BackgroundIntra-abdominal infection is a common complication of blunt abdominal trauma. Early detection and intervention can reduce the incidence of intra-abdominal infection and improve patients’ prognoses. This study aims to construct a clinical model predicting postsurgical intra-abdominal infection after blunt abdominal trauma. MethodsThis study is a retrospective analysis of 553 patients with blunt abdominal trauma from the Department of General Surgery of 7 medical centers (2011–2021). A 7:3 ratio was used to assign patients to the derivation and validation cohorts. Patients were divided into 2 groups based on whether intra-abdominal infection occurred after blunt abdominal trauma. Multivariate logistic regression and least absolute shrinkage and selection operator regression were used to select variables to establish a nomogram. The nomogram was evaluated, and the validity of the model was further evaluated by the validation cohort. ResultsA total of 113 were diagnosed with intra-abdominal infection (20.4%). Age, prehospital time, C-reactive protein, injury severity score, operation duration, intestinal injury, neutrophils, and antibiotic use were independent risk factors for intra-abdominal infection in blunt abdominal trauma patients (P < .05). The area under the receiver operating curve (area under the curve) of derivation cohort and validation cohort was 0.852 (95% confidence interval, 0.784–0.912) and 0.814 (95% confidence interval, 0.751–0.902). The P value for the Hosmer-Lemeshow test was .135 and .891 in the 2 cohorts. The calibration curve demonstrated that the nomogram had a high consistency between prediction and practical observation. The decision curve analysis also showed that the nomogram had a better potential for clinical application. To facilitate clinical application, we have developed an online at https://nomogramcgz.shinyapps.io/IAIrisk/. ConclusionThe nomogram is helpful in predicting the risk of postoperative intra-abdominal infection in patients with blunt abdominal trauma and provides guidance for clinical decision-making and treatment.