829 Chronic sleep deprivation in a 75-year-old female leading to micro-sleeps with atonia causing increased falls

Abstract Introduction Each year, 3 million older people are treated in emergency departments for fall-related injuries. These falls can lead to serious injuries and expensive health care costs. Some have looked into the relationship between chronic sleep disturbances and falls linking chronic sleep deprivation or excessive sleep to falling. Here, we present a unique case of chronic sleep deprivation causing sleep attacks or micro-sleeps with atonia causing falls. Report of case(s) We present a 75-year-old F with a history of increased daily falls up to 4x per day that began 3 years ago. She denies any triggering events, auras, frequent tripping, loss of balance or weakness. Reportedly, she will be walking along then suddenly falls. She is aware that she is falling yet feels as though she cannot prevent/stop the fall or break her fall. No one has witnessed her fall, but is frequently found lying on the floor. She has never sustained a serious injury as a result of her falls. She has an 8 year history of restless legs syndrome treated with ropinirole and a 10 year history of obstructive sleep apnea (OSA) treated with CPAP. Her general bedtime is 2200 and wake-time is 0400 with an average 3–4 hrs of quality sleep per night for many years. She endorses severe daytime hypersomnolence and chronic hypoxemia on 3L home oxygen. We hypothesized her falls were secondary to sleep attacks or micro-sleeps where she enters REM sleep and develops atonia. Nocturnal sleep study followed by MSLT showed severe OSA with severe, persistent daytime sleeping with REM sleep and atonia. She had a mean sleep latency of 2 minutes with 1 sleep-onset REM period. We started NIPPV with supplemental oxygen treatment, and within 4 months her daytime hypersomnolence resolved, exercise intolerance improved, saturations improved to 89–90% on room air, and has <1 fall per day. Conclusion Here, we presented a unique case of a 75 yo F with recurrent falls secondary to chronic sleep deprivation causing micro-sleeps involving REM sleep and atonia. She was treated with NIPPV which improved her oxygenation and reduced her number of falls to <1 per day. Support (if any):

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Chronic sleep deprivation alters theta and gamma powers during REM sleep in mice

Sleep Medicine ◽

10.1016/j.sleep.2015.02.1353 ◽

2015 ◽

Vol 16 ◽

pp. S177-S178

Author(s):

B. Kim ◽

Y. Kim ◽

E. Hwang ◽

R. Strecker ◽

R. McCarley ◽

...

Keyword(s):

Sleep Deprivation ◽

Rem Sleep ◽

Chronic Sleep Deprivation ◽

Chronic Sleep

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419 STOP-Bang Score and History of Radiation Predicts Risk of Obstructive Sleep Apnea in Cancer Patients: A Machine Learning Study

SLEEP ◽

10.1093/sleep/zsab072.418 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A166-A166

Author(s):

Ankita Paul ◽

Karen Wong ◽

Anup Das ◽

Diane Lim ◽

Miranda Tan

Keyword(s):

Machine Learning ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cancer Patients ◽

Prediction Accuracy ◽

Screening Tools ◽

Cancer Type ◽

Sleep Study ◽

Obstructive Sleep ◽

History Of

Abstract Introduction Cancer patients are at an increased risk of moderate-to-severe obstructive sleep apnea (OSA). The STOP-Bang score is a commonly used screening questionnaire to assess risk of OSA in the general population. We hypothesize that cancer-relevant features, like radiation therapy (RT), may be used to determine the risk of OSA in cancer patients. Machine learning (ML) with non-parametric regression is applied to increase the prediction accuracy of OSA risk. Methods Ten features namely STOP-Bang score, history of RT to the head/neck/thorax, cancer type, cancer stage, metastasis, hypertension, diabetes, asthma, COPD, and chronic kidney disease were extracted from a database of cancer patients with a sleep study. The ML technique, K-Nearest-Neighbor (KNN), with a range of k values (5 to 20), was chosen because, unlike Logistic Regression (LR), KNN is not presumptive of data distribution and mapping function, and supports non-linear relationships among features. A correlation heatmap was computed to identify features having high correlation with OSA. Principal Component Analysis (PCA) was performed on the correlated features and then KNN was applied on the components to predict the risk of OSA. Receiver Operating Characteristic (ROC) - Area Under Curve (AUC) and Precision-Recall curves were computed to compare and validate performance for different test sets and majority class scenarios. Results In our cohort of 174 cancer patients, the accuracy in determining OSA among cancer patients using STOP-Bang score was 82.3% (LR) and 90.69% (KNN) but reduced to 89.9% in KNN using all 10 features mentioned above. PCA + KNN application using STOP-Bang score and RT as features, increased prediction accuracy to 94.1%. We validated our ML approach using a separate cohort of 20 cancer patients; the accuracies in OSA prediction were 85.57% (LR), 91.1% (KNN), and 92.8% (PCA + KNN). Conclusion STOP-Bang score and history of RT can be useful to predict risk of OSA in cancer patients with the PCA + KNN approach. This ML technique can refine screening tools to improve prediction accuracy of OSA in cancer patients. Larger studies investigating additional features using ML may improve OSA screening accuracy in various populations Support (if any):

