Machine learning-based prediction of response to growth hormone treatment in Turner syndrome: the LG Growth Study

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

Development and validation of a nomogram to predict poor short-term response to recombinant human growth hormone treatment in children with growth disorders

PURPOSE

The purpose of this study was to develop and validate a clinical predictive model for predicting the likelihood of a poor therapeutic response during the first year of recombinant human growth hormone (rhGH) treatment in children with growth disorders.

METHODS

A total of 627 pediatric patients with growth disorders (GHD, ISS, TS, SGA) from The LG Growth Study cohort were evaluated. Restricted cubic splines (RCS) were utilized to investigate the association between predictors and the risk of poor rhGH response. Variables were selected using LASSO regression, and multivariate logistics regression models were established. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the predictive model's accuracy and clinical value. The predictive accuracy of the model was validated on the testing set.

RESULTS

Two predictive models containing 8 baseline predictors (diagnosis, age, height SDS, bone age minus chronological age, rhGH dosage, distance from mid-parental height in SDS, weight SDS, IGF-1 SDS) and 1 post-treatment predictor (height SDS gain at 6 months) were constructed by multivariate logistic regression analyses. The nomogram was built based on the multivariate predictive model and showed good discrimination and model fit effects in both the training set and the testing set. DCA and CIC analyses presented good clinical usability.

CONCLUSION

The clinical predictive model for predicting the probability of poor short-term response of rhGH treatment in pediatric patients with growth disorders is useful and can assist physicians in making clinical decisions.

Origin of the X-chromosome influences the development and treatment outcomes of Turner syndrome

Turner syndrome (TS) affects 1/2,500 live-born female infants. In the present study, we attempted to clarify the relationship between genetic factors (especially the X-chromosome origin), clinical features, body/sexual development, and treatment outcomes. We enrolled 39 female infants aged between 3 and 14 years. General demographic and clinical features were documented, and laboratory analysis of blood samples was performed. Subject karyotype was determined by G-banding of 50 peripheral white blood cells, and the parenteral origin of the retained X-chromosome was determined. Next, growth hormone (GH) treatment was prescribed for 12 months, with follow-ups performed as determined. For patient groups separated according to X-chromosome origin, the basal height, bone age, insulin-like growth factor (IGF)-1, and insulin-like growth factor binding protein-3 (IGFBP-3) levels were comparable; however, after the 12-month treatment, significant differences in the height increase and IGF-1 levels were observed. If the X-chromosome (or chromosomes) originated from both parents, the increase in height was less substantial, with lower serum IGF-1 levels. The uterine size, prolactin level, increased weight after treatment, and bone age difference after treatment negatively correlated with the mother’s age at the time of birth. The mother’s height at the time of birth demonstrated a negative correlation with the basal bone age difference and a positive correlation with the IGF-1 level. In summary, the retained X-chromosome derived from both parents is associated with poorer response to GH therapy. The mother’s age and height at the time of birth can strongly impact the patient’s body/sexual development and the response to GH treatment. Thus, the mother’s age and height at the time of birth and the parental origin of the X-chromosome should be carefully considered before developing a treatment plan for TS.