593 Odds of Receiving Sleep Diagnoses in Common Pediatric Chronic Medical Conditions

Introduction Sleep disorders (SD) are under-diagnosed in the general pediatric population. Children with chronic medical conditions (CMC) tend to have a higher prevalence rate of SD; however, the studies about the rates of diagnosis of SD are limited. We examined the odds of receiving SD diagnoses in children with various CMC and hypothesized that if this likelihood is established, screening tools can be developed to increase the rates of diagnoses and improve clinical outcomes. Methods Chi-square test and regression analysis were used to test the association between SD and CMC based on ICD-9 and -10 diagnosis codes retrieved from the Medicaid claims submitted for children (0 to 18 years) enrolled in the Centers for Medicare and Medicaid Services-funded Coordinated Health Care for Complex Kids (CHECK) project, at an urban, public tertiary care hospital. Children without a CMC were excluded. Results Among 16,609 children with CMC (mean [SD] age of 9.1 [5.3] years; 56.4% male; 77% with multiple CMC), 14.1% received a diagnosis of SD. Compared to the cohort without a particular CMC, children with attention deficit hyperactivity disorder (ADHD), obesity, developmental disorder, or asthma had following odds of receiving sleep diagnosis respectively (odds ratio (OR) [Confidence Interval]): insomnia (6.9 [5–9.5]; 2.4 [1.7–3.2]; 1.6 [1.2–2.3]; 0.7 [0.5–1]), circadian rhythm disorders (6.1[2.7–13.7]; 2.8 [1.3–2.6]; 3.0 [1.4–6.4]; 0.5* [0.2–1.1]), hypersomnia (2.9 [1.0–8.6]; 7.8 [3–20.2], 0.9* [0.3–2.6], 0.8* [0.3–2.2]), and sleep-related movement disorder (1.9* [0.9–4.3]; 4.4 [2.5–7.8]; 2.2 [1.2–3.9]; 0.6*[0.3–1.1]). (*=p value <0.05). Conclusion Odds of receiving sleep diagnoses vary across CMC. Among others, strong associations between obesity-hypersomnia and ADHD-insomnia were noted. This information may help clinicians implement appropriate screening interventions to improve early SD detection and management. Further studies to examine these associations are necessary. Support (if any):

0994 Assessment Of Sleep-wake And Circadian Rhythm Disruption In Children And Adolescents Diagnosed With Craniopharyngioma

Introduction Patients with craniopharyngioma are at increased risk for hypersomnia/narcolepsy and circadian rhythm disruption, secondary to hypothalamic-pituitary involvement of the tumor. We assessed youth with craniopharyngioma to determine presence of the dim light melatonin onset (DLMO) and concurrent sleep disturbance. Methods Fifty-two patients (7-21 years; 51% female) enrolled on our institutional protocol for craniopharyngioma that included surgery, proton therapy, or both. In-home salivary melatonin was collected after surgery and hourly beginning 3 h before to 1 h after habitual bedtime to determine the DLMO, which was defined as the time that melatonin exceeded a 4 pg/mL threshold. Polysomnography and a next day multiple sleep latency test (MSLT) were also conducted. Results Hypersomnia/narcolepsy was indicated in 86% of patients. DLMO was detected in 29 (56%) patients and averaged 21:04 (±1:14). All but 2 patients with a DLMO had a concurrent sleep diagnosis (18 hypersomnia, 8 narcolepsy, 1 insomnia). In those we could not compute a DLMO, melatonin was above the 4 pg/mL threshold in 19 (37%), suggesting that the DLMO was likely earlier than the sampling window. Two (4%) did not reach threshold, suggesting that the DLMO was later than the window. For patients in which DLMO was not computed, all but 4 had a concurrent sleep diagnosis (7 hypersomnia, 9 narcolepsy, 1 MSLT not completed). Three (6%) participants showed a pattern of melatonin decreasing before bedtime (2 hypersomnia, 1 narcolepsy). Sleep disorder diagnosis was not associated with whether a DLMO was detected or not. Conclusion DLMO did not occur within the sampling window in 44% of patients with the majority due to the DLMO likely occurring before sampling started. Simultaneous assessment of both sleep-wake disturbance and circadian phase could provide more informed sleep interventions for excessive sleepiness and circadian misalignment in this patient population. Support This study was supported by cancer center grant (CA21765) from the National Cancer Institute, and ALSAC.

