Study Objective:Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. Therefore, we hypothesize that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.Methods: Using a dataset including 8,682 polysomnographs, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long 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 achieve the best performance for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence interval 0.599 -0.602); and 0.762 (0.760 -0.763) for discriminating awake vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is better for young participants and for those with a low apnea-hypopnea index, while it is robust for commonly used outpatient medications. Conclusions:Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large population. It opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible, such as in critically ill patients. Deep Network ArchitectureWe trained five deep neural networks based on the following input signals and their combinations: 1) ECG; 2) CHEST (chest respiratory effort); 3) ABD (abdominal respiratory effort); 4) ECG+CHEST; and 5) ECG+ABD. Each deep neural network contained a feed-forward convolutional neural network (CNN) which learned features pertaining to each epoch, and a recurrent neural network (RNN), in this case long-short term memory (LSTM), to learn temporal patterns among consecutive epochs.The CNN of the network is similar to that in Hannun et al. 20 . As shown in Figure 1A and Figure 1B, the network for a single type of input signal, i.e. ECG, CHEST or ABD, consists of a convolutional layer, several residual blocks and a final output block. For a network with both ECG and CHEST/ABD as input signals ( Figure 1C), we first fixed the weights of the layers up to the 9 th residual block (gray) for the ECG network and similarly fixed up to the 5 th residual block (gray) for the CHEST/ABD network, concatenated the outputs, and then fed this concatenation into a subnetwork containing five residual blocks and a final output block. The numbers of fixed layers were chosen so that the outputs of layers from different modalities have the same shape (after padding zeros), and were then concatenated.The LSTM of the network has the same structure for different input signals. It is a bi-directional LSTM, where the context cells from the forward and backward directions are concatenated. For the network

Background We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for 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 (RICs) or the emergency department (ED). Data was extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Outcomes were hospitalization, critical illness (ICU 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 five 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.

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Objective The purpose of this study was to estimate the time to recovery of command‐following and associations between hypoxemia with time to recovery of command‐following. Methods In this multicenter, retrospective, cohort study during the initial surge of the United Statesʼ pandemic (March–July 2020) we estimate the time from intubation to recovery of command‐following, using Kaplan Meier cumulative‐incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID‐19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6). Results Five hundred seventy‐one patients of the 795 patients recovered command‐following. The median time to recovery of command‐following was 30 days (95% confidence interval [CI] = 27–32 days). Median time to recovery of command‐following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command‐following  was associated with hypoxemia (PaO2 ≤55 mmHg hazard ratio [HR] = 0.56, 95% CI = 0.46–0.68; PaO2 ≤70 HR = 0.88, 95% CI = 0.85–0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non‐overlapping second surge cohort (N = 427, October 2020 to April 2021). Interpretation Survivors of severe COVID‐19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life‐sustaining therapies. ANN NEUROL 2022;91:740–755