Characterizing sleep disorders in an autism-specific collection of electronic health records

Stress in autism (STREAM): A study protocol on the role of circadian activity, sleep quality and sensory reactivity

Mental health issues are markedly increased in individuals with autism, making it the number one research priority by stakeholders. There is a crucial need to use personalized approaches to understand the underpinnings of mental illness in autism and consequently, to address individual needs. Based on the risk factors identified in typical mental research, we propose the following themes central to mental health issues in autism: sleep difficulties and stress. Indeed, the prevalence of manifold circadian disruptions and sleep difficulties in autism, alongside stress related to sensory overload, forms an integral part of autistic symptomatology. This proof-of-concept study protocol outlines an innovative, individualised approach towards investigating the interrelationships between stress indices, sleep and circadian activation patterns, and sensory sensitivity in autism. Embracing an individualized methodology, we aim to collect 14 days of data per participant from 20 individuals with autism diagnoses and 20 without. Participants' sleep will be monitored using wearable EEG headbands and a sleep diary. Diurnal tracking of heart rate and electrodermal activity through wearables will serve as proxies of stress. Those objective data will be synchronized with subjective experience traces collected throughout the day using the Temporal Experience Tracing (TET) method. TET facilitates the quantification of relevant aspects of individual experience states, such as stress or sensory sensitivities, by providing a continuous multidimensional description of subjective experiences. Capturing the dynamics of subjective experiences phase-locked to neural and physiological proxies both between and within individuals, this approach has the potential to contribute to our understanding of critical issues in autism, including sleep problems, sensory reactivity and stress. The planned strives to provide a pathway towards developing a more nuanced and individualized approach to addressing mental health in autism.

Assessment of autonomic symptom scales in patients with neurodevelopmental diagnoses using electronic health record data

Background

Sleep disturbances, gastrointestinal problems, and atypical heart rate are commonly observed in patients with autism spectrum disorder (ASD) and may relate to underlying function of the autonomic nervous system (ANS). The overall objective of the current study was to quantitatively characterize features of ANS function using symptom scales and available electronic health record (EHR) data in a clinically and genetically characterized pediatric cohort.

Methods

We assessed features of ANS function via chart review of patient records adapted from items drawn from a clinical research questionnaire of autonomic symptoms. This procedure coded for the presence and/or absence of targeted symptoms and was completed in 3 groups of patients, including patients with a clinical neurodevelopmental diagnosis and identified genetic etiology (NPD, n=244), those with an ASD diagnosis with no known genetic cause (ASD, n=159), and age and sex matched controls (MC, n=213). Symptoms were assessed across four main categories: (1) Mood, Behavior, and Emotion; (2) Secretomotor, Sensory Integration; (3) Urinary, Gastrointestinal, and Digestion; and (4) Circulation, Thermoregulation, Circadian function, and Sleep/Wake cycles.

Results

Chart review scores indicate an increased rate of autonomic symptoms across all four sections in our NPD group as compared to scores with ASD and/or MC. Additionally, we note several significant relationships between individual differences in autonomic symptoms and quantitative ASD traits.

Conclusion

These results highlight EHR review as a potentially useful method for quantifying variance in symptoms adapted from a questionnaire or survey. Further, using this method indicates that autonomic features are more prevalent in children with genetic disorders conferring risk for ASD and other neurodevelopmental diagnoses.

Steganography for medical record image

Medical record images in EHR system are users' privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.

A practical approach to identifying autistic adults within the electronic health record

The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism.

Investigating the genetic pathways of insomnia in Autism Spectrum Disorder

BACKGROUND

Sleep problems are common in children with autism spectrum disorder (autism). There is sparse research to date to examine whether insomnia in people with autism is related to autism genetics or insomnia genetics. Moreover, there is a lack of research examining whether circadian-rhythm related genes share potential pathways with autism.

AIMS

To address this research gap, we tested whether polygenic scores of insomnia or autism are related to risk of insomnia in people with autism, and whether the circadian genes are associated with insomnia in people with autism.

METHODS AND PROCEDURES

We tested these questions using the phenotypically and genotypically rich MSSNG dataset (N = 1049) as well as incorporating in the analyses data from the Vanderbilt University Biobank (BioVU) (N = 349).

OUTCOMES AND RESULTS

In our meta-analyzed sample, there was no evidence of associations between the polygenic scores (PGS) for insomnia and a clinical diagnosis of insomnia, or between the PGS of autism and insomnia. We also did not find evidence of a greater burden of rare and disruptive variation in the melatonin and circadian genes in individuals with autism and insomnia compared to individuals with autism without insomnia.

CONCLUSIONS AND IMPLICATIONS

Overall, we did not find evidence for strong effects of genetic scores influencing sleep in people with autism, however, we cannot rule out the possibility that smaller genetic effects may play a role in sleep problems. Our study indicated the need for a larger collection of data on sleep problems and sleep quality among people with autism.