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2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310208

ABSTRACT

Background: Since the outbreak of the novel coronavirus disease (COVID-19), the fever outpatient clinic has been open in Shanghai East Hospital (SEH). We analyzed the data for all 4,699 patients from SEH and the 27 confirmed COVID-19 cases among them to determine the clinical and epidemiological characteristics of confirmed COVID-19 cases identified in the SEH. Methods: : Data were collected for patients who visited the fever outpatient clinic in the SEH between January 23 and April 30, 2020. We compared the characteristics of confirmed cases, including age, occupation, area, symptoms, laboratory results, and computed tomography (CT) scans, by month. Results: : By April 30, 4,699 patients had visited the fever outpatient clinic of the SEH;of those, 27 (0.57%) were confirmed COVID-19 cases. Among the confirmed domestic cases identified between January and February, four of five were from Wuhan, Hubei. Following the spread of the epidemic to other parts of the world, all confirmed cases identified in March–April were cases of individuals who were returning from abroad, mainly Chinese students living abroad. Further, all cases were from outside Shanghai, and no local residents were diagnosed in the clinic. Symptoms, laboratory tests, and CT scans were consistent with previous literature reports of positive COVID-19 cases. Conclusions: : Given the necessity to control the spread of this epidemic domestically and abroad, the focus of COVID-19 prevention and control has shifted. In Shanghai, measures taken to prevent COVID-19 spread were very successful. Early isolation and quarantine are necessary and effective measures.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310207

ABSTRACT

Background: A fever outpatient clinic at the south campus of Shanghai East Hospital (SEH) openedin response to the coronavirus disease (COVID-19) outbreak. We analyzed the data of all 11,972patients who visited the fever clinic and the 29 confirmed COVID-19 cases to determine the clinical and epidemiological characteristics of confirmed COVID-19 cases diagnosed at SEH. Methods: : Data were collected from all fever outpatient clinic patients between January 23 and September 30, 2020. We compared the characteristics of confirmed patients, including age, occupation, area, symptoms, laboratory results, and computed tomography (CT) findings, according to month. Results: : By September 30, 2020, 11,972 patients, including 29 (0.24%) confirmed COVID-19 cases, visited the clinic. Four of five confirmed domestic cases identified during January–February 2020 were from Wuhan (mainly elderly retirees and local employees), Hubei. After the epidemic spread internationally, all 22 confirmed cases identified during March–April 2020 were individuals who returned from abroad. They were predominantly young Chinese international students. The sporadic two confirmed cases during May–September 2020 included an employee returning to work from Hubei and an Indian servant from abroad. Symptoms, laboratory tests, and CT findings were consistent with previous reports of COVID-19-positive cases. Conclusions: : The characteristics of confirmed COVID-19 cases at SEH varied among different periods in response to the spread of the pandemic. However, due to the effective early isolation and quarantine measures, no outbreak occurred in SEH, which contributed to the prevention and control of the epidemic in Shanghai.

4.
NPJ Digit Med ; 5(1): 5, 2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1625359

ABSTRACT

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

5.
Preprint in English | medRxiv | ID: ppmedrxiv-21268370

ABSTRACT

Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e. self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. To accomplish such an investigation, a wide range of continuous non-invasive biomedical technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. With the novel technique described and exemplified in this paper, we were able to integrate signals from all instruments used in the assessment in a precisely synchronized fashion. We demonstrate that it is possible to visualize the interactions between all different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. The proposed multimodal data visualization can offer an overview of the functioning of the muscle pump during both supine rest and orthostatic recovery and can conduct comparison studies with signals from multiple participants at any given time in the assessment. This could help researchers and clinicians generate and test hypotheses based on the multimodal inspection of raw data, in long COVID and other clinical cohorts.

