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1.
Journal of Affective Disorders ; 2022.
Article in English | ScienceDirect | ID: covidwho-1796594

ABSTRACT

Background The COVID-19 pandemic could increase the number of older adults who are socially isolated including community-dwelling older adults, and result in the secondary damage of mental health. This study aimed to examine the longitudinal association between social isolation transitions and psychological distress among the community-dwelling older adults before and during the COVID-19 pandemic in rural China. Methods A total of 2749 community-dwelling older adults aged 60 years and older in rural Shandong, China were included. We used the generalized estimating equations (GEE) model to estimate the impact of social isolation transitions on psychological distress before and during the COVID-19 pandemic. Results The percentage of high and very high psychological distress (K10 ≥ 22) was 23.54% and 31.36% before and during the COVID-19 pandemic, respectively, indicating a 7.82% increase (P < 0.001). Compared with the group remaining nonisolated, “became socially isolated” and “remained isolated” groups were more likely to have a deterioration of psychological distress after experiencing the COVID-19 pandemic (became socially isolated: b = 0.92, P < 0.001;remained isolated: b = 0.98, P < 0.001). Limitations The main variables in this study were measured by self-report information, which might result in recall bias. Conclusions During the COVID-19 pandemic, psychological distress increased among the community-dwelling older adults in rural China. There was a significant risk of psychological distress among those who had transitioned from nonisolation before the pandemic to social isolation after experiencing the pandemic, thus intervention on social isolation process during the pandemic may be important to protect older adults' mental health.

2.
Sci Rep ; 12(1): 6053, 2022 Apr 11.
Article in English | MEDLINE | ID: covidwho-1784024

ABSTRACT

Facing the emerging COVID viral variants and the uneven distribution of vaccine worldwide, imported pre-symptomatic COVID-19 cases play a pivotal role in border control strategies. A stochastic disease process and computer simulation experiments with Bayesian underpinning was therefore developed to model pre-symptomatic disease progression during incubation period on which we were based to provide precision strategies for containing the resultant epidemic caused by imported COVID-19 cases. We then applied the proposed model to data on 1051 imported COVID-19 cases among inbound passengers to Taiwan between March 2020 and April 2021. The overall daily rate (per 100,000) of pre-symptomatic COVID-19 cases was estimated as 106 (95% credible interval (CrI): 95-117) in March-June 2020, fell to 37 (95% CrI: 28-47) in July-September 2020 (p < 0.0001), resurged to 141 (95% CrI: 118-164) in October-December 2020 (p < 0.0001), and declined to 90 (95% CrI: 73-108) in January-April 2021 (p = 0.0004). Given the median dwelling time, over 82% cases would progress from pre-symptomatic to symptomatic phase in 5-day quarantine. The time required for quarantine given two real-time polymerase chain reaction (RT-PCR) tests depends on the risk of departing countries, testing and quarantine strategies, and whether the passengers have vaccine jabs. Our proposed four-compartment stochastic process and computer simulation experiments design underpinning Bayesian MCMC algorithm facilitated the development of precision strategies for imported COVID-19 cases.


Subject(s)
COVID-19 , Quarantine , Bayes Theorem , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Humans , SARS-CoV-2 , Taiwan/epidemiology
3.
Sustainability ; 14(7):4361, 2022.
Article in English | MDPI | ID: covidwho-1776347

ABSTRACT

The unexpected pandemic has provoked changes in all economic sectors worldwide. COVID-19 has had a direct and indirect effect on countries' development. Thus, the pandemic limits the movements of labour forces among countries, restricting migrants' remittances. In addition, it provokes the reorientation of consumer behaviour and changes in household expenditure. For developing countries, migrant remittances are one of the core drivers for improving household wellbeing. Therefore, the paper aims to analyse how the COVID-19 pandemic has affected household expenditure in Ukraine, as being representative of a developing country. For this purpose, the data series were compiled for 2010 to the second quarter of 2021. The data sources were as follows: Ministry of Finance of Ukraine, The World Bank, and the State Statistics Service of Ukraine. The core variables were as follows: migrants' remittances and expenditure of households by the types. The following methods were applied to achieve the paper's aims: the Dickey–Fuller Test Unit Root and the ARIMA model. The findings confirmed that COVID-19 has changed the structure of household expenditure in Ukraine. Considering the forecast of household expenditure until 2026, it was shown that due to changes in migrants' remittances, household expenditure in all categories tends to increase. The forecasted findings concluded that household expenditure on transport had the most significant growth due to changing migrants' remittances.

