Causes of Sudden Unexpected Death in Childhood: Autopsy Findings from a Specialist Centre

OBJECTIVES: To investigate the aetiologies of sudden unexpected death from natural causes in children aged 1-18 years by retrospective examination of autopsy records from a single centre. MATERIALS AND METHODS: The post-mortem findings from 548 children (1996-2015) were examined. Details were entered into an established research database and categorized according to >400 pre-defined criteria. RESULTS: There were 265 previously apparently healthy children and 283 with pre-existing, potentially life-limiting, conditions. There were more males than females (M:F 1.4:1), and deaths were more frequent in the winter. Infection was commonest accounting for 43% of all deaths. Non-infectious diseases were identified as cause of death in 28%, and 29% of all deaths were unexplained. There was no significant difference in the proportions of deaths in each category between the previously healthy children and those with pre-existing conditions. CONCLUSION: Sudden unexpected death is a rare presentation of death in childhood and those with pre-existing conditions may be more at risk. Standardisation of the post-mortem procedure in such cases may result in more ancillary investigations performed as routine and may reduce the number of cases that are 'unexplained'

Engaging children and young people on the potential role of artificial intelligence in medicine

INTRODUCTION: There is increasing interest in Artificial Intelligence (AI) and its application to medicine. Perceptions of AI are less well-known, notably amongst children and young people (CYP). This workshop investigates attitudes towards AI and its future applications in medicine and healthcare at a specialised paediatric hospital using practical design scenarios. METHOD: Twenty-one members of a Young Persons Advisory Group for research contributed to an engagement workshop to ascertain potential opportunities, apprehensions, and priorities. RESULTS: When presented as a selection of practical design scenarios, we found that CYP were more open to some applications of AI in healthcare than others. Human-centeredness, governance and trust emerged as early themes, with empathy and safety considered as important when introducing AI to healthcare. Educational workshops with practical examples using AI to help, but not replace humans were suggested to address issues, build trust, and effectively communicate about AI. CONCLUSION: Whilst policy guidelines acknowledge the need to include children and young people to develop AI, this requires an enabling environment for human-centred AI involving children and young people with lived experiences of healthcare. Future research should focus on building consensus on enablers for an intelligent healthcare system designed for the next generation, which fundamentally, allows co-creation. IMPACT: Children and young people (CYP) want to be included to share their insights about the development of research on the potential role of Artificial Intelligence (AI) in medicine and healthcare and are more open to some applications of AI than others. Whilst it is acknowledged that a research gap on involving and engaging CYP in developing AI policies exists, there is little in the way of pragmatic and practical guidance for healthcare staff on this topic. This requires research on enabling environments for ongoing digital cooperation to identify and prioritise unmet needs in the application and development of AI

Maternal serum concentrations of pregnancy associated placental protein A and pregnancy specific β-1-glycoprotein in multifetal pregnancies before and after fetal reduction

Placental function in multifetal pregnancies before and after embryo reduction was investigated by measuring maternal serum concentrations of pregnancy associated placental protein-A (PAPP-A) and pregnancy specific β-1-glycoprotein (SP-1). Three groups of pregnant women were studied following assisted reproduction; groups 1 and 2, were 12 singleton and 12 twin pregnancies respectively, and group 3 comprised 12 women with multifetal pregnancies undergoing embryo reduction. PAPP-A and SP-1 were measured serially at 8-21 weeks gestation. In all pregnancies, maternal serum PAPP-A and SP-1 increased with gestation. In twin pregnancies the mean concentrations of SP-1 were significantly higher than in singletons at all gestations, whereas for PAPP-A, concentrations were similar between these groups. In multifetal pregnancies before embryo reduction, the serum concentrations of both proteins were significantly higher than in twin pregnancies. Following reduction, the concentrations of PAPP-A remained significantly higher than for twins throughout, whereas the concentrations of SP-1 gradually converged towards those of twins; by 19 weeks there was no difference between the means of the two groups. These findings suggest that circulating concentrations of SP-1 reflect total placenta mass, which is proportional to the number of live fetuses, whereas the pattern of PAPP-A changes suggests that this protein is produced by the placenta, decidua and other tissue

Reconstruction of fetal and infant anatomy using rapid prototyping of post-mortem MR images

OBJECTIVES: The recent decline in autopsy rates and lack of human anatomical material donated for research and training has resulted in issues for medical training in the United Kingdom. This study aims to examine the feasibility of making accurate three-dimensional (3D) models of the human body and visceral organs using post-mortem magnetic resonance (MR) imaging and rapid prototyping. METHODS: We performed post-mortem MR imaging using a 3D T2-weighted sequence in 11 fetuses and infants, before autopsy, using either a 1.5-T or 9.4-T MR scanner. Internal organs were reconstructed in silico and 3D models were created by rapid prototyping. RESULTS: The median gestation of fetuses was 20 (range 19-30) weeks and the median age of infants was 12 (range 8-16) weeks. Models created by rapid prototyping accurately depicted structural abnormalities and allowed clear visualisation of 3D relationships. CONCLUSIONS: Accurate 3D modelling of anatomical features from post-mortem imaging in fetuses and infants is feasible. These models could have a large number of medical applications, including improved parental counselling, invaluable teaching resources and significant medico-legal applications to demonstrate disease or injury, without the need to show actual autopsy photographs

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

BACKGROUND: Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers. MATERIALS AND METHODS: MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated. RESULTS: Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant. CONCLUSIONS: Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools

Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice

Machine learning encompasses statistical approaches such as logistic regression (LR) through to more computationally complex models such as neural networks (NN). The aim of this study is to review current published evidence for performance from studies directly comparing logistic regression, and neural network classification approaches in medicine. A literature review was carried out to identify primary research studies which provided information regarding comparative area under the curve (AUC) values for the overall performance of both LR and NN for a defined clinical healthcare-related problem. Following an initial search, articles were reviewed to remove those that did not meet the criteria and performance metrics were extracted from the included articles. Teh initial search revealed 114 articles; 21 studies were included in the study. In 13/21 (62%) of cases, NN had a greater AUC compared to LR, but in most the difference was small and unlikely to be of clinical significance; (unweighted mean difference in AUC 0.03 (95% CI 0-0.06) in favour of NN versus LR. In the majority of cases examined across a range of clinical settings, LR models provide reasonable performance that is only marginally improved using more complex methods such as NN. In many circumstances, the use of a relatively simple LR model is likely to be adequate for real-world needs but in specific circumstances in which large amounts of data are available, and where even small increases in performance would provide significant management value, the application of advanced analytic tools such as NNs may be indicated