Supravalvular pulmonary stenosis: A risk factor for reintervention in Noonan syndrome with pulmonary valve stenosis

Abumehdi, Mohammad; Mehta, Chetan; Afifi, Ahmed; Yong, Sanfui; Chaudhari, Milind; Bhole, Vinay; Dhillon, Rami; Stümper, Oliver

doi:10.1002/ccd.30148

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…This result is consistent with the work by Holzmann et al who reported similar results (5). However, a recent study by Abumehdi et al reported that SVPS is signi cantly associated with the need for a re-intervention after BVP (12). A major difference is that our cohort size is smaller than the cohort of Abumehdi (n = 15 vs. n = 52), which is the largest reported series of performed BVP's in children with NS until present.…”

Section: Resultssupporting

confidence: 92%

“…The other 9 patients underwent successful surgical repair. Although in non-syndromic patients re-intervention rates were reported to be as low as 15% (8), re-intervention rate in NS has been shown to be much higher, ranging from 41% to 65% (5,7,12). These results are similar to those found in our study…”

Section: Resultssupporting

confidence: 91%

“…It was not possible to evaluate other genes as a separate group due to the low number of patients. RAF1, for example is known to be associated with more severe PS (12), however in our study, only one patient had a RAF1 mutation and did not require any treatment.…”

Section: Resultscontrasting

confidence: 54%

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Success rate of primary percutaneousballoon angioplasty in children withPulmonary Stenosis and Noonansyndrome

Rubbens

Muiño-Mosquera

Panzer

et al. 2023

Preprint

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Background and aim: Noonan syndrome (NS) is associated with different types of heart defects of which a supravalvular pulmonary stenosis ((SV)PS) is the most frequent. Possible treatment options are percutaneous balloon pulmonary valvuloplasty (BVP) or surgical intervention. Anatomical location of the PS may help predict BVP failure. We aimed to identify factors predicting treatment outcome and reintervention rate of BVP in PS, in children with NS. Methods: Medical records of children with a diagnosis of NS and in follow-up at Antwerp- and Ghent University Hospitals from 2000 to 2022 were retrospectively reviewed. Results: 32 children were identified with a SVPS, either isolated or in combination with other heart defects. 69% of children with PS had SVPS. The prevalence of PS and SVPS was similar for all genes. An isolated SVPS was identified as a risk factor for intervention. An intervention was necessary in 17/32 patients (53%). All but 2 children with pulmonary valve stenosis had SVPS. Only 2 of 17 patients had primary surgical repair. The remaining 15 (13 with SVPS) underwent BVP, of which 10 (67%) needed a second intervention, but all of them ultimately needed surgical repair. The global success rate of BVP was (31,1%). Conclusion: SVPS is the most frequent heart defect in children with NS. The prevalence of SVPS was similar for all genes. Isolated SVPS is a risk factor for intervention. The success rate of BVP in patients with NS is low. BVP might still be useful in selected cases and might be considered to clarify the anatomical location of PS.

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Section: Resultssupporting

confidence: 92%

Section: Resultssupporting

confidence: 91%

See 1 more Smart Citation

Success rate of primary percutaneousballoon angioplasty in children withPulmonary Stenosis and Noonansyndrome

Rubbens

Muiño-Mosquera

Panzer

et al. 2023

Preprint

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Cardiovascular Disease in the RASopathies

Chatfield

2024

The RASopathies

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Facial recognition models for identifying genetic syndromes associated with pulmonary stenosis in children

Shen,

Chen,

Huang

et al. 2024

Postgraduate Medical Journal

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Background Williams–Beuren syndrome, Noonan syndrome, and Alagille syndrome are common types of genetic syndromes (GSs) characterized by distinct facial features, pulmonary stenosis, and delayed growth. In clinical practice, differentiating these three GSs remains a challenge. Facial gestalts serve as a diagnostic tool for recognizing Williams–Beuren syndrome, Noonan syndrome, and Alagille syndrome. Pretrained foundation models (PFMs) can be considered the foundation for small-scale tasks. By pretraining with a foundation model, we propose facial recognition models for identifying these syndromes. Methods A total of 3297 (n = 1666) facial photos were obtained from children diagnosed with Williams–Beuren syndrome (n = 174), Noonan syndrome (n = 235), and Alagille syndrome (n = 51), and from children without GSs (n = 1206). The photos were randomly divided into five subsets, with each syndrome and non-GS equally and randomly distributed in each subset. The proportion of the training set and the test set was 4:1. The ResNet-100 architecture was employed as the backbone model. By pretraining with a foundation model, we constructed two face recognition models: one utilizing the ArcFace loss function, and the other employing the CosFace loss function. Additionally, we developed two models using the same architecture and loss function but without pretraining. The accuracy, precision, recall, and F1 score of each model were evaluated. Finally, we compared the performance of the facial recognition models to that of five pediatricians. Results Among the four models, ResNet-100 with a PFM and CosFace loss function achieved the best accuracy (84.8%). Of the same loss function, the performance of the PFMs significantly improved (from 78.5% to 84.5% for the ArcFace loss function, and from 79.8% to 84.8% for the CosFace loss function). With and without the PFM, the performance of the CosFace loss function models was similar to that of the ArcFace loss function models (79.8% vs 78.5% without PFM; 84.8% vs 84.5% with PFM). Among the five pediatricians, the highest accuracy (0.700) was achieved by the senior-most pediatrician with genetics training. The accuracy and F1 scores of the pediatricians were generally lower than those of the models. Conclusions A facial recognition-based model has the potential to improve the identification of three common GSs with pulmonary stenosis. PFMs might be valuable for building screening models for facial recognition. Key messages What is already known on this topic: Early identification of genetic syndromes (GSs) is crucial for the management and prognosis of children with pulmonary stenosis (PS). Facial phenotyping with convolutional neural networks (CNNs) often requires large-scale training data, limiting its usefulness for GSs. What this study adds: We successfully built multi-classification models based on face recognition using a CNN to accurately identify three common PS-associated GSs. ResNet-100 with a pretrained foundation model (PFM) and CosFace loss function achieved the best accuracy (84.8%). Pretrained with the foundation model, the performance of the models significantly improved, although the impact of the type of loss function appeared to be minimal. How this study might affect research, practice, or policy: A facial recognition-based model has the potential to improve the identification of GSs in children with PS. The PFM might be valuable for building identification models for facial detection.

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Supravalvular pulmonary stenosis: A risk factor for reintervention in Noonan syndrome with pulmonary valve stenosis

Cited by 4 publications

References 13 publications

Success rate of primary percutaneousballoon angioplasty in children withPulmonary Stenosis and Noonansyndrome

Success rate of primary percutaneousballoon angioplasty in children withPulmonary Stenosis and Noonansyndrome

Cardiovascular Disease in the RASopathies

Facial recognition models for identifying genetic syndromes associated with pulmonary stenosis in children

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