Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study

Background and Objectives

Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods

This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk.

Results

There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort.

Discussion

The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

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.

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.

Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography

OBJECTIVE

To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG).

MATERIALS AND METHODS

We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results.

RESULTS

One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%.

SIGNIFICANCE

A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.

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.

Antiseizure Medication Treatment and Outcomes in Patients with Subarachnoid Hemorrhage Undergoing Continuous EEG Monitoring

BACKGROUND

Patients with aneurysmal subarachnoid hemorrhage (aSAH) with electroencephalographic epileptiform activity (seizures, periodic and rhythmic patterns, and sporadic discharges) are frequently treated with antiseizure medications (ASMs). However, the safety and effectiveness of ASM treatment for epileptiform activity has not been established. We used observational data to investigate the effectiveness of ASM treatment in patients with aSAH undergoing continuous electroencephalography (cEEG) to develop a causal hypothesis for testing in prospective trials.

METHODS

This was a retrospective single-center cohort study of patients with aSAH admitted between 2011 and 2016. Patients underwent ≥ 24 h of cEEG within 4 days of admission. All patients received primary ASM prophylaxis until aneurysm treatment (typically within 24 h of admission). Treatment exposure was defined as reinitiation of ASMs after aneurysm treatment and cEEG initiation. We excluded patients with non-cEEG indications for ASMs (e.g., epilepsy, acute symptomatic seizures). Outcomes measures were 90-day mortality and good functional outcome (modified Rankin Scale scores 0-3). Propensity scores were used to adjust for baseline covariates and disease severity.

RESULTS

Ninety-four patients were eligible (40 continued ASM treatment; 54 received prophylaxis only). ASM continuation was not significantly associated with higher 90-day mortality (propensity-adjusted hazard ratio [HR] = 2.01 [95% confidence interval (CI) 0.57-7.02]). ASM continuation was associated with lower likelihood for 90-day good functional outcome (propensity-adjusted HR = 0.39 [95% CI 0.18-0.81]). In a secondary analysis, low-intensity treatment (low-dose single ASM) was not significantly associated with mortality (propensity-adjusted HR = 0.60 [95% CI 0.10-3.59]), although it was associated with a lower likelihood of good outcome (propensity-adjusted HR = 0.37 [95% CI 0.15-0.91]), compared with prophylaxis. High-intensity treatment (high-dose single ASM, multiple ASMs, or anesthetics) was associated with higher mortality (propensity-adjusted HR = 6.80 [95% CI 1.67-27.65]) and lower likelihood for good outcomes (propensity-adjusted HR = 0.30 [95% CI 0.10-0.94]) compared with prophylaxis only.

CONCLUSIONS

Our findings suggest the testable hypothesis that continuing ASMs in patients with aSAH with cEEG abnormalities does not improve functional outcomes. This hypothesis should be tested in prospective randomized studies.

Assessing and analysing contamination of a dairy products processing plant by Staphylococcus aureus using antibiotic resistance and PFGE

A dairy product processing plant was studied for 2.5 years to examine contamination with Staphylococcus aureus and try to correlate the source of contamination. Cultures were submitted to an antibiotic susceptibility test (AST) and characterised by Pulsed-field Gel Electrophoresis (PFGE) analysis. Results showed that 35.2% (19/51) of food handlers were asymptomatic carriers of S. aureus, and that 90.4% (19/21) of raw milk sampled was contaminated. Staphylococcus aureus was isolated from only 10 samples among more than 3200 investigated dairy products. No S. aureus contamination was found on machinery. The AST analysis demonstrated sensitivity of tested S. aureus to oxacillin, cephalothin, vancomycin, gentamicin, and sulfamethoxazole/trimethoprim. AST analysis generated eight different phenotypic profiles, but did not allow us to identify the source of contamination in seven of ten final products. PFGE analysis proved to be a sensitive method as it generated 42 different DNA banding profiles among the 48 S. aureus investigated, demonstrating a lack of predominance of endemic strains in the plant, contrary to suggestions raised by antibiotic resistance typing. Based on PFGE genotyping, S. aureus strains isolated from four contaminated final products were similar to four S. aureus isolated from raw milk. Five final products contained S. aureus different from all other strains collected, and one showed similarity to a strain isolated from a food handler. These results suggest contamination by raw milk as the main source of contamination of the final dairy products.

