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2.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-337041

ABSTRACT

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with small sample sizes 1 or specific patient populations 2,3 limiting generalizability. This study aims to characterize PASC using the EHR data warehouses from two large national patient-centered clinical research networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) and 16.8 million patients in Florida respectively. With a high-throughput causal inference pipeline using high-dimensional inverse propensity score adjustment, we identified a broad list of diagnoses and medications with significantly higher incidence 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We found more PASC diagnoses and a higher risk of PASC in NYC than in Florida, which highlights the heterogeneity of PASC in different populations.

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-337039

ABSTRACT

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.

4.
Frontiers in psychology ; 13, 2022.
Article in English | EuropePMC | ID: covidwho-1837518

ABSTRACT

The COVID-19 outbreak has been a public health crisis of international concern, causing huge impact on people’s lives. As an important part of social public crisis management, how to quickly and effectively raise resources to participate in emergency relief in the era of self-media is a common challenge faced by global charitable organizations. This article attempts to use empirical evidence from Tencent charitable crowdfunding platform, the largest charitable crowdfunding platform in China, to answer this question. We consider 205 COVID-19 charitable projects and 11,177,249 donors to assess the process by which non-profit organizations raise funds through the information about project descriptions. Based on the effects of information and emotional framing, we explore the effects of the readability (i.e., complexity and understandability) and negative tone of the project description on fundraising amount. We then investigate the mediating role of forwarding times, as affective response to the text might explain forwarding times, which in turn affects money raised by increasing the visibility of the campaign. On this basis, the moderating role of recipient’s crisis involvement is tested during this process. The empirical results indicate that the complexity of the description will reduce the fundraising amount, while understandability and negative tone help to improve it. Furthermore, we found that forwarding times played an important mediating role in this process. Then the buffer effect of crisis involvement on the negative effect of complexity was validated, and its amplification on the positive effects of understandability was also verified.

5.
BMC Med Inform Decis Mak ; 22(1): 59, 2022 03 04.
Article in English | MEDLINE | ID: covidwho-1808363

ABSTRACT

BACKGROUND: Venous thromboembolism (VTE) risk assessment in surgical patients is important for the appropriate diagnosis and treatment of patients. The commonly used Caprini model is limited by its inadequate ability to discriminate between risk stratums on the surgical population in southwest China and lengthy risk factors. The purpose of this study was to establish an improved VTE risk assessment model that is accurate and simple. METHODS: This study is based on the clinical data from 81,505 surgical patients hospitalized in the Southwest Hospital of China between January 1, 2019 and June 18, 2021. Among the population, 559 patients developed VTE. An improved VTE risk assessment model, SW-model, was established through Logistic Regression, with comparisons to both Caprini and Random Forest. RESULTS: The SW-model incorporated eight risk factors. The area under the curve (AUC) of SW-model (0.807 [0.758, 0.853], 0.804 [0.765, 0.840]), are significantly superior (p = 0.001 and p = 0.044) to those of the Caprini (0.705 [0.652, 0.757], 0.758 [0.719, 0795]) on two test sets, but inferior (p < 0.001 and p = 0.002) to Random Forest (0.854 [0.814, 0.890], 0.839 [0.806, 0.868]). In decision curve analysis, within threshold range from 0.015 to 0.04, the DCA curves of the SW-model are superior to Caprini and two default strategies. CONCLUSIONS: The SW-model demonstrated a higher discriminative capability to distinguish VTE positive in surgical patients compared with the Caprini model. Compared to Random Forest, Logistic Regression based SW-model provided interpretability which is essential in guarantee the procedure of risk assessment transparent to clinicians.