4.
Metabolism ; 131: 155196, 2022 Mar 31.
Article in English | MEDLINE | ID: covidwho-1768409

ABSTRACT

BACKGROUND: Diabetes is an independent predictor of poor outcomes in patients with COVID-19. We compared the effects of the preadmission use of antidiabetic medications on the in-hospital mortality of patients with COVID-19 having type 2 diabetes. METHODS: A systematic search of PubMed, EMBASE, Scopus and Web of Science databases was performed to include studies (except case reports and review articles) published until November 30, 2021. We excluded papers regarding in-hospital use of antidiabetic medications. We used a random-effects meta-analysis to calculate the pooled OR (95% CI) and performed a sensitivity analysis to confirm the robustness of the meta-analyses. MAIN FINDINGS: We included 61 studies (3,061,584 individuals), which were rated as having low risk of bias. The OR (95% CI) indicated some medications protective against COVID-related death, including metformin [0.54 (0.47-0.62), I2 86%], glucagon-like peptide-1 receptor agonist (GLP-1RA) [0.51 (0.37-0.69), I2 85%], and sodium-glucose transporter-2 inhibitor (SGLT-2i) [0.60 (0.40-0.88), I2 91%]. Dipeptidyl peptidase-4 inhibitor (DPP-4i) [1.23 (1.07-1.42), I2 82%] and insulin [1.70 (1.33-2.19), I2 97%] users were more likely to die during hospitalization. Sulfonylurea, thiazolidinedione, and alpha-glucosidase inhibitor were mortality neutral [0.92 (95% CI 0.83-1.01, I2 44%), 0.90 (95% CI 0.71-1.14, I2 46%), and 0.61 (95% CI 0.26-1.45, I2 77%), respectively]. The sensitivity analysis indicated that our findings were robust. CONCLUSIONS: Metformin, GLP-1RA, and SGLT-2i were associated with lower mortality rate in patients with COVID-19 having type 2 diabetes. DPP-4i and insulin were linked to increased mortality. Sulfonylurea, thiazolidinedione, and alpha-glucosidase inhibitors were mortality neutral. These findings can have a large impact on the clinicians' decisions amid the COVID-19 pandemic.

5.
Ann Palliat Med ; 2022 Mar 07.
Article in English | MEDLINE | ID: covidwho-1743091

ABSTRACT

BACKGROUND: Sleep disturbance is well documented as a crucial element that impairs health. Depression and health-related quality of life (HRQOL), which on behalf of a patient's overall perception of emotional, physical and social well-being, are increasingly emphasized self-reported health outcomes especially during the coronavirus disease 2019 (COVID-19) pandemic. Among dialysis patients, sleep disturbance is associated with depression and poorer HRQOL. The study was designed to depict the prevalence of sleep disturbance, and to explore the association among sleep, depression, and HRQOL in patients with non-dialysis chronic kidney disease (CKD) during the COVID-19 pandemic. METHODS: A total of 172 non-dialysis CKD patients enrolled in this cross-sectional study, with sociodemographic and clinical data recorded. Sleep, HRQOL, and depression were evaluated via the Pittsburgh Sleep Quality Index (PSQI), the Kidney Disease Quality of Life 36-Item Short-Form Survey (KDQOL-36), and the 9-item Patient Health Questionnaire (PHQ-9), respectively. RESULTS: A total of 100 (58%) met the criteria for poor sleep. Good sleepers had strikingly disparate HRQOL and depression scores compared to poor sleepers. Sleep disorders were significantly associated with decreased HRQOL and increased depression in regression models adjusted or unadjusted for sociodemographic and clinical characteristics. Mediation analysis indicated depression was a significant mediator explaining 51% of the relationship between sleep status with physical component summary (PCS) and played a fully mediating role in the association between sleep and mental component summary (MCS). CONCLUSIONS: Our study suggested the high incidence of sleep disorders in patients with non-dialysis CKD during the COVID-19 pandemic, as well as the tight associations among sleep, depression, and HRQOL. Considering the negative influences of sleep and depression on HRQOL, appropriate screening and treatment for these treatable health-related domains are necessary for patients with non-dialysis CKD.