6.
Preprint in English | medRxiv | ID: ppmedrxiv-21268060

ABSTRACT

In this observational cross-sectional study, we investigated predictors of orthostatic intolerance (OI) in adults with long COVID. Participants underwent a 3-minute active stand (AS) with Finapres(R) NOVA, followed by a 10-minute unmedicated 70-degree head-up tilt test. 85 participants were included (mean age 46 years, range 25-78; 74% women), of which 56 (66%) reported OI during AS (OIAS). OIAS seemed associated with female sex, more fatigue and depressive symptoms, and greater inability to perform activities of daily living (ADL), as well as a higher heart rate (HR) at the lowest systolic blood pressure (SBP) point before the 1st minute post-stand (mean HRnadir: 88 vs 75 bpm, P=0.004). In a regression model also including age, sex, fatigue, depression, ADL inability, and peak HR after the nadir SBP, HRnadir was the only OIAS predictor (OR=1.09, 95% CI: 1.01-1.18, P=0.027). 22 participants had initial (iOH) and 5 classical (cOH) orthostatic hypotension, but neither correlated with OIAS. 71 participants proceeded to tilt, of which 28 had OI during tilt (OItilt). Of the 53 who had a 10-minute tilt, 7 (13%) fulfilled hemodynamic postural orthostatic tachycardia syndrome (POTS) criteria, but 6 did not report OItilt. OIAS was associated with a higher initial HR on AS, which after 1 minute equalized with the non-OIAS group. Despite these initial orthostatic HR differences, POTS was infrequent and largely asymptomatic. ClinicalTrials.gov Identifier: NCT05027724 (retrospectively registered on August 30, 2021).

7.
Eur Radiol ; 32(1): 205-212, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1293361

ABSTRACT

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
8.
Preprint in English | medRxiv | ID: ppmedrxiv-21259372

ABSTRACT

ObjectiveTo compare the two phases of long COVID, namely ongoing symptomatic COVID-19 (OSC; signs and symptoms from 4 to 12 weeks from initial infection) and post-COVID-19 syndrome (PCS; signs and symptoms beyond 12 weeks) with respect to symptomatology, abnormal functioning, psychological burden, and quality of life. DesignSystematic review. Data SourcesElectronic search of EMBASE, MEDLINE, ProQuest Coronavirus Research Database, LitCOVID, and Google Scholar between January and April 2021, and manual search for relevant citations from review articles. Eligibility CriteriaCross-sectional studies, cohort studies, randomised control trials, and case-control studies with participant data concerning long COVID symptomatology or abnormal functioning. Data ExtractionStudies were screened and assessed for risk of bias by two independent reviewers, with conflicts resolved with a third reviewer. The AXIS tool was utilised to appraise the quality of the evidence. Data were extracted and collated using a data extraction tool in Microsoft Excel. ResultsOf the 1,145 studies screened, 39 were included, all describing adult cohorts with long COVID and sample sizes ranging from 32 to 1,733. Studies included data pertaining to symptomatology, pulmonary functioning, chest imaging, cognitive functioning, psychological disorder, and/or quality of life. Fatigue presented as the most prevalent symptom during both OSC and PCS at 43% and 44%, respectively. Sleep disorder (36%; 33%), dyspnoea (31%; 40%), and cough (26%; 22%) followed in prevalence. Abnormal spirometry (FEV1 <80% predicted) was observed in 15% and 11%, and abnormal chest imaging observed in 34% and 28%, respectively. Cognitive impairments were also evident (20%; 15%), as well as anxiety (28%; 34%) and depression (25%; 32%). Decreased quality of life was reported by 40% of patients with OSC and 57% by those with PCS. ConclusionsThe prevalences of OSC and PCS were highly variable. Reported symptoms covered a wide range of body systems, with general overlap in frequencies between the two phases. However, abnormalities in lung function and imaging seemed to be more common in OSC, whilst anxiety, depression, and poor quality of life seemed more frequent in PCS. In general, the quality of the evidence was moderate and further research is needed to better understand the complex interplay of somatic versus psychosocial drivers in long COVID. Systematic Review RegistrationRegistered with PROSPERO with ID #CRD42021247846.

