This cohort study assesses the potential use of red blood cell distribution width for risk stratification of patients with coronavirus disease 2019.

Spinal cord ependymoma (SCE) is a rare tumor that is most commonly low-grade. Complete surgical resection has been established as first-line treatment and can be curative. However, SCEs tend to recur when complete tumor resection is not possible. Evidence supporting the use of adjuvant radiation and chemotherapy is not definitive. We review the most recent literature on SCE covering a comprehensive range of topics spanning the biology, presentation, clinical management, and outcomes. In addition, we present a case series of ten SCE patients with the goal of contributing to existing knowledge of this rare disease.

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.

Objective Schizophrenia has been associated with changes in heart rate (HR) and physical activity measures. However, the relationship between analysis window length and classifier accuracy using these features has yet to be quantified. Approach Here we used objective HR and activity data to classify contiguous days of data as belonging to a schizophrenia patient or a healthy control. HR and physical activity recordings were made on 12 medicated subjects with schizophrenia and 12 healthy controls. Features derived from these data included classical statistical characteristics, rest-activity metrics, transfer entropy, and multiscale fuzzy entropy. We varied the analysis window length from two to eight days, and selected features via minimal-redundancy-maximal-relevance. A support vector machine was trained to classify schizophrenia from control windows on a daily basis. Model performance was assessed via subject-wise leave-one-out-crossfold-validation. Main results An analysis window length of eight days resulted in an area under a receiver operating characteristic curve (AUC) of 0.96. Reducing the analysis window length to two days only lowered the AUC to 0.91. The type of most predictive features varied with analysis window length. Significance Our results suggest continuous tracking of subjects with schizophrenia over short time scales may be sufficient to estimate illness severity on a daily basis.

BackgroundCoronavirus disease 2019 is an acute respiratory illness with a high rate of hospitalization and mortality. Prognostic biomarkers are urgently needed. Red blood cell distribution width (RDW), a component of complete blood counts that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases. MethodsWe retrospectively studied the relationship between RDW and COVID-19 mortality risk for 1198 adult patients diagnosed with SARS-CoV-2 at 4 Partners ResultsElevated RDW (> 14.5%) was associated with increased mortality in patients of all ages with a risk ratio of 2.5 (95% CI, 2.3 -2.8). Stratified by age, the risk ratio was 6.2 (4.4 -7.9, N = 312) < 50 years, 3.2 (2.5 -4.1, N = 230) 50-60, 2.3 (1.6 -3.1, N = 236) 60-70, 1.2 (0.7 -1.8, N = 203) 70-80, and 1.9 (1.5 -2.3, N = 216) > 80 years. RDW was significantly associated with mortality in Cox proportional hazards models adjusted for age, D-Dimer, absolute lymphocyte count, and common comorbidities (p < 1e-4 for RDW in all cases). Patients whose RDW increased during admission had a ~3-fold elevation in mortality risk compared to those whose RDW did not change. ConclusionsElevated RDW at diagnosis and an increase in RDW during admission are both associated with increased mortality risk for adult COVID-19 patients at a large academic medical center network.

Objective Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation. Approach Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control. Main Results Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67). Significance These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

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