UNASSIGNED: Timely diagnosis of ischemic stroke (IS) in the acute phase is extremely vital to achieve proper treatment and good prognosis. In this study, we developed a novel prediction model based on the easily obtained information at initial inspection to assist in the early identification of IS.
UNASSIGNED: A total of 627 patients with IS and other intracranial hemorrhagic diseases from March 2017 to June 2018 were retrospectively enrolled in the derivation cohort. Based on their demographic information and initial laboratory examination results, the prediction model was constructed. The least absolute shrinkage and selection operator algorithm was used to select the important variables to form a laboratory panel. Combined with the demographic variables, multivariate logistic regression was performed for modeling, and the model was encapsulated within a visual and operable smartphone application. The performance of the model was evaluated on an independent validation cohort, formed by 304 prospectively enrolled patients from June 2018 to May 2019, by means of the area under the curve (AUC) and calibration.
UNASSIGNED: The prediction model showed good discrimination (AUC = 0.916, cut-off = 0.577), calibration, and clinical availability. The performance was reconfirmed in the more complex emergency department. It was encapsulated as the Stroke Diagnosis Aid app for smartphones. The user can obtain the identification result by entering the values of the variables in the graphical user interface of the application.
UNASSIGNED: The prediction model based on laboratory and demographic variables could serve as a favorable supplementary tool to facilitate complex, time-critical acute stroke identification.

The 2019 coronavirus disease pandemic poses unique challenges to healthcare delivery. To limit the exposure of providers and patients to severe acute respiratory syndrome coronavirus 2, the Centers for Disease Control and Prevention encourages providers to use telehealth platforms whenever possible. Given the maternal mortality crisis in the United States and the compounding 2019 coronavirus disease public health emergency, continued access to quality preconception, prenatal, intrapartum, and postpartum care are essential to the health and well-being of mother and baby.
This commentary explores unique opportunities to optimize virtual obstetric care for low-risk and high-risk mothers at each stage of pregnancy.
In this review paper, we present evidence-based literature and tools from first-hand experience implementing telemedicine in obstetric care clinics during the pandemic.
Using the best evidence-based practices with telemedicine, health care providers can deliver care in the safest, most respectful, and appropriate way possible while providing the critical support necessary in pregnancy. In reviewing the literature, several studies endorse the implementation of specific tools outlined in this article, to facilitate the implementation of telemedicine. From a quality improvement standpoint, evidence-based telemedicine provides a solution for overburdened healthcare systems, greater confidentiality for obstetric services, and a personalized avenue for health care providers to meet maternal health needs in the pandemic.
During the COVID-19 pandemic, continued access to quality prenatal, intrapartum, and postpartum care are essential to the health and well-being of mother and baby.

BACKGROUND: Smartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world.
OBJECTIVE: We aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state.
METHODS: In total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model.
RESULTS: The CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group.
CONCLUSIONS: The CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app.
BACKGROUND: ClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657.