Predictive modeling for clinical features associated with Neurofibromatosis Type 1

Objective: Perform a longitudinal analysis of clinical features associated
with Neurofibromatosis Type 1 (NF1) based on demographic and clinical
characteristics, and to apply a machine learning strategy to determine
feasibility of developing exploratory predictive models of optic pathway
glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a
pediatric NF1 cohort. Methods: Using NF1 as a model system, we perform
retrospective data analyses utilizing a manually-curated NF1 clinical
registry and electronic health record (EHR) information, and develop
machine-learning models. Data for 798 individuals were available, with 578
comprising the pediatric cohort used for analysis. Results: Males and
females were evenly represented in the cohort. White children were more
likely to develop OPG (OR: 2.11, 95%CI: 1.11-4.00, p=0.02) relative to
their non-white peers. Median age at diagnosis of OPG was 6.5 years
(1.7-17.0), irrespective of sex. Males were more likely than females to
have a diagnosis of ADHD (OR: 1.90, 95%CI: 1.33-2.70, p<0.001), and
earlier diagnosis in males relative to females was observed. The gradient
boosting classification model predicted diagnosis of ADHD with an AUROC of
0.74, and predicted diagnosis of OPG with an AUROC of 0.82. Conclusions:
Using readily available clinical and EHR data, we successfully
recapitulated several important and clinically-relevant patterns in NF1
semiology specifically based on demographic and clinical characteristics.
Naïve machine learning techniques can be potentially used to develop and
validate predictive phenotype complexes applicable to risk stratification
and disease management in NF1.