6.
PLoS One ; 17(3): e0264484, 2022.
Article in English | MEDLINE | ID: covidwho-1736510

ABSTRACT

Companies developing automated driving system (ADS) technologies have spent heavily in recent years to conduct live testing of autonomous vehicles operating in real world environments to ensure their reliable and safe operations. However, the unexpected onset and ongoing resurgent effects of the Covid-19 pandemic starting in March 2020 has serve to halt, change, or delay the achievement of these new product development test objectives. This study draws on data obtained from the California automated vehicle test program to determine the extent that testing trends, test resumptions, and test environments have been affected by the pandemic. The importance of government policies to support and enable autonomous vehicles development during pandemic conditions is highlighted.


Subject(s)
Automation/methods , Mechanical Tests/methods , Accidents, Traffic/prevention & control , Accidents, Traffic/trends , Automation/economics , Automobile Driving/statistics & numerical data , COVID-19/economics , California , Humans , Mechanical Tests/economics , User-Centered Design
7.
JAMA Neurol ; 2022 Mar 08.
Article in English | MEDLINE | ID: covidwho-1729079

ABSTRACT

Importance: Determining the long-term impact of COVID-19 on cognition is important to inform immediate steps in COVID-19 research and health policy. Objective: To investigate the 1-year trajectory of cognitive changes in older COVID-19 survivors. Design, Setting, and Participants: This cohort study recruited 3233 COVID-19 survivors 60 years and older who were discharged from 3 COVID-19-designated hospitals in Wuhan, China, from February 10 to April 10, 2020. Their uninfected spouses (N = 466) were recruited as a control population. Participants with preinfection cognitive impairment, a concomitant neurological disorder, or a family history of dementia were excluded, as well as those with severe cardiac, hepatic, or kidney disease or any kind of tumor. Follow-up monitoring cognitive functioning and decline took place at 6 and 12 months. A total of 1438 COVID-19 survivors and 438 control individuals were included in the final follow-up. COVID-19 was categorized as severe or nonsevere following the American Thoracic Society guidelines. Main Outcomes and Measures: The main outcome was change in cognition 1 year after patient discharge. Cognitive changes during the first and second 6-month follow-up periods were assessed using the Informant Questionnaire on Cognitive Decline in the Elderly and the Telephone Interview of Cognitive Status-40, respectively. Based on the cognitive changes observed during the 2 periods, cognitive trajectories were classified into 4 categories: stable cognition, early-onset cognitive decline, late-onset cognitive decline, and progressive cognitive decline. Multinomial and conditional logistical regression models were used to identify factors associated with risk of cognitive decline. Results: Among the 3233 COVID-19 survivors and 1317 uninfected spouses screened, 1438 participants who were treated for COVID-19 (691 male [48.05%] and 747 female [51.95%]; median [IQR] age, 69 [66-74] years) and 438 uninfected control individuals (222 male [50.68%] and 216 female [49.32%]; median [IQR] age, 67 [66-74] years) completed the 12-month follow-up. The incidence of cognitive impairment in survivors 12 months after discharge was 12.45%. Individuals with severe cases had lower Telephone Interview of Cognitive Status-40 scores than those with nonsevere cases and control individuals at 12 months (median [IQR]: severe, 22.50 [16.00-28.00]; nonsevere, 30.00 [26.00-33.00]; control, 31.00 [26.00-33.00]). Severe COVID-19 was associated with a higher risk of early-onset cognitive decline (odds ratio [OR], 4.87; 95% CI, 3.30-7.20), late-onset cognitive decline (OR, 7.58; 95% CI, 3.58-16.03), and progressive cognitive decline (OR, 19.00; 95% CI, 9.14-39.51), while nonsevere COVID-19 was associated with a higher risk of early-onset cognitive decline (OR, 1.71; 95% CI, 1.30-2.27) when adjusting for age, sex, education level, body mass index, and comorbidities. Conclusions and Relevance: In this cohort study, COVID-19 survival was associated with an increase in risk of longitudinal cognitive decline, highlighting the importance of immediate measures to deal with this challenge.

