Accumulating evidence suggests that corpus callosum development is critically involved in the emergence of behavioral and cognitive skills during the first two years of life and that structural abnormalities of the corpus callosum are associated with a variety of neurodevelopmental disorders. Indeed by adulthood ∼30% of individuals with agenesis of the corpus callosum (ACC), a congenital condition resulting in a partial or fully absent corpus callosum, exhibit phenotypic features consistent with autism spectrum disorder (ASD). However, very little is known about developmental similarities and/or differences among infants with ACC and infants who develop ASD. This study describes temperament in infants with ACC during the first year of life in comparison with a neurotypical control group. Additionally, it examines the potential contribution of disrupted callosal connectivity to early expression of temperament in ASD through comparison to children with high familial likelihood of ASD.

Longitudinal ratings of positive and negative emotionality were acquired at 6 and 12 months on the Infant Behavior Questionnaire-Revised across four groups of infants: isolated complete and partial ACC (n=104), high familial likelihood of ASD who do and do not have a confirmed ASD diagnosis (HL+ n=81, HL- n=282), and low-likelihood controls (LL- n=152).

Overall, the ACC group demonstrated blunted affect, with significantly lower positive and negative emotionality than LL controls at both timepoints. Specifically, the ACC group exhibited lower activity and approach dimensions of positive emotionality at both timepoints, with lower high-intensity pleasure at 6 months and lower vocal reactivity at 12 months. On negative emotionality subscales, the ACC group exhibited lower distress to limitations and sadness at both timepoints, as well as lower falling reactivity at 6 months. The ACC and HL groups did not differ significantly on positive emotionality at either timepoint. However, negative emotionality was lower in the ACC group than the HL- group at both timepoints and lower than the HL+ group at 12 months, with lower distress to limitations and sadness ratings than both HL groups at both timepoints.

These findings highlight the importance of interhemispheric connections in facilitating active engagement and pursuit of pleasurable activities during the first year of life, as well as expression of sadness and distress to limitations. Notably, similarities between infants with ACC and infants at elevated familial risk of ASD suggest that disrupted callosal connectivity may specifically contribute to reductions in positive emotionality.

It is unclear how agenesis of the corpus callosum (ACC), a congenital brain malformation defined by complete or partial absence of the corpus callosum, impacts language development. fMRI studies of middle childhood suggest that the corpus callosum plays a role in the interhemispheric language network (Bartha-Doering et al., 2020), and that reduced interhemispheric functional connectivity is correlated with worse language abilities in children with ACC (Bartha-Doering et al., 2021). Additionally, accumulating evidence suggests structural abnormalities of the corpus callosum play a role in neurodevelopmental disorders. While children who go on to receive an autism spectrum disorder (ASD) diagnosis may show early signs of altered word and gesture acquisition (Iverson et al., 2018), the same is not known about ACC. This study examined language development during the second year of life in children with ACC in comparison to neurotypical control participants, as well as other children at elevated risk of ASD.

The MacArthur-Bates Communicative Development Inventories (MCDI): Words and Gestures scales were administered to parents of 74 children with isolated ACC at 12, 18 and 24 months of age. Children whose first language was not English and children who were bilingual were excluded. Comparison groups consisted of individuals with a low familial likelihood of ASD (LL- n=140) and individuals with high familial likelihood of ASD who do and do not have a confirmed ASD diagnosis (HL+ n=68, HL- n=256).

Compared to LL controls, the ACC group produced fewer words at 18 and 24 months of age, and demonstrated fewer words understood at all three timepoints. Similarly, compared to the HL- group, the ACC group demonstrated fewer words produced and understood at 18 months of age, and fewer words produced at 24 months of age. The ACC and HL+ groups did not differ in words produced or words understood at any timepoint.

Overall, infants with ACC demonstrated delayed vocabulary expansion from 12 to 24 months of age. These findings illustrate the role of callosal connectivity in the development of language across the first 2 years of life, and highlight the need for support and interventions that target vocabulary production and comprehension.