Subject(s)
Venous Thromboembolism , Hospitalization , Humans , Retrospective Studies , Risk Assessment/methods , Risk Factors , Venous Thromboembolism/diagnosis , Venous Thromboembolism/epidemiology , Venous Thromboembolism/etiology
6.
Frontiers in pediatrics ; 10, 2022.
Article in English | EuropePMC | ID: covidwho-1781660

ABSTRACT

Objective The aim of this study was to assess the prognostic value of the lung ultrasound (LUS) score in patients with pediatric acute respiratory distress syndrome (pARDS) who received extracorporeal membrane oxygenation (ECMO). Methods A prospective cohort study was conducted in a pediatric intensive care unit (PICU) of a tertiary hospital from January 2016 to June 2021. The severe pARDS patients who received ECMO were enrolled in this study. LUS score was measured at initiation of ECMO (LUS-0 h), then at 24 h (LUS-24 h), 48 h (LUS-48 h), and 72 h (LUS-72 h) during ECMO, and when weaned from ECMO (LUS-wean). The value of LUS scores at the first 3 days of ECMO as a prognostic predictor was analyzed. Results Twenty-nine children with severe pARDS who received ECMO were enrolled with a median age of 26 (IQR 9, 79) months. The median duration of ECMO support was 162 (IQR 86, 273) h and the PICU mortality was 31.0% (9/29). The values of LUS-72 h and LUS-wean were significantly lower in survivors than that in non-survivors (both P < 0.001). Daily fluid balance volume during the first 3 days of ECMO support were strongly correlated with LUS score [1st day: r = 0.460, P = 0.014;2nd day: r = 0.540, P = 0.003;3rd day: r = 0.589, P = 0.001]. The AUC of LUS-72 h for predicting PICU mortality in these patients was 1.000, and the cutoff value of LUS-72 h was 24 with a sensitivity of 100.0% and a specificity of 100.0%. Furthermore, patients were stratified in two groups of LUS-72 h ≥ 24 and LUS-72 h < 24. Consistently, PICU mortality, length of PICU stay, ratio of shock, vasoactive index score value, and the need for continuous renal replacement therapy were significantly higher in the group of LUS-72 h ≥ 24 than in the group of LUS-72 h < 24 (all P < 0.05). Conclusion Lung ultrasound score is a promising tool for predicting the prognosis in patients with ARDS under ECMO support. Moreover, LUS-72 h ≥ 24 is associated with high risk of PICU mortality in patients with pARDS who received ECMO.

7.
Adv Sci (Weinh) ; 9(14): e2104333, 2022 05.
Article in English | MEDLINE | ID: covidwho-1782562

ABSTRACT

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.


Subject(s)
COVID-19 Vaccines , COVID-19 , Globulins , Animals , COVID-19/therapy , Globulins/therapeutic use , Humans , Immunization, Passive , Mice , SARS-CoV-2 , Spike Glycoprotein, Coronavirus
9.
Alexandria Engineering Journal ; 61(12):9661-9671, 2022.
Article in English | ScienceDirect | ID: covidwho-1763528

ABSTRACT

In this paper, we introduce a new class of statistical models to deal with the data sets in the sports and health sectors. The new class is called, a novel exponent power-Y (NovEP-Y) family of distributions. By implementing the NovEP-Y approach, a new model, namely, a novel exponent power-Weibull (NovEP-Weibull) distribution is introduced. Some distributional properties of the NovEP-Y family such as identifiability, order statistics, quantile function, and moments are obtained. The maximum likelihood estimators of the parameters are also derived. Furthermore, a brief Monto Carlo simulation study is conducted to evaluate the performances of the estimators. To show the applicability of the NovEP-Weibull model, two data sets from the sports and health sciences are considered. The first data set represents the time-to-even data collected from different football matches during the period 1964–2018. Whereas, the second data set is taken from the health sector, representing the survival times of the COVID-19 infected patients. Based on some well-known statistical tests, it is observed that the NovEP-Weibull model is a very competitive distribution for modeling the data sets in the sports and health sectors.

10.
BJPsych Open ; 8(2): e58, 2022 Mar 03.
Article in English | MEDLINE | ID: covidwho-1724711

ABSTRACT

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.

