Risk Factors for New Neurologic Diagnoses in Hospitalized Patients With COVID-19: A Case-Control Study in New York City.

Background and Objectives: There have been numerous reports of neurologic manifestations identified in hospitalized patients infected with SARS-CoV-2, the virus that causes COVID-19. Here, we identify the spectrum of associated neurologic symptoms and diagnoses, define the time course of their development, and examine readmission rates and mortality risk posthospitalization in a multiethnic urban cohort. Methods: We identify the occurrence of new neurologic diagnoses among patients with laboratory-confirmed SARS-CoV-2 infection in New York City. A retrospective cohort study was performed on 532 cases (hospitalized patients with new neurologic diagnoses within 6 weeks of positive SARS-CoV-2 laboratory results between March 1, 2020, and August 31, 2020). We compare demographic and clinical features of the 532 cases with 532 controls (hospitalized COVID-19 patients without neurologic diagnoses) in a case-control study with one-to-one matching and examine hospital-related data and outcomes of death and readmission up to 6 months after acute hospitalization in a secondary case-only analysis. Results: Among the 532 cases, the most common new neurologic diagnoses included encephalopathy (478, 89.8%), stroke (66, 12.4%), and seizures (38, 7.1%). In the case-control study, cases were more likely than controls to be male (58.6% vs 52.8%, p = 0.05), had baseline neurologic comorbidities (36.3% vs 13.0%, p < 0.0001), and were to be treated in an intensive care unit (62.0% vs 9.6%, p < 0.0001). Of the 394 (74.1%) cases who survived acute hospitalization, more than half (220 of 394, 55.8%) were readmitted within 6 months, with a mortality rate of 23.2% during readmission. Discussion: Hospitalized patients with SARS-CoV-2 and new neurologic diagnoses have significant morbidity and mortality postdischarge. Further research is needed to define the effect of neurologic diagnoses during acute hospitalization on longitudinal post-COVID-19-related symptoms including neurocognitive impairment.

Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia.

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 States' 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.

Association of depression and COVID-induced PTSD with cognitive symptoms after COVID-19 illness.

OBJECTIVE: Many patients recovering from COVID-19 report persistent psychological and cognitive symptoms months after viral clearance. We examined the association of depression and COVID-induced PTSD with cognitive symptoms following COVID-19 illness. METHODS: Patients treated for COVID-19 between March 26 and May 27, 2020 were surveyed three months later. Cognitive symptoms were assessed by asking "Since your COVID-19 illness, do you now have more difficulty: 1) Remembering conversations a few days later? 2) Remembering where you placed familiar objects? 3) Finding the right words while speaking?" Patients endorsing at least one such complaint were coded positive for cognitive symptoms. Logistic regression was used to estimate the association of depression (PHQ-8 ≥ 10) and COVID-induced PTSD (PCL-5 ≥ 30) with cognitive symptoms, adjusting for demographic and clinical factors. RESULTS: Among 153 participants, 44.4% reported at least one cognitive symptom, 18.3% were depressed, and 23.5% had COVID-induced PTSD. Adjusting for covariates, depression (OR 5.15, 95% CI 1.30-20.35, p = 0.02) and COVID-induced PTSD (OR 3.67, 95% CI 1.13-11.89, p = 0.03) were significantly associated with cognitive symptoms; self-reported history of mental illness was also associated (OR 4.90, 95% CI 1.24-19.41, p = 0.02). CONCLUSIONS: Depression, COVID-induced PTSD, and prior mental illness were strongly associated with cognitive symptoms three months after acute COVID-19 illness.

Disorders of Consciousness in Hospitalized Patients with COVID-19: The Role of the Systemic Inflammatory Response Syndrome.