8.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324815

ABSTRACT

The 2019 novel SARS-like coronavirus (SARS-CoV-2) entry depends on the host membrane serine protease TMPRSS2, which can be blocked by some clinically-proven drugs. Here we analyzed spatial relevance between glycosylation sequons and antibody epitopes and found that, different from SARS-CoV S, most high-surface-accessible epitopes of SARS-CoV-2 S are blocked by the glycosylation, and the optimal epitope with the highest surface accessibility is covered by the S1 cap. TMPRSS2 inhibitor treatments may prevent unmasking of this epitope and therefore prolong virus clearance and may induce antibody-dependent enhancement. Interestingly, a heparin-binding sequence immediately upstream of the S1/S2 cleavage site has been found in SARS-CoV-2 S but not in SARS-CoV S. Binding of SARS-CoV-2 with heparins may lead to exposure of S686, which then facilitates the S1/S2 cleavage, induces exposure of the optimal epitope, and therefore increases the antibody titres. A combination of heparin and vaccine (or convalescent serum) treatments thus is recommended.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324619

ABSTRACT

Using data on imported and domestic COVID-19 cases from Taiwan and New Zealand between January and June 2020, we develop a Bayesian random-effects Poisson model to detect cluster infections from imported cases. We find remarkable consistency in the predictive power of the model. An increase in one imported case increased the risk of domestic cases by 9.54% in Taiwan and 10.97% in New Zealand. The Taiwan epidemic curve revealed that imported cases did not lead to a large-scale community-acquired outbreak. In New Zealand, a community-acquired outbreak during 29th March-4th April could have been averted if control actions had been taken one-week earlier prior to the predicted cluster infection between 22nd and 28th March. Our model can be used as an early warning of outbreaks during the initial stage of pandemic or the resurgence of outbreaks after lifting containment measures, such as lockdown orders and border control, during a pandemic.

10.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319505

ABSTRACT

Background: The effects of temperature and humidity on the epidemic growth of coronavirus disease 2019 (COVID-19)remains unclear. Methods: : Daily scatter plots between the epidemic growth rate (GR) and average temperature (AT) or average relative humidity (ARH) were presented with curve fitting through the “loess” method. The heterogeneity across days and provinces were calculated to assess the necessity of using a longitudinal model. Fixed effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. Results: : An increased AT dramatically reduced the GR when the AT was lower than −5°C, the GR was moderately reduced when the AT ranged from −5°C to 15°C, and the GR increased when the AT exceeded 15°C. An increasedARH increased theGR when the ARH was lower than 72% and reduced theGR when the ARH exceeded 72%. Conclusions: : High temperatures and low humidity may reduce the GR of the COVID-19 epidemic. The temperature and humidity curves were not linearly associated with the COVID-19 GR.