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Sleep Deprivation and Sleep-Onset Insomnia are Associated with Blunted Physiological Reactivity to Stressors

Military Medicine ◽

10.1093/milmed/usaa464 ◽

2021 ◽

Vol 186 (Supplement_1) ◽

pp. 246-252

Author(s):

Devon A Hansen ◽

Brieann C Satterfield ◽

Matthew E Layton ◽

Hans P A Van Dongen

Keyword(s):

Sleep Deprivation ◽

Laboratory Study ◽

Salivary Cortisol ◽

Sleep Onset ◽

Deprivation Condition ◽

Increased Risk ◽

Significant Difference ◽

Sleep Deficiency ◽

The Impact ◽

Chronic Sleep

ABSTRACT Introduction Military operations often involve intense exposure to stressors combined with acute sleep deprivation, while military personnel also experience high prevalence of chronic sleep deficiency from insomnia and other sleep disorders. However, the impact of acute and chronic sleep deficiency on physiologic stressor responses is poorly understood. In a controlled laboratory study with normal sleepers and individuals with chronic sleep-onset insomnia, we measured responses to an acute stressor administered in a sleep deprivation condition or a control condition. Methods Twenty-two adults (aged 22-40 years; 16 females)—11 healthy normal sleepers and 11 individuals with sleep-onset insomnia—completed a 5-day (4-night) in-laboratory study. After an adaptation day and a baseline day, subjects were assigned to a 38-hour total sleep deprivation (TSD) condition or a control condition; the study ended with a recovery day. At 8:00 PM after 36 hours awake in the sleep deprivation condition or 12 hours awake in the control condition, subjects underwent a Maastricht Acute Stress Test (MAST). Salivary cortisol was measured immediately before the MAST at 8:00 PM, every 15 minutes after the MAST from 8:15 PM until 9:15 PM, and 30 minutes later at 9:45 PM. Baseline salivary cortisol was collected in the evening of the baseline day. Additionally, before and immediately upon completion of the MAST, self-report ratings of affect and pain were collected. Results The MAST elicited a stressor response in both normal sleepers and individuals with sleep-onset insomnia, regardless of the condition, as evidenced by increases in negative affect and pain ratings. Relative to baseline, cortisol levels increased immediately following the MAST, peaked 30 minutes later, and then gradually returned to pre-MAST levels. At the cortisol peak, there was a significant difference across groups and conditions, reflecting a pronounced blunting of the cortisol response in the normal sleepers in the TSD condition and the sleep-onset insomnia group in both the TSD and control conditions. Conclusions Blunted stressor reactivity as a result of sleep deficiency, whether acute or chronic, may reflect reduced resiliency attributable to allostatic load and may put warfighters at increased risk in high-stakes, rapid response scenarios.

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818 A Ghost from the Past: Unmasked Insomnia Affecting Hypoglossal Nerve Stimulation Therapy Adherence

SLEEP ◽

10.1093/sleep/zsab072.815 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A319-A320

Author(s):

Elena Stuewe ◽

Aarti Grover ◽

Peter Ostrow ◽

Greg Schumaker ◽

Joel Oster ◽

...

Keyword(s):

Nerve Stimulation ◽

Hypoglossal Nerve ◽

Sleep Onset ◽

Chronic Insomnia ◽

Apnea Hypopnea Index ◽

Therapy Adherence ◽

Hypoglossal Nerve Stimulation ◽

Obstructive Sleep ◽

Patient Reported ◽

History Of

Abstract Introduction Hypoglossal nerve stimulation (HNS) is an efficacious option for treating moderate to severe obstructive sleep apnea (OSA). However, there is sparse evidence regarding tolerance and adherence to HNS therapy in patients with a diagnosis of insomnia. Report of case(s) A 57-year-old man with well-controlled depression presented for evaluation for HNS therapy. He had been diagnosed with moderate OSA with an apnea-hypopnea index of 22/hour, intolerant of continuous positive airway pressure and mandibular advancement device. He underwent uvulopalatopharyngoplasty without significant improvement. At the time of initial evaluation, he denied history of insomnia and prior sleep aid use. He subsequently underwent successful HNS device implantation and activation. One week after HNS initiation, the patient reported new symptoms of significant difficulty with sleep onset and inability to fall back asleep, which was worse than his untreated OSA symptoms. Device interrogation did not reveal any hardware problems. Adjustments to start delay, pause time and device configuration with awake endoscopy did not improve tolerance. Subsequently, the patient disclosed a remote history of insomnia, which was treated with multiple hypnotics in addition to cognitive-behavioral therapy for insomnia (CBTi) and had resolved. He was diagnosed with recurrent chronic insomnia, for which eszopiclone was initiated without significant improvement. He eventually agreed to CBTi, with partial improvement in device tolerance and improvement in insomnia symptoms. Conclusion This case highlights that HNS therapy adherence can be affected by prior history of, or a current diagnosis of insomnia. Our patient had a predisposition for insomnia that was well controlled prior to HNS therapy initiation. The onset of recurrent insomnia with HNS activation suggests that HNS was a precipitating factor for his now chronic insomnia. Although there is insufficient evidence to suggest whether history of insomnia should affect the decision to initiate HNS therapy, this case illustrates the importance of screening for insomnia at pre-implant evaluation. Our center is now routinely screening for a history of insomnia to identify patients who may benefit from treatment prior to HNS implantation. Larger studies are needed to explore a possible relationship between insomnia and HNS adherence. Support (if any):