BACKGROUND: Prevalence and etiology of unconsciousness are uncertain in hospitalized patients with coronavirus disease 2019 (COVID-19). We tested the hypothesis that increased inflammation in COVID-19 precedes coma, independent of medications, hypotension, and hypoxia. METHODS: We retrospectively assessed 3203 hospitalized patients with COVID-19 from March 2 through July 30, 2020, in New York City with the Glasgow Coma Scale and systemic inflammatory response syndrome (SIRS) scores. We applied hazard ratio (HR) modeling and mediation analysis to determine the risk of SIRS score elevation to precede coma, accounting for confounders. RESULTS: We obtained behavioral assessments in 3203 of 10,797 patients admitted to the hospital who tested positive for SARS-CoV-2. Of those patients, 1054 (32.9%) were comatose, which first developed on median hospital day 2 (interquartile range [IQR] 1-9). During their hospital stay, 1538 (48%) had a SIRS score of 2 or above at least once, and the median maximum SIRS score was 2 (IQR 1-2). A fivefold increased risk of coma (HR 5.05, 95% confidence interval 4.27-5.98) was seen for each day that patients with COVID-19 had elevated SIRS scores, independent of medication effects, hypotension, and hypoxia. The overall mortality in this population was 13.8% (n = 441). Coma was associated with death (odds ratio 7.77, 95% confidence interval 6.29-9.65) and increased length of stay (13 days [IQR 11.9-14.1] vs. 11 [IQR 9.6-12.4]), accounting for demographics. CONCLUSIONS: Disorders of consciousness are common in hospitalized patients with severe COVID-19 and are associated with increased mortality and length of hospitalization. The underlying etiology of disorders of consciousness in this population is uncertain but, in addition to medication effects, may in part be linked to systemic inflammation.

Analysis of Air Pollutants in Covid 19 Pandemic Lockdown- A case study of Bareilly, UP, India

The contemporary world is dealing with the rise of the novel coronavirus pandemic. Globally, as on 14 September 2020, there have been 28,918,900 confirmed cases of COVID-19, including 922,252 deaths, reported to WHO with the cases still on the rise. In India, as a preventive measure, complete lockdown was imposed all over the country from 25th March, 2020 which has significantly reduced the vehicular movement. Bareilly was reported among the seven most air polluted cities of Uttar Pradesh where PM10 was almost four times the annual standard of 60 μg/m3 averaging 226 μg/m3 for the year 2015 and 2016.The city Bareilly of State Uttar Pradesh do not have too much of industries and therefore industries cannot be blamed. Alternatively, vehicular or construction emission sources could not be ruled out and it can be concluded that primary sources of air pollution could be either automobiles or incomplete construction work. The present study is focused on monitoring of air pollutants PM10, PM2.5, SO2 and NO2, at Bareilly district of Uttar Pradesh and analyzed during the lockdown period due to pandemic COVID 19 from three monitoring stations. In the first week of lockdown, i.e. from 25th March 2020 to 31st March 2020 the PM10 and PM2.5 concentration averaged 60μg/m3 and 47 μg/m3 respectively which is below the NAAQS average limits of 80μg/m3 and 60μg/m3 respectively. Whereas the concentrations of gaseous pollutants SO2 and NO2 was found to be much below the monthly NAAQS limits of 60 μg/m3 averaging 21μg/m3 and 15μg/m3 respectively. In April 2020, the vehicular movement was minimum and the level of air pollutants, PM10, PM2.5, SO2 and NO2, were found to be 54μg/m3, 41μg/m3, 19μg/m3 and 14μg/m3 respectively which is minimum in the six months of study from January 2020 to June 2020 and lowest in comparison to the air quality data of last 25 years.It can therefore be concluded that vehicular emissions contribute significantly for air pollution in Bareilly city.

Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper, we have proposed a Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images. Multiple state-of-the-art CNN models—DenseNet201, Resnet50V2 and Inceptionv3, have been adopted in the proposed work. They have been trained individually to make independent predictions. Then the models are combined, using a new method of weighted average ensembling technique, to predict a class value. To test the efficacy of the solution we have used publicly available chest X-ray images of COVID +ve and –ve cases. 538 images of COVID +ve patients and 468 images of COVID –ve patients have been divided into training, test and validation sets. The proposed approach gave a classification accuracy of 91.62% which is higher than the state-of-the-art CNN models as well the compared benchmark algorithm. We have developed a GUI-based application for public use. This application can be used on any computer by any medical personnel to detect COVID +ve patients using Chest X-Ray images within a few seconds. [ABSTRACT FROM AUTHOR] Copyright of Pattern Analysis & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hypercoagulable viscoelastic blood clot characteristics in critically ill coronavirus disease 2019 patients and associations with thrombotic complications.

