Paediatrics and Child Health - Journal Articles
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- ItemGrowth hormone stimulation testing patterns contribute to sex differences in pediatric growth hormone treatment(Karger, 2021-10-18) Kamoun, Camilia; Hawkes, Colin P.; Gunturi, Hareesh; Dauber, Andrew; Hirschhorn, Joel N.; Grimberg, Adda; National Institute of Diabetes and Digestive and Kidney Diseases; Pfizer; Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentIntroduction: Males are twice as likely as females to receive pediatric growth hormone (GH) treatment in the USA, despite similar distributions of height z (HtZ)-scores in both sexes. Male predominance in evaluation and subspecialty referral for short stature contributes to this observation. This study investigates whether sex differences in GH stimulation testing and subsequent GH prescription further contribute to male predominance in GH treatment. Methods: Retrospective chart review was conducted of all individuals, aged 2–16 years, evaluated for short stature or poor growth at a single large tertiary referral center between 2012 and 2019. Multiple logistic regression models were constructed to analyze sex differences. Results: Of 10,125 children referred for evaluation, a smaller proportion were female (35%). More males (13.1%) than females (10.6%) underwent GH stimulation testing (p < 0.001) and did so at heights closer to average (median HtZ-score −2.2 [interquartile range, IQR −2.6, −1.8] vs. −2.5 [IQR −3.0, −2.0], respectively; p < 0.001). The proportion of GH prescriptions by sex was similar by stimulated peak GH level. Predictor variables in regression modeling differed by sex: commercial insurance predicted GH stimulation testing and GH prescription for males only, whereas lower HtZ-score predicted GH prescription for females only. Conclusions: Sex differences in rates of GH stimulation testing but not subsequent GH prescription based on response to GH stimulation testing seem to contribute to male predominance in pediatric GH treatment. That HtZ-score predicted GH prescription in females but not males raises questions about the extent to which sex bias – from children, parents, and/or physicians – as opposed to objective growth data, influence medical decision-making in the evaluation and treatment of short stature.
- ItemReference centiles for infant sleep parameters from 4 to 16 weeks of age: findings from an Irish cohort(BMJ Publishing Group Ltd & Royal College of Paediatrics and Child Health, 2023-03-21) O'Sullivan, Marc Paul; Livingstone, Vicki; Korotchikova, Irina; Dempsey, Eugene M.; Murray, Deirdre M.; Boylan, Geraldine B.; Science Foundation Ireland; Johnson and JohnsonObjectives: To establish unconditional reference centiles for sleep parameters in infants 4–16 weeks of age. Design and setting: Secondary data analysis of sleep parameters recorded at 4–16 weeks of age in a longitudinal randomised controlled trial (RCT) (BabySMART). Patients: Healthy term infants assigned to the non-intervention arm of the RCT. Main outcome measures: Infants’ sleep duration was recorded by parents/guardians daily, from week 2–16 of age using a sleep diary. Reference centiles for total, daytime, night-time and longest sleep episode duration were estimated using multilevel modelling. Results: One hundred and six infants, mean (SD) gestational age of 39.9 (1.2) weeks and mean (SD) birth weight of 3.6 (0.5) kg had sleep recorded contributing 1264 measurements for each sleep parameter. Between 4 and 16 weeks of age total sleep duration in a 24-hour period, night-time sleep duration in a 12-hour period and infant’s longest sleep episode duration increased, while daytime sleep duration in a 12-hour period decreased. Conclusions: Reference centiles up to 4 months of age in infants highlight the gradual decrease in daytime sleep and large increases in night-time sleep, which occur in tandem with increasing lengths of sleep episodes. These reference centiles provide useful sleep values for infant sleep trajectory occurring in early life and may be helpful for parents and clinicians.
- ItemAll Island Congenital Heart Network brings diagnosis closer to home(Irish Medical Organisation, 2022-12) Finn, Daragh; Allawendy, S.A.A.; Dempsey, Eugene M.; McMahon, C. J.Aim: The All-Island congenital heart network appointed paediatricians with expertise in cardiology in regional centres. Prior to these appointments children with suspected congenital heart disease were referred to the national children’s heart centre for investigation. The aim of this study is to quantify paediatric cardiology activity in a regional Irish centre over the first year of service provision. Methods: Data was collected retrospectively on all inpatient neonatal referrals over a 12-month period (January 2019 to January 2020). Results: There were 268 neonatal referrals. Premature infants (< 37 weeks gestation) accounted for 26% (n= 69) of total neonatal referrals. Congenital cardiac disease was identified in 58.5% (n= 113) of referrals. Cardiac intervention in the first year of life was required in 24 infants, 12.2% of referrals (5.6% catheter and 6.6% surgery). Discussion: Our report displays how clinical networks of care can reduce hospital transfers from regional neonatal centres for non-invasive cardiology investigations.
- ItemDevelopment of an EEG artefact detection algorithm and its application in grading neonatal hypoxic-ischemic encephalopathy(Elsevier Ltd., 2022-10-21) O'Sullivan, Mark E.; Lightbody, Gordon; Mathieson, Sean R.; Marnane, William P.; Boylan, Geraldine B.; O'Toole, John M.; Wellcome Trust; Science Foundation IrelandObjective: The primary aim of this study is to develop and evaluate algorithms for neonatal EEG artefact detection. The secondary aim is to subsequently assess its application as a post-processing routine for automated EEG grading of background abnormalities in neonatal hypoxic-ischemic encephalopathy (HIE). Methods: A database of neonatal EEG with expertly annotated artefacts was used to train and validate machine learning models to automatically identify EEG epochs containing artefacts. Three approaches were developed and compared, specifically, a simple threshold-based digital signal processing (DSP) method, a machine learning method, and a deep learning method. The artefact detection classifier was subsequently assessed as a post-processing tool to assist in the application of automated EEG grading of HIE. A new deep learning model for grading the EEG was developed by training an existing network on a large, multi-centre dataset. The artefact detection algorithm was integrated into the grading algorithm through a post-processing routine. Results: Using a database containing 19 h of EEG from 51 patients with per-channel and per-second annotations of artefacts, a deep learning convolutional neural network solution achieved best performance for artefact detection with an area under the operating characteristic curve (AUC) of 0.84, compared to an AUC of 0.68 and 0.82 for a DSP method and a random-kernel ridge-classifier model, respectively. The automated EEG grading algorithm was trained and tested on 653 h of EEG from 181 patients, which achieved an accuracy of 82.8 % (95 % CI: 80.5 % to 85.2 %). The percentage of detected artefacts in the misclassified epochs was not statistically different (p = 0.568) compared to that of correctly classified epochs. Using artefact detection, a small number of epochs were removed from grading, resulting in a minor increase in accuracy for the EEG grading algorithm from 82.6 % to 83.6 %. Conclusion: Deep learning methods achieved highest classification performance for neonatal EEG artefact detection, although a ridge classifier using random kernels achieved comparable performance without significant parameter tuning or training time. The inclusion of artefact detection in automated EEG grading does not significantly improve accuracy in our curated dataset, but does allow for a quality measure to be presented alongside the automated EEG grades which may increase user confidence in its real-world application.
- ItemChallenges of developing robust AI for intrapartum fetal heart rate monitoring(Frontiers Media, 2021-10-26) O'Sullivan, Mark E.; Considine, Elizabeth C.; O'Riordan, Mairead; Marnane, William P.; Rennie, J. M.; Boylan, Geraldine B.; Science Foundation IrelandBackground: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.