9.
Curr Psychol ; : 1-14, 2021 May 24.
Article in English | MEDLINE | ID: covidwho-1244624

ABSTRACT

During the COVID-19 pandemic in early 2020, domestic violence, interpersonal conflicts, and cyberbullying have risen sharply in China. We speculate that the perceived threat of COVID-19 is related to a general, non-target-specific aggressive tendency during the pandemic. We surveyed 1556 Chinese people in April 2020 (757 people in Hubei Province, the pandemic epicenter in China, and 799 in other regions of China where the pandemic is relatively not severe). A multiple-group structural equation modeling analysis found significant total effects between perceived threat of COVID-19 and aggressive tendencies during the pandemic in both regional groups, and the effect between them was mainly achieved through the mediating roles of sense of control and powerlessness during the pandemic. For all participants, negative coping strategies significantly aggravated the association between perceived threat of COVID-19 and aggressive tendencies during the pandemic, but the buffers were different across regions of outbreak severity. For participants in other regions where the pandemic is relatively not severe, positive coping strategies could mitigate the association between perceived threat of COVID-19 and aggressions. However, for participants in Hubei Province, the epicenter of China's pandemic, higher life satisfaction was more effective in buffering. These findings extend the possible consequences of the perceived COVID-19 threat and suggest that improving the life satisfaction of residents in areas with severe outbreaks is more effective in mitigating the adverse effects of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12144-021-01792-7.

10.
Urban Climate ; 38:100877, 2021.
Article in English | ScienceDirect | ID: covidwho-1244845

ABSTRACT

It is interesting and important to know what factors cause improvements in regional air quality. This study analyzed the factors that improved the air quality in cities in the Guanzhong region of China–in terms of meteorology and controlling emissions–following the implementation of the “Action Plan for Air Pollution Prevention and Control” in 2013. The average air quality index (AQI) values, PM2.5, PM10, SO2, CO, and NO2 in these cities in 2020 decreased by 45.1%, 43.8%, 82.9%, 57.3%, and 31.6%, respectively, compared to the values in 2014, while the O3 concentration increased by 16.7%. During the COVID-19 pandemic of February to May 2020, lockdown measures in cities in Guanzhong resulted in reductions of approximately 18.4%, 24.2%, and 17.9% in the AQI, PM2.5, and PM10 concentrations compared to the same period in 2019. Principal component analysis showed that the yearly reduction in AQI in cities in Guanzhong was attributed mainly to reductions in industrial emissions, followed by reductions in emissions from homes and motor vehicle exhausts. We propose the strengthening of measures to control particulate matter, O3 and greenhouse gas to see the improvement of air quality among this region in the future.

11.
Lancet Digit Health ; 3(5): e286-e294, 2021 05.
Article in English | MEDLINE | ID: covidwho-1152741

ABSTRACT

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.


Subject(s)
Artificial Intelligence , COVID-19/physiopathology , Prognosis , Radiography, Thoracic , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , United States , Young Adult
12.
Korean J Radiol ; 22(7): 1213-1224, 2021 07.
Article in English | MEDLINE | ID: covidwho-1143395

ABSTRACT

OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND METHODS: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. RESULTS: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. CONCLUSION: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.


Subject(s)
COVID-19/diagnosis , Machine Learning , Severity of Illness Index , Tomography, X-Ray Computed/methods , Critical Illness , Humans , Male , Middle Aged , ROC Curve , Retrospective Studies , SARS-CoV-2/pathogenicity
13.
Preprint in English | bioRxiv | ID: ppbiorxiv-181297

ABSTRACT

Since the end of 2019, COVID-19 pandemic caused by the SARS-CoV-2 emerged globally. The angiotensin-converting enzyme 2 (ACE2) on the cell surface is crucial for SARS-COV-2 entering into the cells. We use SARS-COV-2 pseudo virus and humanized ACE2 mice to mimic the possible transmitting of SARS-COV-2 through skin based on the data we found that skin ACE2 level is associated with skin pre-existing cutaneous conditions in human and mouse models and inflammatory skin disorders with barrier dysfunction increased the penetration of topical FITC conjugated spike protein into the skin. Our study indicated the possibility that the pre-existing cutaneous conditions could increase the risk for SARS-COV-2 infection. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/181297v4_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@14cef60org.highwire.dtl.DTLVardef@1f78c65org.highwire.dtl.DTLVardef@1224834org.highwire.dtl.DTLVardef@1b27475_HPS_FORMAT_FIGEXP M_FIG C_FIG

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