BACKGROUND: Critically ill coronavirus disease 2019 (COVID-19) patients have frequent thrombotic complications and laboratory evidence of hypercoagulability. The relationship of coagulation tests and thrombosis requires investigation to identify best diagnostic and treatment approaches. We assessed for hypercoagulable characteristics in critically ill COVID-19 patients using rotational thromboelastometry (ROTEM) and explored relationships of D-dimer and ROTEM measurements with thrombotic complications. METHODS: Critically ill adult COVID-19 patients receiving ROTEM testing between March and April 2020 were analyzed. Patients receiving therapeutic anticoagulation before ROTEM were excluded. Rotational thromboelastometry measurements from COVID-19 patients were compared with non-COVID-19 patients matched by age, sex, and body mass index. Intergroup differences in ROTEM measurements were assessed using t tests. Correlations of D-dimer levels to ROTEM measurements were assessed in COVID-19 patients who had available concurrent testing. Intergroup differences of D-dimer and ROTEM measurements were explored in COVID-19 patients with and without thrombosis. RESULTS: Of 30 COVID-19 patients receiving ROTEM, we identified hypercoagulability from elevated fibrinogen compared with non-COVID-19 patients (fibrinogen assay maximum clot firmness [MCF], 47 ± 13 mm vs. 20 ± 7 mm; mean intergroup difference, 27.4 mm; 95% confidence interval [CI], 22.1-32.7 mm; p < 0.0001). In our COVID-19 cohort, thrombotic complications were identified in 33%. In COVID-19 patients developing thrombotic complications, we identified higher D-dimer levels (17.5 ± 4.3 µg/mL vs. 8.0 ± 6.3 µg/mL; mean difference, 9.5 µg/mL; 95% CI, 13.9-5.1; p < 0.0001) but lower fibrinogen assay MCF (39.7 ± 10.8 mm vs. 50.1 ± 12.0 mm; mean difference, -11.2 mm; 95% CI, -2.1 to -20.2; p = 0.02) compared with patients without thrombosis. We identified negative correlations of D-dimer levels and ROTEM MCF in these patients (r = -0.61; p = 0.001). CONCLUSION: We identified elevated D-dimer levels and hypercoagulable blood clot characteristics from increased fibrinogen on ROTEM testing in critically ill COVID-19 patients. However, we identified lower, albeit still hypercoagulable, ROTEM measurements of fibrinogen in COVID-19 patients with thrombotic complications compared with those without. Further work is required to externally validate these findings and to investigate the mechanistic drivers for these relationships to identify best diagnostic and treatment approaches for these patients. LEVEL OF EVIDENCE: Epidemiologic, level IV.

Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.

OBJECTIVE: To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent. METHODS: 98 days of continuous frontotemporal EEG from 11 consecutive patients was evaluated daily by an epileptologist to recommend reduction or maintenance of the sedative level. We evaluated the need to increase sedation in the 6 h following this recommendation. Post-hoc analysis of the quantitative EEG was correlated with the level of sedation using a machine learning algorithm. RESULTS: Eleven patients were studied for a total of ninety-eight sedation days. EEG was consistent with excessive sedation on 57 (58%) and adequate sedation on 41 days (42%). Recommendations were followed by the team on 59% (N = 58; 19 to reduce and 39 to keep the sedation level). In the 6 h following reduction in sedation, increases of sedation were needed in 7 (12%). Automatized classification of EEG sedation levels reached 80% (±17%) accuracy. CONCLUSIONS: Visual inspection of a limited EEG helped sedation depth guidance. In a secondary analysis, our data supported that this determination may be automated using quantitative EEG analysis. SIGNIFICANCE: Our results support the use of frontotemporal EEG for guiding sedation in patients with COVID-19.

Psychological distress, coping behaviors, and preferences for support among New York healthcare workers during the COVID-19 pandemic.

OBJECTIVE: The mental health toll of COVID-19 on healthcare workers (HCW) is not yet fully described. We characterized distress, coping, and preferences for support among NYC HCWs during the COVID-19 pandemic. METHODS: This was a cross-sectional web survey of physicians, advanced practice providers, residents/fellows, and nurses, conducted during a peak of inpatient admissions for COVID-19 in NYC (April 9th-April 24th 2020) at a large medical center in NYC (n = 657). RESULTS: Positive screens for psychological symptoms were common; 57% for acute stress, 48% for depressive, and 33% for anxiety symptoms. For each, a higher percent of nurses/advanced practice providers screened positive vs. attending physicians, though housestaff's rates for acute stress and depression did not differ from either. Sixty-one percent of participants reported increased sense of meaning/purpose since the COVID-19 outbreak. Physical activity/exercise was the most common coping behavior (59%), and access to an individual therapist with online self-guided counseling (33%) garnered the most interest. CONCLUSIONS: NYC HCWs, especially nurses and advanced practice providers, are experiencing COVID-19-related psychological distress. Participants reported using empirically-supported coping behaviors, and endorsed indicators of resilience, but they also reported interest in additional wellness resources. Programs developed to mitigate stress among HCWs during the COVID-19 pandemic should integrate HCW preferences.