Studies on plasmid stability, cell metabolism and superoxide dismutase production by Pgk? strains of Saccharomyces cerevisiae

A double mutant sod1/pgk1 strain of Saccharomyces cerevisiae has been constructed in order to investigate the effects of different environmental conditions on yeast physiology, plasmid stability, and superoxide dismutase (SOD) production. Strains were transformed with yeast episomal plasmids (YEp) containing both PGK1 and SOD1 genes and were grown on fermentable carbon sources and under vigorous aeration. Under these conditions, the presence of the PGK1 gene was made essential for growth and both genes were efficiently expressed. However, plasmid-borne PGK1 was found not to increase the stability of YEp vectors in batch cultures of Pgk- cells. Paradoxically, plasmid stability increased during the respiratory phase of growth. An investigation of the metabolism of Pgk- cells demonstrated that these glycolytic pathway mutants do not appreciably metabolize glycerol. Thus Pgk+, plasmid-containing, cells have a selective advantage during the respiratory phase of batch growth since they can utilize both glycerol and ethanol.

[Neutralizing antibodies to poliovirus in the sera of newborn infants before and after mass immunization of the Brazilian population from 0 to 5. São Paulo, Brazil (1980)]

Foram colhidas amostras de sangue de 178 recém-nascidos (RN) em berçários de hospital localizado no Município de São Paulo. Noventa crianças foram puncionadas antes do primeiro "Dia Nacional de Vacinação Contra a Poliomielite" e as outras 88, após o segundo "Dia Nacional de Vacinação Contra a Poliomielite", realizados em 1980. Nessas campanhas foram imunizadas as crianças com idade de zero a cinco anos, em todo o Brasil. No presente trabalho pesquisou-se os títulos de anticorpos neutralizantes contra poliovírus nos dois grupos de recém-nascidos. Após a imunização em massa verificou-se que a taxa de recém-nascidos triplo suscetíveis decresceu de 8,9% para 4,5%, enquanto que o aumento observado do triplo imunes foi de 38,9% para 52,3%; essas diferenças mostraram-se estatisticamente significantes ao nível de 5,0%. A proporção de recém-nascidos, com títulos de anticorpos neutralizantes contra poliovírus iguais ou maiores do que 8, aumentou após as campanhas de imunização, quando passaram de 68,9% para 81,8%, de 73,3% para 83,0% e de 57,8% para 70,5%, respectivamente, para os sorotipos 1, 2 e 3. Essas diferenças mostraram se estatisticamente significantes, ao nível de 5,0%, em relação aos poliovírus 1 e 3.

[Occurrence of enterovirus infection in newborn infants admitted to a maternity hospital in the municipality of São Paulo, Brazil (1980)]

Pesquisou-se infecção intestinal por enterovirus em 300 crianças, aparentemente normais, nascidas de parto hospitalar, com a mediana das idades de 2 dias, estudadas antes da alta hospitalar. Essas crianças foram divididas em três grupos de igual número, sendo que o primeiro grupo foi examinado no período que precedeu o 1.º Dia Nacional de Vacinação Contra a Poliomielite, efetuado em 1980, o segundo logo após a realização dessa imunização em massa e o terceiro posteriormente ao 2º Dia Nacional de Vacinação Contra a Poliomielite levado a efeito no mesmo ano. A pesquisa de enterovirus foi feita a partir de uma única amostra de fezes, colhida de cada criança por meio de Swab retal. Obteve-se o isolamento de poliovírus em 13 (4,3%) dos 300 recém-nascidos estudados, sendo que 8 deles pertenciam ao primeiro grupo e os outros 5 ao segundo. Das crianças infectadas, 12 eliminavam poliovírus lei poliovírus 3. Não foram isolados outros enterovirus. Discute-se a possibilidade da infecção por esses poliovírus ter ocorrido por transmissão transplacentária, via canal de parto ou ainda por infecção cruzada no próprio ambiente hospitalar.