Sleep Apnea Identification using Ecg and Ppg Signals Involving Neural Network

Sleep apnea is one of the hypothetically severe sleep disorders that often stops and begins to breathe. The undiagnosed sleep apnea can be very serious, resulting in fast decreases in blood oxygen levels, during which developed insulin resistance and type 2 diabetes may increase. Several people do not know their condition, though. Typical for sleep diagnosis is an overnight polysomnography (PSG) in a dedicated sleep laboratory. Since these exams are expensive and beds are restricted due to the need for trained employees to evaluate the full. An automatic detection technique would allow faster diagnosis and more patients to be analyzed. Hence detection of sleep apnea is compulsory so that it could be treated. This study established an algorithm that signaled a short-term electrocardiographic event extraction (ECG) and combined neural network methodologies for automatic sleep apnea detection. This study provides users with visual experiences through visual parameters such as HRV measurements, Poincare plot, global and local return map. This enables the doctor evaluate whether or not the individual is suffering from sleep apnea.

Shooting a high-density electroencephalographic picture on sleep in children with attention-deficit/hyperactivity disorder

Study Objectives Sleep-related slow-wave activity (SWA) has been recognized as a marker of synaptic plasticity. In children affected by attention deficit hyperactivity disorder (ADHD), SWA is mainly located in the central rather than frontal regions, reflecting a maturational delay. A detailed subjective and objective sleep investigation, including a full night video-polysomnography (PSG-HD-EEG), was performed on 30 consecutive drug naïve outpatients with a diagnosis of ADHD. They received a diagnosis of sleep disorders in 29/30 cases, and most of them had a past history of sleep problems. They had a higher apnea–hypopnea index at PSG, and slept less than 9 hr at actigraphy. We aimed to describe the SWA behavior in the same group of children with ADHD. Materials and Methods The full-night PSG-HD EEG of children with ADHD was compared with the one of the 25 healthy controls. The scalp SWA mapping, the decrease of SWA during the night, and the EEG source of SWA were analyzed. Results At scalp topography, the focus of SWA was observed over the centro–parietal–occipital regions in participants with ADHD (p < 0.01), which remained significant in the subgroups divided between subgroups according to the sleep diagnosis (p < 0.01). The physiological decrease in SWA was more evident in control participants. The source analysis revealed a greater delta power over the posterior cingulate in participants with ADHD (p < 0.01). Conclusions Our results confirm static and dynamic changes in SWA behavior in children with ADHD, which may reflect a maturational delay occurring at a vulnerable age, as a consequence of chronic sleep deprivation.

Patient reported outcomes and predictors of distress.

6559 Background: ASCO/NCCN guidelines recommend screening for multifactorial distress in cancer patients. Understanding predictors of cancer related distress can lead to early intervention and improve clinical outcomes, symptom management, and operational efficiency. Through electronic distress screening (EDS), patient reported outcomes (PRO) were collected across 42 practice locations. Methods: EDS has 39 questions related to cancer related distress including: distress, cancer symptoms/side effects, malnutrition, depression, anxiety, social/family support, financial, and spiritual concerns. 27,106 patients completed screens between 2017-2018. Multivariate analysis and logistic regressions determined predictors of distress for completed screens overall, registry matched, and within 60 days of diagnosis. Results: Median age was 59 (IQR 18-101) and 65% were female. Five symptoms consistently predicted clinically significant distress ≥ 4: anxiety, fatigue, pain, poor emotional coping, and sleep. Diagnosis (dx), staging at time of dx, and timing of screen did not independently predict distress. Factors predicting clinically significant distress varied across geographic regions. Conclusions: In large patient population, five key PROs are predictive of clinically significant distress and could potentially impact clinical outcomes. Early PROs predictive of distress were consistent along the continuum, thus the importance of early symptom identification. EDS can help custom tailor supportive oncology programs to mitigate symptoms related to cancer distress. [Table: see text]