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.
Abbreviations:3D (Three-dimensional), AUROC (Area under the receiver-operating characteristic), BMI (Body mass index), CI (Confidence interval), CT (Computed tomography), IQR (Interquartile range), ROC (Receiver-operating characteristic), SD (Standard deviation), VIF (Variance inflation factor)
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Published online: November 12, 2021
IRB: METCZ20190048, approval date: April 9, 2019.
Funding: This work was supported by the Zuyderland Research and Innovation Fund of Zuyderland Medical Center (Heerlen, the Netherlands) [2019-005].
Conflicts of Interest: None of the authors have anything to declare.
© 2021 Elsevier Inc. All rights reserved.
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- Commentary: A New Way to Gauge Pectus SeveritySeminars in Thoracic and Cardiovascular SurgeryVol. 35Issue 1
- PreviewPectus excavatum is a disorder which most thoracic surgeons will encounter at some point in their practices. The degree of cardiac compression in patients with pectus excavatum is an important measurement in assessing the need for operative repair. Typically the assessment is done using computed tomography of the chest. The widest transverse measurement of the chest is compared to the anterior-posterior distance to calculate the Haller index.1 When determining whether surgery is needed or not, it is helpful to determine how significantly the heart is being compressed.
- Commentary: Challenges of 3D Surface Image-based Prediction Models for Pectus ExcavatumSeminars in Thoracic and Cardiovascular SurgeryVol. 35Issue 1
- PreviewSince 1987, the Haller index has been the gold standard for determining the need for surgical intervention in patients with pectus excavatum.1 However, this requires performing computed tomography (CT) scan with ionizing radiation in an adolescent and young adult population which has potential long-term ramifications.2,3 Daemen and colleagues 4 seek to eliminate these risks by providing a radiation-free alternative with the use of 3D surface imaging prediction models to evaluate for the presence of cardiac compression and ultimately identify patients in need of surgical intervention.