Risk factor analysis and nomogram for predicting massive postoperative gastrointestinal bleeding in patients with Crohn’s disease: a multicenter retrospective study - Zhang, Mengyi; Yao, Danhua; Huang, Yuhua; He, Qi; He, Yining; Jiao, Yiran; Zhang, Yuanruohan; Chen, Chunqiu; Cui, Zhe; Li, Yousheng (Frontiers in Medicine, 2025)
BackgroundMassive postoperative gastrointestinal bleeding is a severe postoperative complication of Crohn’s disease (CD) with a high mortality rate, and deteriorating patients’ recovery. However, there are few related studies, and it lacks effective prevention measures. Therefore, we conducted a multicenter study to explore the risk factors for massive postoperative gastrointestinal bleeding in CD patients.MethodsThis study was a multicenter retrospective case-control study. Patients who were diagnosed with CD and underwent gastrointestinal (GI) surgery were enrolled. The control group was matched 1:4 for gender and age. By comparing perioperative medical information between two groups, risk factors were identified through logistic regression analysis. A nomogram was constructed and internal validation was performed by bootstrap resampling.ResultsA total of 170 patients were included. Multivariable logistic regression revealed the independent predictors of massive postoperative gastrointestinal bleeding involving the number of previous abdominal surgeries (OR = 2.56, 95% CI = 1.54–4.24), GI bleeding history (OR = 6.17, 95% CI = 1.59–23.97), serum albumin (ALB) (OR = 0.88, 95% CI = 0.81–0.96), and Nutrition Risk Screening 2002 (OR = 1.57, 95% CI = 1.08–2.29). The nomogram achieved an area under the curve (AUC) value of 0.85 (95% CI: 0.76–0.93). In internal validation, the AUC value was 0.976 (95% CI: 0.955–0.997). Calibration curves showed good alignment. DCA demonstrated that the diagnostic model had good clinical efficiency.ConclusionThe risk of massive postoperative gastrointestinal bleeding in CD patients will be increased with a GI bleeding history, more previous abdominal surgeries, higher nutrition risk, and lower ALB level. Our nomogram model is effective and could be a useful tool for prediction.