Incorporating informatively collected laboratory data from EHR in clinical prediction models.

Background: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need.

RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.

Purpose: To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients.

Methods: A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT.

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.

Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection.

Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies.

Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques.

A Disease Identification Algorithm for Medical Crowdfunding Campaigns: Validation Study.

Background: Web-based crowdfunding has become a popular method to raise money for medical expenses, and there is growing research interest in this topic. However, crowdfunding data are largely composed of unstructured text, thereby posing many challenges for researchers hoping to answer questions about specific medical conditions. Previous studies have used methods that either failed to address major challenges or were poorly scalable to large sample sizes.

Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation.

Background: Viewing their habitual smoking environments increases smokers' craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers' daily environments.

Objective: In this study, we aim to predict environment-associated risk from continuously acquired images of smokers' daily environments.

Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents.

Study Objectives: Caffeine use is ubiquitous among adolescents and may be harmful to sleep, with downstream implications for health and development. Research has been limited by self-reported and/or aggregated measures of sleep and caffeine collected at a single time point. This study examines bidirectional associations between daily caffeine consumption and electroencephalogram-measured sleep among adolescents and explores whether these relationships depend on timing of caffeine use.

Regularity and Timing of Sleep Patterns and Behavioral Health Among Adolescents.

Objective: Sleep is vital to supporting adolescent behavioral health and functioning; however, sleep disturbances remain under-recognized and undertreated in many health care settings. One barrier is the complexity of sleep, which makes it difficult for providers to determine which aspects-beyond sleep duration-may be most important to assess and treat to support adolescent health. This study examined associations between 2 sleep indices (regularity and timing) and adolescent behavioral health and functioning over and above the impact of shortened/fragmented sleep.

Review of Popularity and Quality Standards of Opioid-Related Smartphone Apps.

Purpose Of Review: Opioid misuse, addiction, and related harm is a global crisis that affects public health and social and economic welfare. Many of the strategies being used to combat the opioid crisis could benefit from improved access and dissemination, such as that afforded by smartphone apps. The goal of this study was to characterize the purpose, audience, quality and popularity of opioid-related smartphone apps.

Identifying neural signatures of tobacco retail outlet exposure: Preliminary validation of a "community neuroscience" paradigm.

An extensive epidemiological literature indicates that increased exposure to tobacco retail outlets (TROs) places never smokers at greater risk for smoking uptake and current smokers at greater risk for increased consumption and smoking relapse. Yet research into the mechanisms underlying this effect has been limited. This preliminary study represents the first effort to examine the neurobiological consequences of exposure to personally relevant TROs among both smokers (n = 17) and nonsmokers (n = 17).

Health system utilization before age 1 among children later diagnosed with autism or ADHD.

Children with autism spectrum disorder (ASD) or attention deficit hyperactivity disorder (ADHD) have 2-3 times increased healthcare utilization and annual costs once diagnosed, but little is known about their utilization patterns early in life. Quantifying their early health system utilization could uncover condition-specific health trajectories to facilitate earlier detection and intervention. Patients born 10/1/2006-10/1/2016 with ≥ 2 well-child visits within the Duke University Health System before age 1 were grouped as ASD, ADHD, ASD + ADHD, or No Diagnosis using retrospective billing codes.

Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study.

Background: Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group.

Digital envirotyping: quantifying environmental determinants of health and behavior.

Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver context-appropriate interventions. The coupling of improved smart eyewear with deep learning represents a technological turning point, one that calls for more comprehensive, ambitious study of environments and health.

Author Correction: Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Sleep/Wake Regularity Associated with Default Mode Network Structure among Healthy Adolescents and Young Adults.

Sleep deprivation and disorders are linked to reduced DMN connectivity. Less is known about how naturalistic sleep patterns - specifically sleep irregularity - relate to the DMN, particularly among adolescents and young adults. Additionally, no studies have utilized graph theory analysis to clarify whether sleep-related decreases in connectivity reflect global or local DMN changes.

The Many Channels of Screen Media Technology in ADHD: a Paradigm for Quantifying Distinct Risks and Potential Benefits.

Purpose Of Review: Individuals with attention-deficit hyperactivity disorder (ADHD) may be unusually sensitive to screen media technology (SMT), from television to mobile devices. Although an association between ADHD and SMT use has been confirmed, its importance is uncertain partly due to variability in the way SMT has been conceptualized and measured. Here, we identify distinct, quantifiable dimensions of SMT use and review possible links to ADHD to facilitate more precise, reproducible investigation.

Identifying Smoking Environments From Images of Daily Life With Deep Learning.

Importance: Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions.

Objective: To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life.

Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk.

Sleep disturbances, including insufficient sleep duration and circadian misalignment, confer risk for cardiometabolic disease. Less is known about the association between the regularity of sleep/wake schedules and cardiometabolic risk. This study evaluated the external validity of a new metric, the Sleep Regularity Index (SRI), among older adults (n = 1978; mean age 68.

Real-world walking in multiple sclerosis: Separating capacity from behavior.

Background: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors.

Objective: To develop specific measures of MS-associated walking capacity through statistically sophisticated HPA analysis, thereby more precisely defining the real-world impact of disease.

Methods: Eighty-eight MS and 38 control subjects completed timed walks and patient-reported outcomes in clinic, then wore an accelerometer for 7days.

Remotely engaged: Lessons from remote monitoring in multiple sclerosis.

Objectives: Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being.

Methods: Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months.

Fatigue and fluid hydration status in multiple sclerosis: A hypothesis.

Background: Fatigue is a prevalent and functionally disabling symptom for individuals living with multiple sclerosis (MS) which is poorly understood and multifactorial in etiology. Bladder dysfunction is another common MS symptom which limits social engagement and quality of life. To manage bladder issues, individuals with MS tend to limit their fluid intake, which may contribute to a low-hydration (LoH) state and fatigue.

Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects.

Background: The six-minute walk (6MW) is a common walking outcome in multiple sclerosis (MS) thought to measure fatigability in addition to overall walking disability. However, direct evidence of 6MW induced gait deterioration is limited by the difficulty of measuring qualitative changes in walking.

Objectives: This study aims to (1) define and validate a measure of fatigue-related gait deterioration based on data from body-worn sensors; and (2) use this measure to detect gait deterioration induced by the 6MW.