11.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315710

ABSTRACT

Background: Understanding the long-term effects of coronavirus disease 2019 (COVID-19) on cognitive function is essential for the prevention of cognitive decline in elderly population. This study aims to assess cognitive status and longitudinal decline at 6 months post-infection in elderly patients recovered from COVID-19.Methods: This cross-sectional study recruited 1013 COVID-19 inpatients aged over 60 years who were discharged from three COVID-19-designated hospitals in Wuhan, China, from February 10 to March 13, 2020. In total, 262 uninfected living spouses of COVID-19 patients were selected as controls. Subjects were examined for their current cognitive status using a Chinese version of the Telephone Interview of Cognitive Status-40 (TICS-40) and longitudinal cognitive decline using an Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Cognitive assessments were performed 6 months after patient discharge.Findings: COVID-19 patients had significantly lower TICS-40 scores (patients: 29.73±6.13;controls: 30.74±5.95, p=0.016) and higher IQCODE scores (patients: 3.40±0.81;controls: 3.15±0.39, p<0.001) than the controls. Severe COVID-19 patients had lower TICS-40 scores and higher IQCODE scores than non-severe COVID-19 patients (TICS-40: 22.98±7.12 vs. 30.46±5.53, p<0.001;IQCODE: 4.06±1.39 vs. 3.33±0.68, p<0.001) and controls (TICS-40: 22.98±7.12 vs. 30.74±5.95, p<0.001;IQCODE: 4.06±1.39 vs. 3.15±0.39, p<0.001). Severe COVID-19 patients had a higher proportion of cases with a current cognitive impairment and longitudinal cognitive decline than non-severe COVID-19 patients and controls. COVID-19 severity (OR: 8.142, 95% CI: 5.007-13.239) was associated with worse current cognitive function. Older age (OR: 1.024, 95% CI: 1.003 to 1.046), COVID-19 severity (OR: 2.277, 95% CI: 1.308 to 3.964), mechanical ventilation (OR: 5.388, 95% CI: 3.007 to 9.656), and hypertension (OR: 1.866, 95% CI: 1.376 to 2.531) were associated with an increased risk of longitudinal cognitive decline.Interpretation: SARS-CoV-2 infection is associated with delayed cognitive decline in elderly population. COVID-19 patients with risk factors, including severe disease, older age, mechanical ventilation, and hypertension, should be intensively monitored for delayed cognitive decline. Funding: National Natural Science Foundation of China.Conflict of Interest: We declared no conflict of interests.Ethical Approval: The study protocols were approved by the institutional review boards of the hospitals. Verbal informed consent was obtained from all participants prior to the survey.

12.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-310363

ABSTRACT

Objective: to analyze the epidemic situation of COVID-19 in northeast China, Liaoning and Jilin. To study the prevalence of COVID-19 virus in areas other than Hubei province. To understand the spread of COVID-19 in Liaoning and Jilin provinces by means of communication network. More in-depth understanding of COVID-19 epidemic, and put forward effective prevention and control recommendations. Methods: We collected the demographic characteristics, exposure history and course of action of patients with laboratory-confirmed infection with COVID-19 published by Liaoning Provincial Health Commission and Jilin Provincial Health Commission as of February 15, 2020. We describe the demographic characteristics, case characteristics, spatial distribution characteristics and related interpersonal network of these patients. To analyze the transmission of COVID-19 in two provinces. Results: : By February 15, 2020, the cumulative number of infected people in Liaoning province was 119.The largest number was 27(22.7%) in Shenyang and the smallest in Fushun, with no reported cases of infection. Among them, 55(46.2%) have a history of sojourning in Hubei province. The mainly clinical symptoms of the infected patients were fever, and 67(56.3%) of them developed fever at the time of diagnosis. Cough, sneezing and other respiratory symptoms are less. The cumulative number of infected people in Jilin province was 89, with the highest number in Changchun city at 39(43.8%) and the lowest in Baishan city, with no reported infections. 21(23.6%) people with a history of sojourning in Hubei province. Most of those infected in the two provinces were related to Hubei province, and most of those infected in the second generation or more were infected by close contact with relatives. Conclusion: The COVID-19 outbreaks in Liaoning and Jilin provinces are gradually stabilizing, but have not yet reached the time required to lower the prevention and control level. The fatality rate of the two provinces is relatively low. There is no evidence of super-spreader in either province.

13.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308192

ABSTRACT

Background: The outbreak of sever acute respiratory syndrome coronavirus 2(SARS-CoV-2) has become a great threat to the world. No study has been done on the mild or asymptomatic SARS-CoV-2 in a family cluster. Methods: : We report the epidemiological, clinical, laboratory, radiological, and clinical outcomes of five patients in a family cluster. Results: : We enrolled a family of five patients who was confirmed with SARS-CoV-2 infection. One of them worked in Wuhan and returned to Danzhou, Hainan on January 22,2020. The other four family members, who did not travel to Wuhan, became infected with the virus after several days of contact with the family member. Five family members (aged 33–57years) presented with fever, cough or no symptom onset. Three of them had negative nucleic test on first swab sampling. One of them was not confirmed until the third nucleic acid test. Two of them had radiological ground-glass lung opacities. Two patients presenting with fever had lymphopenia or decreased white blood cells. No one had increased C-reactive protein or lactate dehydrogenase levels. After treatment, they were discharged. Conclusions: : Person-to-person transmission of SARS-CoV-2 was confirmed in family setting. Concerns should be raised for the asymptomatic persons in a family cluster.

