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Background: Follow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability. Methods: We developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine. Results: One thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4–10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76–0.81(95% CI, 0.73–0.83) for cognitive regress with 4 variables and 0.79–0.83 (95% CI, 0.76–0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75–0.78 (95% CI, 0.72–0.82) for cognitive delay with 10 variables and 0.73–0.82 (95% CI, 0.72–0.85) for motor delay with 4 variables. Conclusions: Our EL-NDI demonstrated good performance using simpler, transparent, explainable models for clinical purpose. Implementing these models for VPI during follow-up visits may facilitate more informed discussions between parents and physicians and identify high-risk infants more effectively for early intervention. [ABSTRACT FROM AUTHOR]

The present study included the first case of mucopolysaccharidosis (MPS) type VII in Taiwan. During pregnancy, the patient was diagnosed with hydrops fetalis and had ascites aspiration 4 times. In the following years, she presented gradually with chronic lung disease, developmental delay, short stature, dysmorphic features of coarse face, macroglossia and pigeon chest with scoliosis. Upon referral at age 4 years, she had corneal clouding, mild limitation of range of motion (ROM) and hepatosplenomegaly. X-ray showed paddle ribs and dysplastic vertebral bodies. MPS was suspected and urine glycosaminoglycans (GAGs) elevated were noted. The leukocyte enzymatic analyses for MPS I, MPS II, MPS IIIB, MPS IVA, and MPS VI were all normal. Afterward, the molecular analysis showed two heterozygous genetic variants of c.104C > A and c.1454C > T in trans in the GUSB gene (NM_000181.4) which were the causes for MPS VII. Then, we checked the leukocyte β-glucuronidase activity for MPS VII and showed extremely low, therefore confirmed the diagnosis. Clinicians should increase the awareness on the early signs of MPS to have a prompt diagnosis and offer the correct treatment like enzyme replacement therapy (ERT) as early as possible. [ABSTRACT FROM AUTHOR]