A predictive model of delayed pseudoaneurysm formation in paediatric patients with isolated blunt splenic injury using logistic regression analysis.

AIM

To develop and evaluate a predictive model for delayed pseudoaneurysm formation after non-operative management (NOM) in children with blunt splenic injuries.

METHODS

A post hoc analysis of a multicenter cohort study in Japan included patients aged ≤16 years who underwent NOM for isolated blunt splenic injuries. The outcome was the formation of a pseudoaneurysm, which was not identified on admission and confirmed at least 24 h after admission. Predictors were determined from data available within 24 h of hospital arrival. Five predictive models were developed using logistic regression analysis and evaluated using discrimination (receiver operating characteristic [ROC] and precision-recall curve [PRC]), calibration (calibration plot and Brier score) and decision curve analysis (DCA) with bootstrap resampling data.

RESULTS

Pseudoaneurysms developed in 41 (9.4%) of 434 cases of isolated splenic injury in our cohort. Model 1 (19 predictors) had the highest ROC (0.828) and PRC (0.358), followed by model 5 (8 predictors; ROC 0.805, PRC 0.295). Calibration was similar across models, indicating good calibration. Models 1 and 5 outperformed the other DCAs. Overall, model 5, incorporating factors such as age, sex, Injury Severity Score, American Association for the Surgery of Trauma-Organ Injury Scale, contrast extravasation on computed tomography, concomitant injuries, cryoprecipitate dose and NOM details, was simpler and showed better predictive ability than the other models.

CONCLUSION

A predictive model for delayed pseudoaneurysm formation was developed with moderate discrimination and calibration. Further improvement using different modelling methods, such as machine learning, may be necessary.