11.
Intell Med ; 2(1): 13-29, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1712703

ABSTRACT

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

12.
Diabet Med ; 39(5): e14815, 2022 05.
Article in English | MEDLINE | ID: covidwho-1703494

ABSTRACT

AIMS: To examine the association between baseline glucose control and risk of COVID-19 hospitalization and in-hospital death among patients with diabetes. METHODS: We performed a retrospective cohort study of adult patients in the INSIGHT Clinical Research Network with a diabetes diagnosis and haemoglobin A1c (HbA1c) measurement in the year prior to an index date of March 15, 2020. Patients were divided into four exposure groups based on their most recent HbA1c measurement (in mmol/mol): 39-46 (5.7%-6.4%), 48-57 (6.5%-7.4%), 58-85 (7.5%-9.9%), and ≥86 (10%). Time to COVID-19 hospitalization was compared in the four groups in a propensity score-weighted Cox proportional hazards model adjusting for potential confounders. Patients were followed until June 15, 2020. In-hospital death was examined as a secondary outcome. RESULTS: Of 168,803 patients who met inclusion criteria; 50,016 patients had baseline HbA1c 39-46 (5.7%-6.4%); 54,729 had HbA1c 48-57 (6.5-7.4%); 47,640 had HbA1c 58-85 (7.5^%-9.9%) and 16,418 had HbA1c ≥86 (10%). Compared with patients with HbA1c 48-57 (6.5%-7.4%), the risk of hospitalization was incrementally greater for those with HbA1c 58-85 (7.5%-9.9%) (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI] 1.06-1.34) and HbA1c ≥86 (10%) (aHR 1.40, 95% CI 1.19-1.64). The risk of COVID-19 in-hospital death was increased only in patients with HbA1c 58-85 (7.5%-9.9%) (aHR 1.29, 95% CI 1.06, 1.61). CONCLUSIONS: Diabetes patients with high baseline HbA1c had a greater risk of COVID-19 hospitalization, although association between HbA1c and in-hospital death was less consistent. Preventive efforts for COVID-19 should be focused on diabetes patients with poor glucose control.


Subject(s)
COVID-19 , Diabetes Mellitus, Type 2 , Diabetes Mellitus , Adult , Blood Glucose , COVID-19/complications , COVID-19/epidemiology , Diabetes Mellitus/epidemiology , Diabetes Mellitus, Type 2/complications , Glycated Hemoglobin A/analysis , Hospital Mortality , Hospitalization , Humans , Retrospective Studies , Risk Factors
13.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-319475

ABSTRACT

Background: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. Methods: : 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. Results: : Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values <0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. Conclusions: : Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

14.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316850

ABSTRACT

Background: Novel coronavirus (COVID-19) infection is a global public health issue and has now affected more than 70 countries worldwide. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with high risk of mortality. However, prognostic factors assessing poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain unclear. Methods: : We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the of end of follow up period was the primary outcome. Prognostic factors for mortality were also assessed and a prognostic model was developed, calibrated and validated. Results: : The study included 492 patients with SARS-CoV-2, which were divided into three cohorts, the training cohort (n=237), the validation cohort 1 (n=120), and the validation cohort 2 (n=135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including age, odds ratio (OR), 1.1 / years increase (p<0.001);neutrophil-to-lymphocyte ratio OR, 1.14 (p<0.001), body temperature on admission OR, 1.53 / °C increase (p=0.005), increase of aspartate transaminase OR, 2.47 (p=0.019), and decrease of total protein OR, 1.69 (p=0.018).Furthermore, the prognostic model drawn from the training cohort was validated with the validation cohort 1 and 2 with comparable area under curve (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell’s C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. Conclusions: : A validated prognostic model was developed to assist in determining the clinical prognosis for SARS- CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted in the low risk group had a high probability of survival.