14.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308191

ABSTRACT

Background: Confirmed cases of coronavirus disease 2019 (COVID-19) is still increasing, detailed analysis of confirmed cases may be beneficial for disease control. Methods: : To describe the clinical and radiological findings of patients confirmed with COVID-19 infection in Haikou, China. Results: : A total of 67 patients confirmed with COVID-19 infection were included in this study. 50 were imported cases. Most infected patients presented with fever and cough. The typical CT findings of lung lesions were bilateral, multifocal lung lesions (52[78%]), with subpleural distribution, and more than two lobes involved (51[78%]). 54 (81%) patients of COVID-19 pneumonia had ground glass opacities. Consolidation was in 30 (45%) patients, crazy paving pattern or interlobular thickening in 17 (25%), adjacent pleura thickening in 23 (34%) patients. Additionally, baseline chest CT did not reveal positive CT findings in 7 patients (23%), but 3 patients presented unilateral ground glass opacities at follow-up. Importantly, the follow-up CT findings were fitted well with the clinical outcomes. Conclusions: : Chest CT could be used as an important tool for early diagnosis of COVID-19, monitoring the disease evolution, judging the treatment effectiveness and predicting the clinical outcomes.

15.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308190

ABSTRACT

To describe the clinical and radiological findings of patients confirmed with 2019 novel coronavirus disease (COVID-19) infection in Haikou, China. A total of 67 patients confirmed with COVID-19 infection were included in this study. 50 were imported cases. Most infected patients presented with fever and cough. The typical CT findings of lung lesions were bilateral, multifocal lung lesions (52[78%]), with subpleural distribution, and more than two lobes involved (51[78%]). 54 (81%) patients of COVID-19 pneumonia had ground glass opacities. Consolidation was in 30 (45%) patients, crazy paving pattern or interlobular thickening in 17 (25%), adjacent pleura thickening in 23 (34%) patients. Additionally, baseline chest CT did not reveal positive CT findings in 7 patients (23%), but 3 patients presented unilateral ground glass opacities at follow-up. Importantly, the follow-up CT findings were fitted well with the clinical outcomes.

16.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308189

ABSTRACT

Objective: To elucidate the consistency between CT findings and real time reverse-transcription–polymerase chain- reaction (RT-PCR) results and investigate the relationship between CT features and clinical prognosis in COVID-19. Methods: : The clinical manifestations, laboratory parameters and CT imaging findings were analyzed in thirty-four patients with COVID-19 confirmed by RT-PCR from January 20 to February 4 in Hainan province. CT score was compared between the discharged patients and ICU patients. Results: : Fever (85%) and cough (79%) were most commonly seen. 10 (29%) patients demonstrated negative results on their first RT-PCR.22/34(65%) patients showed pure ground glass opacity (GGO). 17/34 (50%) patients had five lobes of lung involvement, while the 23(68%) patients had lower lobes were involved and 24/34 (71%) were subpleural. Lesions of 24 (71%) patients were distributed mainly in the subpleural. During follow-up, the initial CT lesions of ICU patients are distributed in both subpleural and parenchyma (80%) and the lesions are scattered. 60% of ICU patients had five lobes involved, while this was seen in only 25% discharged patients. Lesions of discharged patients are mainly in the subpleural (75%). 62.5% of discharged patients showed pure ground-glass opacity. 80% ICU demonstrated progressive stage on their first CT scan. 75 % discharged patients were at an early stage. CT score of ICU patients were significantly higher than that of the discharged patients. Conclusion: Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with negative RT-PCR. The initial features in CT may be associated with prognosis.Authors Hui Juan Chen and Jie Qiu contributed equally to this work.