Differences in adaptive functioning present early in development for many children with monogenic (Down Syndrome, Fragile X) and neurodevelopmental disorders. At this time, it is unclear whether children with ACC present with early adaptive delays, or if difficulties emerge later as functional tasks become more complex. While potential delays in motor development are frequently reported, other domains such as communication, social and daily living skills are rarely described. We used a prospective, longitudinal design to examine adaptive behavior from 6-24 months in children with ACC and compared their trajectories to those with monogenic and neurodevelopmental conditions.

Our sample included children with primary ACC (n= 27-47 depending on time point) whose caregivers completed the Vineland Adaptive Behavior Scales-Interview 3rd Edition, via phone at 6, 12, 18 and 24 months. Comparison samples (using the Vineland-2) included children with Down Syndrome (DS; n = 15-56), Fragile X (FX; n = 15-20), children at high familial likelihood for autism (HL-; n=192-280), and low likelihood (LL; no family history of autism and no developmental/behavioral diagnosis; n = 111196). A subset of the HL children received an autism diagnosis (HL+; n = 48-74). The DS group did not have an 18-month Vineland.

A series of linear mixed model analyses (using maximum likelihood) for repeated measures was used to compare groups on three Vineland domains at 6, 12, 18 and 24 month timepoints). All fixed factors (diagnostic group, timepoint, and group X timepoint interaction) accounted for significant variance on all Vineland domains (p < .001). Post hoc comparisons with Bonferroni-correction examined ACC Vineland scores compared to the other diagnostic groups at each timepoint. At 6 months, parent-ratings indicated the ACC group had significantly weaker skills than the LL group in Communication and Motor domains. At 12, 18 and 24 months, ratings revealed weaker Communication, Daily Living and Motor skills in the ACC group compared to both the LL and HL- groups. Compared to the other clinical groups, the ACC group had stronger Socialization and Motor skills than Fragile X at 6 months, and at 24 months had stronger Communication and Socialization skills than both the DS and FX groups, as well as stronger Socialization than the HL+ group.

Compared to children with low likelihood of ASD, children with primary ACC reportedly have weaker Communication and Motor skills from 6 to 24 months, with weakness in Daily Living Skills appearing at 12 months and all differences increase with age. Compared to Fragile X, the ACC exhibited relative strengths in socialization and motor skills starting at 6 months. By 24 months, the ACC group was outperforming the monogenic groups on Socialization and Communication. In general, the ACC scores were consistent with the HL+ sample, except the ACC group had stronger Social skills at 18 and 24 months. The results clearly inform the need for early intervention in the domains of motor and language skills. Additionally, as we know that children with ACC are at increased risk for social difficulties, research is needed both using more fine-grained social-communication tools, and following children from infancy through middle childhood.

Recent reviews have shown that many real-world evidence (RWE) studies suffer from avoidable methodological flaws. Meanwhile, the National Institute for Health and Care Excellence (NICE) is seeing an increase in RWE submissions in Health Technology Appraisals and is keen to support the use of this evidence. However, limited guidance exists for the development and assessment of RWE, risking both missed opportunities for unbiased evidence generation and inconsistent decision making based on that evidence. As part of its RWE framework, NICE has developed methods guidance to provide clear expectations for the conduct and reporting of non-randomized comparative effects studies using real world data.

A conceptual model and draft framework were developed based on established international best practices in RWE and observational research. This was refined with focused literature searches, for example, on the use of external control arm studies. We then engaged with external stakeholders to incorporate their feedback and develop case studies. A reporting template was developed and tested on multiple use cases.

The guidance stresses the central importance of a target trial approach to study design, e.g., adopting an active comparator, new user design, where possible. Target trial emulation is a useful tool to improve the quality and transparency of RWE studies, helping to overcome selection and confounding biases. Various other study design and analytical approaches are outlined for addressing confounding bias and biases due to missing data, measurement error, or misclassification, which are common challenges in RWE. Alongside traditional approaches to sensitivity analysis, the framework promotes quantitative bias analyses which includes a range of methods to assess and communicate the potential impact of remaining bias to study findings by quantifying the direction, magnitude, and uncertainty of bias. A reporting template, based on common methodological pitfalls, is provided to help evidence developers consider key areas of bias in their work and to inform reviewers of any approaches used to investigate or resolve these.

