Exploring perioperative risk factors for poor recovery of postoperative gastrointestinal function following gynecological surgery: A retrospective cohort study.

Purpose: To investigate perioperative risk factors that affect the recovery of postoperative gastrointestinal function in patients undergoing gynecological surgery and to establish a preoperative risk prediction scoring system. Methods: In this retrospective cohort study, characteristics and perioperative factors of patients who underwent elective gynecological surgery at Union Hospital from January 2021 to March 2022 were extracted from electronic medical records. Patients were grouped according to the Intake, Feeling nauseated, Emesis, physical Exam, and Duration of symptoms (I-FEED) scoring system to compare collected data. Results: In total, clinical data from 208 gynecological patients were extracted. The incidence of poor postoperative gastrointestinal recovery was 7.21 %. The number of previous abdominal surgeries (0.73 ± 0.06 vs 1.20 ± 0.24, p = 0.044), the incidence of malignant disease (20.2 % vs 53.3 %, p = 0.003), postoperative maximum WBC count (9.15 vs 12.44, p = 0.005) and postoperative minimum potassium (3.97 ± 0.36 vs 3.76 ± 0.37, p = 0.036) were not only associated with poor postoperative gastrointestinal recovery, but also malignant disease (p = 0.000), postoperative maximum WBC count (p = 0.027) and postoperative minimum potassium (p = 0.024) were significantly associated with the severity of postoperative gastrointestinal function. An increased number of previous abdominal surgeries and malignant primary disease could increase the risk of an I-FEED score >2 as independent risk factors. Conclusion: Patients with poor postoperative GI function had poorer postoperative recovery outcomes. A preoperative score prediction system was established, in which patients with ≥2 points had a 19.4 % risk of poor postoperative gastrointestinal recovery. Higher-quality prospective studies should be performed to achieve more precise risk stratification and to construct a more accurate prediction system.