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Gene Networks Underlying Chronic Sleep Deprivation in Drosophila

10.21236/ada610340 ◽

2014 ◽

Author(s):

Ralph J. Greenspan

Keyword(s):

Sleep Deprivation ◽

Gene Networks ◽

Chronic Sleep Deprivation ◽

Chronic Sleep

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Daytime sleepiness and chronic sleep deprivation effects on academic performance among the Sudanese medical students

Journal of Taibah University Medical Sciences ◽

10.1016/j.jtumed.2015.05.003 ◽

2015 ◽

Vol 10 (4) ◽

pp. 467-470 ◽

Cited By ~ 5

Author(s):

Hyder O. Mirghani ◽

Mohammed A. Ahmed ◽

Abdulateef S. Elbadawi

Keyword(s):

Academic Performance ◽

Medical Students ◽

Sleep Deprivation ◽

Daytime Sleepiness ◽

Chronic Sleep Deprivation ◽

Chronic Sleep

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Effect of Chronic Sleep Deprivation and Sleep Recovery on Hippocampal CA3 neurons, Spatial Memory and Anxiety-like behavior in Rats

Neurobiology of Learning and Memory ◽

10.1016/j.nlm.2021.107559 ◽

2021 ◽

pp. 107559

Author(s):

Konakanchi Suresh ◽

Venkateswarlu Raavi ◽

Harendra Kumar ◽

Vinutha Shankar

Keyword(s):

Sleep Deprivation ◽

Spatial Memory ◽

Hippocampal Ca3 ◽

Chronic Sleep Deprivation ◽

Ca3 Neurons ◽

Chronic Sleep

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Acute and Chronic Sleep Deprivation-Related Changes in N-methyl-D-aspartate Receptor—Nitric Oxide Signalling in the Rat Cerebral Cortex with Reference to Aging and Brain Lateralization

International Journal of Molecular Sciences ◽

10.3390/ijms20133273 ◽

2019 ◽

Vol 20 (13) ◽

pp. 3273 ◽

Cited By ~ 4

Author(s):

Zdenka Kristofikova ◽

Jana Sirova ◽

Jan Klaschka ◽

Saak V. Ovsepian

Keyword(s):

Nitric Oxide ◽

Nmda Receptor ◽

Sleep Deprivation ◽

Nmda Receptor Subunits ◽

Aspartate Receptor ◽

Age Dependent ◽

Chronic Sleep Deprivation ◽

Nos Isoforms ◽

The Impact ◽

Chronic Sleep

Aging and chronic sleep deprivation (SD) are well-recognized risk factors for Alzheimer’s disease (AD), with N-methyl-D-aspartate receptor (NMDA) and downstream nitric oxide (NO) signalling implicated in the process. Herein, we investigate the impact of the age- and acute or chronic SD-dependent changes on the expression of NMDA receptor subunits (NR1, NR2A, and NR2B) and on the activities of NO synthase (NOS) isoforms in the cortex of Wistar rats, with reference to cerebral lateralization. In young adult controls, somewhat lateralized seasonal variations in neuronal and endothelial NOS have been observed. In aged rats, overall decreases in NR1, NR2A, and NR2B expression and reduction in neuronal and endothelial NOS activities were found. The age-dependent changes in NR1 and NR2B significantly correlated with neuronal NOS in both hemispheres. Changes evoked by chronic SD (dysfunction of endothelial NOS and the increasing role of NR2A) differed from those evoked by acute SD (increase in inducible NOS in the right side). Collectively, these results demonstrate age-dependent regulation of the level of NMDA receptor subunits and downstream NOS isoforms throughout the rat brain, which could be partly mimicked by SD. As described herein, age and SD alterations in the prevalence of NMDA receptors and NOS could contribute towards cognitive decline in the elderly, as well as in the pathobiology of AD and the neurodegenerative process.

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