Estimating the Prevalence of Intimate Partner Violence at an Urban Hospital Before and During the COVID-19 Pandemic Using a Novel Natural Language Processing Algorithm

The impact of COVID-19 on intimate partner violence (IPV) in the United States is still relatively unknown, although some early data demonstrate that cases of IPV increased during COVID-19. The objective of this study was to measure the prevalence of IPV before and during the COVID-19 pandemic in a southeastern urban hospital. We performed a retrospective analysis of IPV encounters at a single high-volume Level I trauma hospital. IPV encounters were identified through a novel natural language processing algorithm using IPV-related words and phrases within unstructured clinical notes. IPV encounters from February to August 2019 (pre-COVID-19 period) were compared with encounters from February to August 2020 (COVID-19 period). The IPV visit rate during the COVID-19 period was higher than that during the pre-COVID-19 period (0.82% of all visits in 2020 vs. 0.72% of all visits in 2019). The number of IPV encounters for patients with no prior IPV visits was higher in 2020, whereas the number of revisits, patients with prior IPV encounters, was lower in 2020. There was an increased incidence of IPV during the COVID-19 pandemic with an increase in the number of patients presenting with first time IPV encounters. Future hospital and community pandemic preparedness protocols must include expansion of screening, resource allocation, and protective policies for those in unsafe situations.

Detectroops - A Street Mascarar

The proposed Detectroops system would promote public safety by measuring a person's temperature and assessing whether or not they are wearing a mask in public settings. The person will be able to enter public space after sterilising himself and meeting the requirements. By doing so, on every occasion the count of people attending will be taken into account. On exceeding the count limit, the officials will be notified till then they would not be allowed to enter. All of these systems are connected to a single system, which will be controlled and monitored by an official. We try to implement Detectroops by using various technologies like Deep Learning, Internet of Things. Since it is not possible to have everything under control, Detectroops are crucial in limiting the virus's spread and maximizing the safety of the people. © 2021 IEEE.

Body temperature trajectory associated with venous thromboembolism in COVID-19 patients

Rationale: COVID-19 is associated with significant morbidity and presents unique challenges, including an increased risk of venous thromboembolism (VTE). In a single-center study early in the pandemic, we identified four distinct COVID-19 subphenotypes using longitudinal body temperature (i.e., temperature trajectory subphenotypes). Importantly, these subphenotypes had significant differences in hematological labs such as platelets and d-dimer, suggesting a relationship between temperature and coagulopathy. In this study, we aim to validate the temperature trajectory subphenotypes in a multi-center cohort of COVID-19 patients and evaluate whether temperature trajectory can identify patients at higher risk for VTEs. Methods: We included all patients hospitalized with laboratory-confirmed diagnosis of COVID-19 across four hospitals in the greater Atlanta area. For the trajectory analyses, we included patients' temperature measurements from the first 72 hours of hospitalization. We compared the temperature measurements from the study patients to each of the four trajectories from the published model to calculate the “trajectory distance” (i.e., the distance the patient is away from each trajectory). The patients were classified into the trajectory subphenotype from which they were the smallest distance away. We used ICD-10 codes at discharge to identify patients who had documented diagnoses of acute VTEs and evaluated the association between VTEs and trajectory subphenotype. Then, we used logistic regression to evaluate whether trajectory distance could predict VTE when controlling for demographics and ddimer levels. Results: The 2,107 hospitalized patients who met study criteria had a median age of 59 years (IQR 47-71 years), were 51% female, 65% Black, 21% White, and 10% Hispanic. The incidence of VTE was 12% and the inpatient mortality rate was 11.6%. By temperature trajectory subphenotype: 12% were Group 1, 31% Group 2, 48% Group 3, and 8.1% Group 4 (“hypothermic”). Temperature trajectory had significant association with mortality (p<0.001), with Groups 1 and 4 having the highest mortality rates (17 and 18%, respectively). Temperature trajectory subphenotype was significantly associated with VTE (p=0.004), with “hypothermic” patients having twice the incidence of other subphenotypes. On logistic regression, trajectory distance was significantly associated with VTEs even controlling for d-dimer (Figure). Conclusions: We validated our temperature trajectory subphenotypes in a multi-center cohort of hospitalized patients with COVID-19. We found that temperature trajectory could have utility in identifying patients at higher risk for VTEs who may require more aggressive anticoagulation. (Table Presented).

Altered heart rate variability predicts mortality early among critically ill COVID-19 patients

INTRODUCTION: The novel coronavirus (SARS-CoV-2) has led to a large cascade of transmissions resulting in high numbers of individuals hospitalized for the coronavirus disease 2019 (COVID-19), an impact still being accounted across the globe. In this study, we seek to evaluate whether novel heart rate variability (HRV) measures can predict mortality within 24 hours of admission to the ICU among critically ill COVID-19 patients. METHODS: All medical, surgical, neurocritical, transplant, and cardiac ICU admissions with COVID-19 between March 1 through April 31st, 2020 within Emory Healthcare system were screened. Patients were selected to be included in the analysis if they were in the ICU for greater than 24 hours, had at least one positive qRT-PCR test for SARS-CoV-2 earlier in their hospitalization. EKG (250 Hz) was then analyzed for each patient over a 300 second (s) observational window, that was shifted by 30s in each iteration for the first 24 hrs after admission. MATLAB® was used to analyze the continuous EKG and extract relevant HRV features. We use the Kruskal-Wallis and Steel-Dwass tests (P < 0.05) for statistical analysis and interpretations of HRV multiple measures. RESULTS: A total of 312 COVID-19 patients were identified in a clinical chart review for SARS-CoV-2 by use of quantitative RT-PCR (qRT-PCR), of which 85 patients were admitted to the ICU and had sufficient data quality. The median HRV aggregates, including AC, DC, LFHF, VLF, SD1, SD2, SD1SD2, RMSSD, and pNN50 were all statistically significant (FDR p<0.01) between survivors and nonsurvivors. pNN50 and VLF displayed characteristics of muted temporal variability over the course of the first 24 hrs within nonsurvivors. CONCLUSIONS: Heart rate variability is broadly implicated across patients positive for SARS-CoV-2, and admitted to the ICU for critical illness, between survivors and non-survivors. HRV measures across all domains, including time-frequency and nonlinear features, demonstrate statistical significance in predicting outcomes. Temporal trajectories of these markers further suggest significant decoupling between the cohorts as the disease progresses, with salient decoupling noticeable as early as 12 hrs into the ICU stay.