Development of Prediction Models for Cardiac Compression in Pectus Excavatum Based on Three-Dimensional Surface Images

Abstract

In pectus excavatum, three-dimensional (3D) surface imaging provides an accurate and radiation-free alternative to computed tomography (CT) to determine severity. Yet, it does not allow for cardiac evaluation since 3D imaging solely captures the chest wall surface. The objective was to develop a 3D image-based prediction model for cardiac compression in patients evaluated for pectus excavatum. A prospective cohort study was conducted including consecutive patients referred for pectus excavatum who received a thoracic CT. Additionally, 3D images were acquired. The external pectus depth, its length, craniocaudal position, cranial slope, asymmetry, anteroposterior distance and chest width were calculated from 3D images. Together with baseline patient characteristics they were submitted to forward multivariable logistic regression to identify predictors for cardiac compression. Cardiac compression on CT was used as reference. The model's performance was depicted by the area under the receiver operating characteristic (AUROC) curve. Internal validation was performed using bootstrapping. Sixty-one patients were included of whom 41 had cardiac compression on CT. A combination of the 3D image derived external pectus depth and external anteroposterior distance was identified as predictive for cardiac compression, yielding an AUROC of 0.935 (95% confidence interval [CI]: 0.878-0.992) with an optimism of 0.006. In a second model for males alone, solely the external pectus depth was identified as predictor, yielding an AUROC of 0.947 (95% CI: 0.892-1.000) with an optimism of 0.0002. We have developed two 3D image-based prediction models for cardiac compression in patients evaluated for pectus excavatum which provide an outstanding discriminatory performance between the presence and absence of cardiac compression with negligible optimism.