DIAGNOSTIC PERFORMANCE OF THE AIMRO SCORE AND ITS ARTIFICIAL-INTELLIGENCE VERSION IN PREDICTING REACTIVITY TO METAL-CONTAINING OCCLUDERS IN CHILDREN
DOI:
https://doi.org/10.5281/Keywords:
risk stratification; ROC analysis; AUC; AIMRO score; machine learning; occluder; post-implantation reactivity; predictive accuracy; paediatric interventional cardiology.Abstract
The study compared four approaches to predicting post-implantation reactivity in 126 children after transcatheter closure of congenital septal heart defects: isolated allergological parameters, isolated immunological parameters, the integrated AIMRO score and the AIMRO-AI software module. For persistent reactivity the AIMRO score reached an accuracy of 90.5% with sensitivity 92.3%, specificity 89.7% and AUC 0.93, exceeding the allergological (69.0%, AUC 0.71) and immunological (78.6%, AUC 0.82) blocks. AIMRO-AI, which reconstructs specialized parameters from four routinely available ones, retained an accuracy of 83.3% and AUC 0.87, confirming its applicability where extended immunological testing is unavailable.
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