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Optimizing Diagnostic Accuracy of Clinical Red Flags in RASopathies.

Emanuele Bobbio¹, Martina Caiazza¹, Emanuele Monda¹

¹Inherited and Rare Cardiovascular Diseases, Department of Translational Medical Sciences, University of Campania "Luigi Vanvitelli", Monaldi Hospital, Naples, Italy.

American Journal of Medical Genetics. Part A

|March 11, 2026

Summary

This summary is machine-generated.

RASopathies are genetic disorders. Pulmonary valve stenosis and facial dysmorphisms are key indicators, prompting early genetic testing for RASopathies and improving diagnostic accuracy.

Keywords:

Noonan RAS signaling RASopathy genetics inherited cardiac conditions machine learning red flags

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Area of Science:

Genetics
Cardiology
Machine Learning

Background:

RASopathies are genetic disorders linked to the RAS-MAPK pathway, often causing heart defects and developmental issues.
Accurate diagnosis of RASopathies is crucial for timely intervention and management.

Purpose of the Study:

To evaluate the diagnostic yield of genetic testing in suspected RASopathy cases.
To identify clinical red flags predictive of RASopathy using machine learning.

Main Methods:

Retrospective analysis of 669 patients evaluated between 2020-2023.
Genetic testing (exome sequencing) and cardiovascular evaluation for suspected RASopathy cases.
Machine learning (random forest) applied to 13 clinical red flags for predictive analysis.

Main Results:

A 71% diagnostic yield for RASopathy was observed in 34 suspected cases (24 confirmed).
Pulmonary valve stenosis (PVS) and facial dysmorphisms were identified as the strongest predictors.
A model using PVS and facial dysmorphisms achieved an AUC of 0.86.

Conclusions:

PVS and facial dysmorphisms are critical indicators for RASopathy diagnosis, warranting genetic testing.
Machine learning effectively identified key clinical predictors for RASopathies.
Further external validation is required for the developed ML model.