Objective

Congenital hypothyroidism (CH) is an inborn thyroid hormone (TH) deficiency mostly caused by thyroidal (primary CH) or hypothalamic/pituitary (central CH) disturbances. Most CH newborn screening (NBS) programs are thyroid-stimulating-hormone (TSH)-based, thereby only detecting primary CH. The Dutch NBS is based on measuring thyroxine (T4) aiming to detect primary and central CH at the cost of more false-positive referrals (FPRs) (positive predictive value (PPV) of 21% in 2007-2017). An artificial PPV of 26% was yielded when using a machine-based learning model on the adjusted dataset described below (methods) based on the Dutch CH NBS. Recently, amino acids (AAs) and acylcarnitines (ACs) are shown to be associated with TH concentration. We therefore aimed to investigate whether AAs and ACs measured during NBS can contribute to better performance of the CH screening in the Netherlands by using a revised machine-based learning model.

Methods

Dutch NBS data between 2007-2017 (CH screening results, AAs and ACs) from 1079 FPRs, 515 newborns with primary (431) and central CH (84), and data from 1842 healthy controls were used. A Random Forest model including these data was developed.

Results

The Random Forest model with an artificial sensitivity of 100% yielded a PPV of 48% and AUROC of 0.99. Besides T4 and TSH, tyrosine and succinylacetone were the main parameters contributing to the model’s performance.

Conclusions

The PPV improved significantly (26% to 48%) by adding several AAs and ACs to our machine-based learning model suggesting that adding these parameters benefit the current algorithm.