The National Institute for Health and Care Excellence (NICE) is determined to make better use of health and social care data in the development of its guidance. Real-world data (RWD) has the potential to significantly improve our understanding of the value of new and existing health and social care interventions. RWD is already widely used to characterize populations, interventions, and outcomes and to populate economic models, but its use in estimating the effects of interventions remains limited, especially for medicines. Key barriers to its greater use in this context include limited transparency around studies, sometimes a lack of confidence in their integrity, and methodological concerns around how studies have addressed major sources of potential bias.

This abstract focuses on the real-world evidence (RWE) framework developed by NICE to support its ambitions. The framework was developed in an iterative way based on: reviews of best practice approaches to the conduct and assessment of real-world evidence studies; case studies; and workshops with key external stakeholders. The initial version of this living framework focuses on de novo RWE studies using individual patient data.

The RWE framework consists of an overarching research governance framework which describes expectations around the planning, conduct, and reporting of RWE studies across uses of real-world data. Uses are categorized by risk according to their importance to decision-making, the impact of decisions on patient and system outcomes, and their complexity as proxied by risk of bias. Studies of the effects of interventions on patient health and system outcomes are considered the highest risk. The research governance framework is supported by a tool to aid assessment of data suitability for its intended application, and detailed guidance on the conduct and reporting of comparative effect studies using RWD, following the target trial approach.

The RWE Framework underpins NICE’s ambitions to make better use of RWD in its guidance and is intended to improve the quality and utility of RWE studies submitted to NICE enabling more consistent and appropriate evaluation.

The National Institute for Health and Care Excellence (NICE) intends to increasingly use real-world evidence in developing guidance. To increase trust in such evidence, NICE has developed a framework for developing and assessing real-world evidence studies, including understanding the value of the selected data source for the decision problem.

Starting with published high-quality studies about data quality, we developed a conceptual model of the elements needed to understand the quality of a data source. Results from a literature search were then mapped to the model. We used this to design a structured reporting tool, the data Suitability Assessment Tool (dataSAT), and tested it in several cases studies. Additionally, we engaged with internal and external stakeholders to obtain feedback on the tool and revised it accordingly.

DataSAT covers provenance of the data, assessment of data quality, and the data’s relevance to the research question. For data provenance, information is requested about the data source independent of the study’s interests, including the purpose, setting, dates of operation, funding, data specification, and management and quality assurance plans for the data sources. Data quality is covered by quantitively assessing the completeness and accuracy of the following key study elements to inform critical appraisal of the study: population inclusion and exclusion criteria; intervention; comparator; and outcomes and key covariates. The findings on data sources and data quality are then interpreted in terms of relevance to the decision problem. This includes relevance to the population in the United Kingdom, the treatment pathway and care setting, the availability of key study elements, time-related factors such as length of follow up, and the effects of sample size and missing data on the validity of findings.

DataSAT allows summary information on source data, including quality and relevance, to be reported in a structured manner, enabling decision makers to better understand how the data influence the robustness of analyses used in health technology assessment. This helps increase trust in the use of real-world evidence.

Basic symptoms, defined as subjectively perceived disturbances in thought, perception and other essential mental processes, have been established as a predictor of psychotic disorders. However, the relationship between basic symptoms and family history of a transdiagnostic range of severe mental illness, including major depressive disorder, bipolar disorder and schizophrenia, has not been examined.

We sought to test whether non-severe mood disorders and severe mood and psychotic disorders in parents is associated with increased basic symptoms in their biological offspring.

We measured basic symptoms using the Schizophrenia Proneness Instrument – Child and Youth Version in 332 youth aged 8–26 years, including 93 offspring of control parents, 92 offspring of a parent with non-severe mood disorders, and 147 offspring of a parent with severe mood and psychotic disorders. We tested the relationships between parent mental illness and offspring basic symptoms in mixed-effects linear regression models.

Offspring of a parent with severe mood and psychotic disorders (B = 0.69, 95% CI 0.22–1.16, P = 0.004) or illness with psychotic features (B = 0.68, 95% CI 0.09–1.27, P = 0.023) had significantly higher basic symptom scores than control offspring. Offspring of a parent with non-severe mood disorders reported intermediate levels of basic symptoms, that did not significantly differ from control offspring.

Basic symptoms during childhood are a marker of familial risk of psychopathology that is related to severity and is not specific to psychotic illness.

None.