.674, P = 0.055). However, the ADRs in the transverse colon and left colon were significantly higher in the CF-H290I group (22 [11.1%] vs 6 [3.0%], Z = 9.588, P = 0.002 and 57 [28.8%] vs 37 [18.6%], Z = 5.212, P = 0.017, respectively). More sessile serrated lesions were detected in the CF-H290I group (52 [26.3] vs 30 [15.1%], Z = 7.579, P = 0.006). Patient satisfaction with colonoscopy was better in the PCF-Q260JI group (8.91 ± 1.09 vs 8.51 ± 1.44, t = -3.158, P < 0.01) with less likelihood of discomfort (23 [11.6%] vs 41 [20.7%], Z = 6.144, P = 0.013), The number of patients willing to undergo colonoscopy in the future was significantly greater in the PCF-Q260JI group (168 [84.4%] vs 149 [75.3%], Z = 5.186, P = 0.023). The cecal intubation time was significantly shorter in the CF-H290I group (256.09 ± 155.70 s vs 315.64 ± 171.64 s, P = 0.004). There were no complications such as perforation or bleeding in either group. CONCLUSION: The CF-H290I and PCF-Q260JI colonoscopes each have advantages when used in patients with a history of abdominal or pelvic surgery. The CF-H290I has higher ADRs in the transverse and left colon whereas the PCF-Q260JI is less painful and better accepted by patients. This study was approved by the Clinical Research Ethics Committee of Wuxi People's Hospital and was registered in the Chinese Clinical Trial Registry (ChiCTR2200063092).

Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.

INTRODUCTION: Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. METHODS: This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. RESULTS: Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. CONCLUSION: The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.