Sound and light levels in intensive care units in a large urban hospital in the United States.

Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.

Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks.

Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study.

PURPOSE: Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. METHODS: Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. RESULTS: Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. CONCLUSIONS: Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations With Clinical Outcomes.

To develop a physiologic grading system for the severity of acute encephalopathy manifesting as delirium or coma, based on EEG, and to investigate its association with clinical outcomes. DESIGN: This prospective, single-center, observational cohort study was conducted from August 2015 to December 2016 and October 2018 to December 2019. SETTING: Academic medical center, all inpatient wards. PATIENTS/SUBJECTS: Adult inpatients undergoing a clinical EEG recording; excluded if deaf, severely aphasic, developmentally delayed, non-English speaking (if noncomatose), or if goals of care focused primarily on comfort measures. Four-hundred six subjects were assessed; two were excluded due to technical EEG difficulties. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A machine learning model, with visually coded EEG features as inputs, was developed to produce scores that correlate with behavioral assessments of delirium severity (Confusion Assessment Method-Severity [CAM-S] Long Form [LF] scores) or coma; evaluated using Spearman R correlation; area under the receiver operating characteristic curve (AUC); and calibration curves. Associations of Visual EEG Confusion Assessment Method Severity (VE-CAM-S) were measured for three outcomes: functional status at discharge (via Glasgow Outcome Score [GOS]), inhospital mortality, and 3-month mortality. Four-hundred four subjects were analyzed (mean [sd] age, 59.8 yr [17.6 yr]; 232 [57%] male; 320 [79%] White; 339 [84%] non-Hispanic); 132 (33%) without delirium or coma, 143 (35%) with delirium, and 129 (32%) with coma. VE-CAM-S scores correlated strongly with CAM-S scores (Spearman correlation 0.67 [0.62-0.73]; p < 0.001) and showed excellent discrimination between levels of delirium (CAM-S LF = 0 vs ≥ 4, AUC 0.85 [0.78-0.92], calibration slope of 1.04 [0.87-1.19] for CAM-S LF ≤ 4 vs ≥ 5). VE-CAM-S scores were strongly associated with important clinical outcomes including inhospital mortality (AUC 0.79 [0.72-0.84]), 3-month mortality (AUC 0.78 [0.71-0.83]), and GOS at discharge (0.76 [0.69-0.82]). CONCLUSIONS: VE-CAM-S is a physiologic grading scale for the severity of symptoms in the setting of delirium and coma, based on visually assessed electroencephalography features. VE-CAM-S scores are strongly associated with clinical outcomes.

Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation.

OBJECTIVE: Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea-Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO2 signals using a large (n = 412) dataset serving as ground truth. DESIGN: Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%) feature, one allowing a time lag of 30 s between the two signals. RESULTS: Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively. CONCLUSIONS: A wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.

Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S). DESIGN: Retrospective cohort study. SETTING: Single-center tertiary academic medical center. PATIENTS: Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity. CONCLUSIONS: The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

Enhanced Virus Detection and Metagenomic Sequencing in Patients with Meningitis and Encephalitis.

Meningitis and encephalitis are leading causes of central nervous system (CNS) disease and often result in severe neurological compromise or death. Traditional diagnostic workflows largely rely on pathogen-specific tests, sometimes over days to weeks, whereas metagenomic next-generation sequencing (mNGS) profiles all nucleic acid in a sample. In this single-center, prospective study, 68 hospitalized patients with known (n = 44) or suspected (n = 24) CNS infections underwent mNGS from RNA and DNA to identify potential pathogens and also targeted sequencing of viruses using hybrid capture. Using a computational metagenomic classification pipeline based on KrakenUniq and BLAST, we detected pathogen nucleic acid in cerebrospinal fluid (CSF) from 22 subjects, 3 of whom had no clinical diagnosis by routine workup. Among subjects diagnosed with infection by serology and/or peripheral samples, we demonstrated the utility of mNGS to detect pathogen nucleic acid in CSF, importantly for the Ixodes scapularis tick-borne pathogens Powassan virus, Borrelia burgdorferi, and Anaplasma phagocytophilum. We also evaluated two methods to enhance the detection of viral nucleic acid, hybrid capture and methylated DNA depletion. Hybrid capture nearly universally increased viral read recovery. Although results for methylated DNA depletion were mixed, it allowed the detection of varicella-zoster virus DNA in two samples that were negative by standard mNGS. Overall, mNGS is a promising approach that can test for multiple pathogens simultaneously, with efficacy similar to that of pathogen-specific tests, and can uncover geographically relevant infectious CNS disease, such as tick-borne infections in New England. With further laboratory and computational enhancements, mNGS may become a mainstay of workup for encephalitis and meningitis. IMPORTANCE Meningitis and encephalitis are leading global causes of central nervous system (CNS) disability and mortality. Current diagnostic workflows remain inefficient, requiring costly pathogen-specific assays and sometimes invasive surgical procedures. Despite intensive diagnostic efforts, 40 to 60% of people with meningitis or encephalitis have no clear cause of CNS disease identified. As diagnostic uncertainty often leads to costly inappropriate therapies, the need for novel pathogen detection methods is paramount. Metagenomic next-generation sequencing (mNGS) offers the unique opportunity to circumvent these challenges using unbiased laboratory and computational methods. Here, we performed comprehensive mNGS from 68 prospectively enrolled patients with known (n = 44) or suspected (n = 24) CNS viral infection from a single center in New England and evaluated enhanced methods to improve the detection of CNS pathogens, including those not traditionally identified in the CNS by nucleic acid detection. Overall, our work helps elucidate how mNGS can become integrated into the diagnostic toolkit for CNS infections.

Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19.

Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19. Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis. Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14-2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33-0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34-10.92) compared to patients without cerebrovascular disease. Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.

HIV increases sleep-based brain age despite antiretroviral therapy.

STUDY OBJECTIVES: Age-related comorbidities and immune activation raise concern for advanced brain aging in people living with HIV (PLWH). The brain age index (BAI) is a machine learning model that quantifies deviations in brain activity during sleep relative to healthy individuals of the same age. High BAI was previously found to be associated with neurological, psychiatric, cardiometabolic diseases, and reduced life expectancy among people without HIV. Here, we estimated the effect of HIV infection on BAI by comparing PLWH and HIV- controls. METHODS: Clinical data and sleep EEGs from 43 PLWH on antiretroviral therapy (HIV+) and 3,155 controls (HIV-) were collected from Massachusetts General Hospital. The effect of HIV infection on BAI, and on individual EEG features, was estimated using causal inference. RESULTS: The average effect of HIV on BAI was estimated to be +3.35 years (p < 0.01, 95% CI = [0.67, 5.92]) using doubly robust estimation. Compared to HIV- controls, HIV+ participants exhibited a reduction in delta band power during deep sleep and rapid eye movement sleep. CONCLUSION: We provide causal evidence that HIV contributes to advanced brain aging reflected in sleep EEG. A better understanding is greatly needed of potential therapeutic targets to mitigate the effect of HIV on brain health, potentially including sleep disorders and cardiovascular disease.

Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing.

BACKGROUND: Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. OBJECTIVE: Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. METHODS: Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women's Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. RESULTS: The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: "appointments specialty," "home health," and "home care" (home); "intubate" and "ARDS" (inpatient rehabilitation); "service" (SNIF); "brief assessment" and "covid" (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition. CONCLUSIONS: A supervised learning-based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients' discharge disposition that is possible with EHR data.

CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis.

BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March-2 May) and prospective (n = 2205, 3-14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS: In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.

Association of Sleep Electroencephalography-Based Brain Age Index With Dementia.

Importance: Dementia is an increasing cause of disability and loss of independence in the elderly population yet remains largely underdiagnosed. A biomarker for dementia that can identify individuals with or at risk for developing dementia may help close this diagnostic gap. Objective: To investigate the association between a sleep electroencephalography-based brain age index (BAI), the difference between chronological age and brain age estimated using the sleep electroencephalogram, and dementia. Design, Setting, and Participants: In this retrospective cross-sectional study of 9834 polysomnograms, BAI was computed among individuals with previously determined dementia, mild cognitive impairment (MCI), or cognitive symptoms but no diagnosis of MCI or dementia, and among healthy individuals without dementia from August 22, 2008, to June 4, 2018. Data were analyzed from November 15, 2018, to June 24, 2020. Exposure: Dementia, MCI, and dementia-related symptoms, such as cognitive change and memory impairment. Main Outcomes and Measures: The outcome measures were the trend in BAI when moving from groups ranging from healthy, to symptomatic, to MCI, to dementia and pairwise comparisons of BAI among these groups. Findings: A total of 5144 sleep studies were included in BAI examinations. Patients in these studies had a median (interquartile range) age of 54 (43-65) years, and 3026 (59%) were men. The patients included 88 with dementia, 44 with MCI, 1075 who were symptomatic, and 2336 without dementia. There was a monotonic increase in mean (SE) BAI from the nondementia group to the dementia group (nondementia: 0.20 [0.42]; symptomatic: 0.58 [0.41]; MCI: 1.65 [1.20]; dementia: 4.18 [1.02]; P < .001). Conclusions and Relevance: These findings suggest that a sleep-state electroencephalography-based BAI shows promise as a biomarker associated with progressive brain processes that ultimately result in dementia.

COVID-19 Outpatient Screening: a Prediction Score for Adverse Events.

BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak. METHODS: Single-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC). RESULTS: In the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.

Cerebral vascular malformations and their imaging modalities.

Cerebrovascular malformations are uncommon diverse group of dysmorphic vascular communications that may occur sporadically or as part of genetic syndromes. These include non-neoplastic lesions such as arteriovenous malformations (AVM), cavernous malformations (CM), developmental venous anomalies (DVA), and telangiectasias as well as others like arteriovenous fistulas (AVF), vein of Galen malformations (VOGM), and mixed or unclassified angiomas. These lesions often carry a high degree of morbidity and mortality often requiring surgical or endovascular interventions. The field of cerebrovascular anomalies has seen considerable advancement in the last few years. Treatment and management options of various types of brain anomalies have evolved in neurological, neurosurgical, and neuro-interventional radiology arena. The use of radiological imaging studies is a critical element for treatment of such neurosurgical cases. As imaging modalities continue to evolve at a rapid pace, it is imperative for neurological surgeons to be familiar with current imaging modalities essential for a precise diagnosis. Better understanding of these cerebrovascular lesions along with their associated imaging findings assists in determining the appropriate treatment options. In the current review, authors highlight various cerebrovascular malformations and their current imaging modalities.

Sleep staging from electrocardiography and respiration with deep learning.

STUDY OBJECTIVES: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals. METHODS: Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals. RESULTS: ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications. CONCLUSIONS: Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.

Rapid Detection of Powassan Virus in a Patient With Encephalitis by Metagenomic Sequencing.

We describe a patient with severe and progressive encephalitis of unknown etiology. We performed rapid metagenomic sequencing from cerebrospinal fluid and identified Powassan virus, an emerging tick-borne flavivirus that has been increasingly detected in the United States.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.