15.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316849

ABSTRACT

Background: Elderly patients infected with COVID-19 are reported to be facing a substantially increased risk of mortality. Clinical characteristics, treatment options, and potential survival factors remain under investigation. This study aimed to fill this gap and provide clinically relevant factors associated with survival of elderly patients with COVID-19.MethodsIn this multi-center study, elderly patients (age ≥65 years old) with laboratory-confirmed COVID-19 from 4 Wuhan hospitals were included. The clinical end point was hospital discharge or deceased with last date of follow-up on Mar. 08, 2020. Clinical, demographic, and laboratory data were collected.Univariate and multivariate analysis were performed to analyze survival and risk factors. A metabolic flux analysis using a large-scale molecular model was applied to investigate the pathogenesis of SARS- CoV-2 with regard to metabolism pathways.ResultsA total of 223 elderly patients infected with COVID-19 were included, 91 (40.8%) were discharged and 132 (59.2%) deceased. Acute respiratory distress syndrome (ARDS) developed in 140 (62.8%) patients, 23 (25.3%) of these patients survived. Multivariate analysis showed that potential risk factors were D- Dimer (odds ratio: 1.13 [95% CI 1.04 - 1.22], p=.005), immune-related metabolic index (6.42 [95% CI 2.66 - 15.48], p<.001), and neutrophil-to-lymphocyte ratio (1.08 [95% 1.03 - 1.13], p<.001). Elderly patients receiving interferon atmotherapy showed an increased probability of survival (0.29 [95% CI0.17 - 0.51], p<.001). Based on these factors, an algorithm (AlgSurv) was developed to predict survival for elderly patients. The metabolic flux analysis showed that 12 metabolic pathways including phenylalanine (odds ratio: 28.27 [95% CI 10.56 - 75.72], p<0.001), fatty acid (15.61 [95% CI 6.66 - 36.6], p<0.001), and pyruvate (12.86 [95% CI 5.85 - 28.28], p<0.001) showed a consistently lower flux in the surviving versus the deceased subgroup. This may reflect a key pathogenesis of COVID-19 infection.ConclusionAlthough a high mortality has been reported for elderly patients with COVID-19, in this analysis, several factors such as interferon atmotherapy and activity of metabolic pathways were found to be associated with survival of elderly patients. Based on these findings, the survival algorithm (AlgSurv) was developed to assist the clinical stratification for elderly patients. Deregulation of metabolic pathways revealed in this study may aid in the drug development against COVID-19.

16.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315725

ABSTRACT

Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: : A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: : In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: : Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

17.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315683

ABSTRACT

Background: The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain. Methods: Based on the reported cases, the effective reproduction number (B) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between a and NPIs through a generalized linear model (GLM). Results: Different NPIs were found to have led to different levels of reduction in c. Stay-at-home contributed approximately 51% (95% CI 46%-57%), wearing (face) masks 29% (15%-42%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel restriction 11% (5%-16%), school closure 10% (7%-14%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 7% (2%-11%). Conclusions: : This retrospective assessment of NPIs on k has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.

18.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-312623

ABSTRACT

Essential workers such as medical workers and police officers have been playing crucial roles in the fight against the COVID-19 pandemic, and are under heavy stress both physically and mentally. The goal of the present study was to develop a novel nature-based intervention to promote their well-being. A representative sample of essential workers in China was recruited for a five-day intervention program, and were randomly assigned to two groups. The experimental group watched two-minute video clips of natural scenes every day, while the control group watched urban scenes. Results indicated that after five days, the natural stimuli intervention yielded overall improvements in various indices of subjective well-being. Furthermore, analyses of nested longitudinal data confirmed that everyday nature stimuli exposure provided both immediate and cumulative restorative benefits. The proposed natural-based intervention is brief and easy-to-use, offering a cost-efficient psychological booster to promote subjective well-being of essential workers during this crisis time.

19.
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.

20.
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.

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