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Background: Different approaches to the prevention of postoperative ileus have been evaluated in numerous randomized controlled trials. This network meta-analysis aimed to investigate the relative effectiveness of different interventions in preventing postoperative ileus. Methods: Randomized controlled trials (RCTS) on the prevention of postoperative ileus were screened from Chinese and foreign medical databases and compared. STATA software was used for network meta-analysis using the frequency method. Random-effects network meta-analysis was also used to compare all schemes directly and indirectly. Results: A total of 105 randomized controlled trials with 18,840 participants were included in this report. The results of the network meta-analysis showed that intravenous analgesia was most effective in preventing the incidence of postoperative ileus, the surface under the cumulative ranking curve (SUCRA) is 90.5. The most effective intervention for reducing the first postoperative exhaust time was postoperative abdominal mechanical massage (SUCRA: 97.3), and the most effective intervention for reducing the first postoperative defecation time was high-dose opioid antagonists (SUCRA: 84.3). Additionally, the most effective intervention for reducing the time to initiate a normal diet after surgery was accelerated rehabilitation (SUCRA: 85.4). A comprehensive analysis demonstrated the effectiveness and prominence of oral opioid antagonists and electroacupuncture (EA) combined with gum. Conclusion: This network meta-analysis determined that oral opioid antagonists and EA combined with chewing gum are the most effective treatments and optimal interventions for reducing the incidence of postoperative ileus. However, methods such as abdominal mechanical massage and coffee require further high-quality research.
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Backgrounds: Spontaneous preterm birth (SPB) is a global problem. Early screening, identification, and prevention in asymptomatic pregnant women with risk factors for preterm birth can help reduce the incidence and mortality of preterm births. Therefore, this study systematically reviewed prediction models for spontaneous preterm birth, summarised the model characteristics, and appraised their quality to identify the best-performing prediction model for clinical decision-making. Methods: PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, VIP Database, and Wanfang Data were searched up to September 27, 2021. Prediction models for spontaneous preterm births in singleton asymptomatic pregnant women with risk factors were eligible for inclusion. Six independent reviewers selected the eligible studies and extracted data from the prediction models. The findings were summarised using descriptive statistics and visual plots. Results: Twelve studies with twelve developmental models were included. Discriminative performance was reported in 11 studies, with an Area Under the Curve (AUC) ranging from 0.75 to 0.95. The AUCs of the seven models were greater than 0.85. Cervical length (CL) is the most commonly used predictor of spontaneous preterm birth. A total of 91.7% of the studies had a high risk of bias in the analysis domain, mainly because of the small sample size and lack of adjustment for overfitting. Conclusion: The accuracy of the models for spontaneous preterm births in singleton asymptomatic women with risk factors was good. However, these models are not widely used in clinical practice because they lack replicability and transparency. Future studies should transparently report methodological details and consider more meaningful predictors with new progress in research on preterm birth.
