Establish and validate a nomogram to help predict the preoperative risk of a pathological intussusception.
A primary cohort of patients who underwent surgery for an intussusception were enrolled from one center, while a validation cohort consisted of patients from another center. Multivariate logistic regression analysis was used to identify the variables to build the nomogram. A calibration curve accompanied by the Hosmer-Lemeshow test was used to assess the calibration of the nomogram. To quantify the discrimination of the nomogram, Harrell’s C-index was calculated. The performance of the validated nomogram was tested in the external validation cohort. The logistic regression formulae created during the analysis of the primary cohort was applied to all patients in the external validation cohort, and the total points for each patient were calculated.
The primary cohort consisted of 368 patients and the validation cohort included 74. The LASSO logistic algorithm identified three (recurrence episodes, mass size, and infection history) out of 11 potential clinical variables as significantly predictive of a pathologic intussusception. The C-index for the predictive nomogram was 0.922 (95% CI, 0.885–0.959) for the primary cohort and 0.886 (95% CI, 0.809–0.962) for the validation cohort. The decision curve showed that if the threshold probability of a patient in the validation cohort was > 7%, then the nomogram was more beneficial than either indiscriminately treating all or none of the patients.
We developed a nomogram based on clinical risk factors that could be used to individually predict pathological intussusceptions in children prior to surgical intervention.