Dexmedetomidine and sleep quality in mechanically ventilated critically ill patients: study protocol for a randomised placebo-controlled trial.

INTRODUCTION: Sleep deprivation, which is a common complication in the intensive care unit (ICU), is associated with delirium and increased mortality. Sedation with gamma-aminobutyric acid agonists (propofol, benzodiazepine) results in significant disturbance of the sleep architecture. Dexmedetomidine is a lipophilic imidazole with an affinity for α2-adrenoceptors and it has sedative and analgesic properties. It has been reported to enhance sleep efficiency, thus sedate while preserving sleep architecture. METHODS AND ANALYSIS: Thirty consecutive patients are planned to be included, at the Department of Anesthesia and Intensive Care at the Hospital of Southwest Jutland, Denmark. The study is a double-blinded, randomised, controlled trial with two parallel groups (2:1 allocation ratio). Screening and inclusion will be done on day 1 from 8:00 to 16:00. Two 16 hours PSG (polysomnography) recording will be done starting at 16:00 on day 1 and day 2. Randomisation is performed if the first recording is of acceptable quality, otherwise the patient is excluded before randomisation. Dexmedetomidine/placebo will be administered during the second recording from 18:00 on day 2 to 6:00 on day 3. PRIMARY ENDPOINT: Improvement of total sleep time and sleep quality of clinical significance determined by PSG. SECONDARY ENDPOINTS: Sleep phases determined by PSG. Daytime function and delirium determined by Confusion Assessment Method-ICU. Alertness and wakefulness determined by Richmonde Agitation Sedation Scale. The objective is to compare the effect of dexmedetomidine versus placebo on sleep quality in critical ill mechanically ventilated patients. ETHICS AND DISSEMINATION: The trial investigate the potential benefit of dexmedetomidine on clinically relevant endpoints. If a beneficial effect is shown, this would have a large impact on future treatment of mechanically ventilated critically ill patients. Publication in peer-reviewed journal are plannedand the study has been approved by the National Committee on Health Research Ethics (ID:S-20180214). TRIAL REGISTRATION NUMBER: EudraCT (2017-001612-11DK) and Danish National Committee on Health Research Ethics (ID:S-20180214). The study related to pre-results.

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting.

STUDY OBJECTIVES: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. METHODS: A deep neural network model was developed using 15,684 polysomnography studies from five different cohorts. We applied four different scenarios: (1) impact of varying timescales in the model; (2) performance of a single cohort on other cohorts of smaller, greater, or equal size relative to the performance of other cohorts on a single cohort; (3) varying the fraction of mixed-cohort training data compared with using single-origin data; and (4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. RESULTS: Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777-0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787-0.790]; 3: 0.808 ± 0.092, 95% CI [0.807-0.810]; 4: 0.821 ± 0.085, 95% CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts. CONCLUSIONS: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.