17.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308188

ABSTRACT

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19). Methods: : In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the region of interest (ROI), and the radiomic features were extracted. The Support Vector Machine(SVM) model was built on the combination of the 4 groups of features, including radiomic features, traditional radiological features, quantifying features and clinical features, by repeated cross-validation procedure and the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: : For the SVM model that built on the combination of 4 groups of features(integrated model), the per-exam AUC of 0.925(95% CI: 0.856 to 0.994) was reached for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816(95% CI: 0.651 to 0.917) and 0.923(95% CI: 0.621 to 0.996), respectively. For the SVM models that built on radiomic features, radiological features, quantifying features and clinical features individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607 and 0.739 respectively, significantly lower than the integrated model, except for the radiomic model. Conclusion: The machine learning-based CT radiomics models may accurately detect COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

18.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-308187

ABSTRACT

Purpose: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19). Methods: In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results: For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion: The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.

19.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325222

ABSTRACT

BACKGROUND: Previous study suggested that Chinese Herbal Medicine (CHM) Formula Huashibaidu granule might shorten disease course of Corona Virus Disease 2019 (COVID-19) patients. Our research aims to investigate the early treatment effect of Huashibaidu granule in mild COVID-19 patients under well clinical management.METHODS: An unblended cluster-randomized clinical trial was conducted at the Dongxihu FangCang hospital. 2 cabins were randomly allocated to CHM or control group, with 204 randomly sampled mild COVID-19 patients in each cabin. All participants received a 7-day conventional treatment, and CHM group cabin used additional Huashibaidu granule 10g twice daily. Participants were followed up until they met clinical endpoint. The primary outcome was patient become worsening before clinical endpoint occurred. The secondary outcomes was discharge with cure before clinical endpoint occurred and relief of composite symptoms after 7 days treatment.FINDINGS: All 408 participants were followed up to meet clinical endpoint and included in statistical analysis. The baseline characteristics were comparable between 2 groups. The number of worsening patients in the CHM group was 5 (2.5%), and that in the control group was 16 (7.8%). There was a significant difference between groups (P=0.014). 8 foreseeable mild adverse events occurred without statistical difference between groups.INTERPRETATION: 7-day early treatment with Huashibaidu granule reduced worsening conversion of mild COVID-19 patients. Our study supports Huashibaidu Granule as an active option for early treatment of mild COVID-19 in similar medical locations with well management.TRIAL REGISTRATION: The Chinese Clinical Trial Registry: ChiCTR2000029763.FUNDING: This study was supported by “National Key R&D Program of China” (No.2020YFC0841500).DECLARATION OF INTERESTS: The authors guaranteed that there existed no competing interest in this paper.ETHICS APPROVAL STATEMENT: Ethics Review Committee of Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences Approval of Ethical Review Acceptance Number: S2020-001;Approval Number: P20001/PJ01.

20.
Health Sci Rep ; 5(1): e477, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1669425

ABSTRACT

The use of wearable photoplethysmography (PPG) technology for estimating heart rate (HR) and HR variability (HRV) in the health care system is gaining attention in recent years. However, the performance of these devices remains questionable in their ability to collect data in real working conditions for long-term monitoring. The present study aimed to examine the data collected from nurses during working hours by PPG and electrocardiography (ECG) devices. Twenty-two nurses underwent a 60-minute work protocol during the normal working conditions while wearing a PPG device and an ECG device. HR, low-frequency component (LF) and high-frequency component (HF), LF/HF ratio, and percent LF distribution in total spectral power, and steps were examined. Pearson's correlation analysis and Bland-Altman method was performed to examine the relationships between the two devices based on HR and HRV indices. The results found strong positive correlations between HR estimates of both devices, and moderate correlations between LF/HF ratio and percent LF indices estimates, respectively. Moreover, the Bland-Altman analysis showed a small mean bias in general between the captured data of both devices. This pilot study suggested that the PPG device appears to demonstrate good overall reliability in measuring HR, LF/HF ratio, and percent LF. A further large-scale study is required to investigate the feasibility and practicality for HR and HRV analysis in nurses during real working conditions using PPG devices.

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