Risk factors and nomogram model for short-term postoperative complications in patients with hirschsprung disease

Objective Short-term postoperative complications (SPCs) in patients with Hirschsprung disease (HD) are a topic of concern. The aims of this study were to identify the risk factors for different severities of SPCs based on the Clavien-Dindo classification system and to develop a nomogram model for clinical prediction.Methods The medical records of 304 eligible patients who were diagnosed with HD were retrospectively reviewed. Univariate/multivariate logistic regression analysis was used to identify the independent risk factors for SPCs of varying severity. Then, the enrolled patients were randomly divided into a training set (n = 212) and a testing set (n = 92) at a 7:3 ratio. Logistic regression analysis was conducted using R software, incorporating selected features from the LASSO regression model to construct a predictive model.Results The SPCs of 304 patients was 21.7% (66/304). LASSO regression identified six candidate predictors: type of HD, feeding methods, preoperative nutritional status, preoperative hypoproteinemia, preoperative HAEC, and length of postoperative hospital stay. Multivariate analysis further confirmed formula feeding (OR: 5.150, 95%CI: 1.912-14.29, P = 0.001), malnutrition (OR: 3.696, 95%CI: 0.987-13.37, P = 0.047), and L-HD (OR: 14.45, 95%CI: 4.690-51.44, P < 0.001) were independent factors for SPCs, and clinical nomogram model was developed based on these determinants. The area under of the receiver operating characteristics curve of the training set was 0.841 and that of the testing set was 0.834. Calibration curve analysis, decision curve analysis, and clinical impact curve analysis verified the accuracy and practicability of the predictive model.Conclusion The feeding method, preoperative nutritional status, and type of HD were found to be independent risk factors for SPCs. Formula feeding lacks immune protection, malnutrition delays recovery, and L-HD requires extensive surgery. A nomogram model based on the above factors could be utilized to predict whether HD patients are at risk of developing SPCs, thereby guiding timely nutritional interventions, personalized postoperative care, and close monitoring to reduce complication rates