Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China's Guizhou.

China has implemented a series of long-term measures to control the spread of COVID-19, however, the effects of these measures on other chronic and acute respiratory infectious diseases remain unclear. Tuberculosis (TB) and scarlet fever (SF) serve as representatives of chronic and acute respiratory infectious diseases, respectively. In China's Guizhou province, an area with a high prevalence of TB and SF, approximately 40,000 TB cases and hundreds of SF cases are reported annually. To assess the impact of COVID-19 prevention and control on TB and SF in Guizhou, the exponential smoothing method was employed to establish a prediction model for analyzing the influence of COVID-19 prevention and control on the number of TB and SF cases. Additionally, spatial aggregation analysis was utilized to describe spatial changes in TB and SF before and after the COVID-19 outbreak. The parameters of the TB and SF prediction models are R2 = 0.856, BIC = 10.972 and R2 = 0.714, BIC = 5.325, respectively. TB and SF cases declined rapidly at the onset of COVID-19 prevention and control measures, with the number of SF cases decreasing for about 3-6 months and the number of TB cases remaining in decline for 7 months after the 11th month. The spatial aggregation of TB and SF did not change significantly before and after the COVID-19 outbreak but exhibited a marked decrease. These findings suggest that China's COVID-19 prevention and control measures also reduced the prevalence of TB and SF in Guizhou. These measures may have a long-term positive impact on TB, but a short-term effect on SF. Areas with high TB prevalence may continue to experience a decline due to the implementation of COVID-19 preventive measures in the future.

The Effect of Work Engagement and Perceived Organizational Support on Turnover Intention among Nurses: A Meta-Analysis Based on the Price–Mueller Model

Aim. To elaborate on the relationship between work engagement, perceived organizational support, and the turnover intention of nurses by analysing some potential moderators. Background. Nurses' turnover intention is negatively impacted by their level of work engagement and perceptions of organizational support. However, it is challenging to reach a consistent conclusion. Methods. Data were acquired from six electronic databases. Each study was evaluated using the quality assessment tool for cross-sectional studies of the Agency for Healthcare Research and Quality (AHRQ). STATA 15.0 was used to analyse the data, and a random effects model was used. The groups that included two or more studies were added to the moderator analysis. Results. A total of 40 study articles involving 23,451 participants were included. The turnover intention of nurses was inversely associated with work engagement (coefficient: −0.42) and perceived organizational support (coefficient: −0.32). A substantial moderating role was played by cultural background, economic status, working years, and investigation time (P<0.05). Conclusion. Work engagement and organizational support significantly reduced turnover intention among nurses. Considering the acute shortage of nurses worldwide, nurses with lower wages, fewer working years, and lower levels of work engagement should be given more attention and support from their organizations. Implications for Nursing Management. The meta-analysis suggested that managers should give their employees a more organizational support and promote their work engagement to motivate nurses' retention intention and maintain a stable workforce with little employee turnover.

Establishment of infection prevention and control strategy in nursing managements during surgical operations in COVID-19 patients based on Delphi method.

AIM: The aim of this study was to establish an infection prevention and control strategy for nursing managements during surgical operations in coronavirus disease 2019 (COVID-19) patients. DESIGN: A Delphi method. METHODS: Between November 2021 and March 2022, we first formulated a preliminary infection prevention and control strategy based on the literature review and institutional experience. Then, we applied Delphi method and performed expert surveys to reach a final strategy for nursing managements during surgical operations in COVID-19 patients. RESULTS: The strategy included seven dimensions with 34 items. The positive coefficients of Delphi experts in both surveys were 100%, indicating a high coordination among experts. The degree of authority and expert coordination coefficient were 0.91 and 0.097-0.213. After the second expert survey, value assignments for importance of each dimension and item were 4.21-5.00 and 4.21-4.76 points, respectively. The coefficients of variation for dimension and item were 0.09-0.19 and 0.05-0.19, respectively. PATIENT OR PUBLIC CONTRIBUTION: Except the medical experts and research personnel, there was no other patient or public contribution involved in the study.

A Review of Filtration Performance of Protective Masks.

Masks are essential and effective small protective devices used to protect the general public against infections such as COVID-19. However, available systematic reviews and summaries on the filtration performance of masks are lacking. Therefore, in order to investigate the filtration performance of masks, filtration mechanisms, mask characteristics, and the relationships between influencing factors and protective performance were first analyzed through mask evaluations. The summary of filtration mechanisms and mask characteristics provides readers with a clear and easy-to-understand theoretical cognition. Then, a detailed analysis of influencing factors and the relationships between the influencing factors and filtration performance is presented in. The influence of the aerosol size and type on filtration performance is nonlinear and nonconstant, and filtration efficiency decreases with an increase in the gas flow rate; moreover, fitness plays a decisive role in the protective effects of masks. It is recommended that the public should wear surgical masks to prevent COVID-19 infection in low-risk and non-densely populated areas. Future research should focus on fitness tests, and the formulation of standards should also be accelerated. This paper provides a systematic review that will be helpful for the design of masks and public health in the future.

Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China’s Guizhou (preprint)

China has implemented a series of long-term measures for the public in order to control spread of COVID-19,whether these measures will affect other chronic and acute respiratory infectious diseases and what kind of impact are unclear. Tuberculosis (TB) and scarlet fever (SF) as the representative of chronic and acute respiratory infectious diseases respectively,and China’s Guizhou was an area with high prevalence of TB and SF ,with about 40,000 TB and hundreds of SF cases were reported every year.To assess impact of COVID-19 prevention and control on TB and SF in China’s Guizhou, exponential smoothing method was used to establish a prediction model to analyze the impact of COVID-19 on the number of TB and SF cases in Guizhou,and spatial aggregation analysis was used to describe the spatial changes of TB and SF before and after the outbreak of COVID-19.The parameters of TB and SF prediction models are R²=0.856, BIC=10.972;R²=0.714,BIC=5.325, respectively.TB and SF cases declined rapidly at the beginning of COVID-19 prevention and control,but SF cases number in decline for about 3-6 months,TB cases number remained in decline for 7 months after implementation for 11 months. Spatial aggregation of TB and SF did not change much before and after the COVID-19 outbreak but decreased significantly.Our fndings indicated that China's COVID-19 prevention and control measures also reduced TB and SF prevalence in Guizhou, these measures may lead to a long-term beneficial impact on TB, but a short-term on SF. Area with high TB incidence may benefit from COVID-19 experiences in the future.

Dynamic Evaluation of Transformation Ability for Emergency Scientific Research Achievements Based on an Improved Minimum Distance-Maximum Entropy Combination Weighting Method: A Case Study of COVID-19 Epidemic Data.

In the process of responding to major public health emergencies, the transformation of emergency scientific research results often faces many unfavourable factors such as limited resources, tight time, changes in needs, and lack of results. It is necessary to evaluate and analyze the ability to transform emergency scientific research results under public health emergencies, so as to rationally allocate emergency scientific research resources between subjects and regions, improve the efficiency of emergency results transformation, enhance emergency scientific research capabilities, and efficiently support incident prevention, control, and treatment. Starting from the patent level, this paper constructs an indicator system to evaluate the transformation ability of emergency scientific research results under major public health emergencies. It improves the minimum distance-maximum entropy combination weighting method to realize the static evaluation of transformation ability for emergency scientific research results from the perspective of patents, then constructs the dynamic evaluation model of transformation ability for emergency scientific research results in public health emergencies from the perspective of patents, and carries out the dynamic evaluation of the emergency scientific research achievements transformation ability of different subjects and different regions. We also improve the ER index, measure the static polarization effect of the transformation ability for regional emergency scientific research results, and consider the time factor to construct a dynamic polarization effect measurement model for the transformation ability of emergency scientific research achievement. Furthermore, this paper improves the measurement model of contribution degree to the polarization effect, and analyzes the contribution degree to polarization of the transformation ability for regional emergency scientific research results.

Hypertension Exacerbates Severity and Outcomes of COVID-19 in Elderly Patients: A Retrospective Observational Study.

OBJECTIVE: To evaluate the impact of hypertension on the clinical outcome of COVID-19 patients aged 60 years old and older. METHODS: This single-center retrospective cohort study enrolled consecutive COVID-19 patients aged 60 years old and older, who were admitted to Liyuan Hospital from January 1, 2020 to April 25, 2020. All included patients were divided into two groups: hypertension and nonhypertension group. The baseline demographic characteristics, laboratory test results, chest computed tomography (CT) images and clinical outcomes were collected and analyzed. The prognostic value of hypertension was determined using binary logistic regression. RESULTS: Among the 232 patients included in the analysis, 105 (45.3%) patients had comorbid hypertension. Compared to the nonhypertension group, patients in the hypertension group had higher neutrophil-to-lymphocyte ratios, red cell distribution widths, lactate dehydrogenase, high-sensitivity C-reactive protein, D-dimer and severity of lung lesion, and lower lymphocyte counts (all P<0.05). Furthermore, the hypertension group had a higher proportion of intensive care unit admissions [24 (22.9%) vs. 14 (11.0%), P=0.02) and deaths [16 (15.2%) vs. 3 (2.4%), P<0.001] and a significantly lower probability of survival (P<0.001) than the nonhypertension group. Hypertension (OR: 4.540, 95% CI: 1.203-17.129, P=0.026) was independently correlated with all-cause in-hospital death in elderly patients with COVID-19. CONCLUSION: The elderly COVID-19 patients with hypertension tend to have worse conditions at baseline than those without hypertension. Hypertension may be an independent prognostic factor of poor clinical outcome in elderly COVID-19 patients.

A Meta-Analysis of Accuracy and Sensitivity of CRISPR and Chest CT in COVID‑19 Diagnosis (preprint)

Objective: To clarify the accuracy of clusters of regularly spaced short palindrome repeats (CRISPR) technology and chest CT in the diagnosis of Corona Virus Disease2019(COVID-19). Methods: The term "Corona Virus Disease " "clustered regularly spaced short palindromic repeats" "CRISPR", "chest CT", "sensitivity and specificity" as the subject words or keywords were searched in databases such as Pubmed, Embase, Cochrane Library, Wiley and Scopus and Chinese academic databases (such as CNKI, Wanfang and Chongqing VIP data) for relevant literature on the use of CRISPR technology and chest CT for the diagnosis of COVID-19. Meta-analysis was performed after literature screening, quality assessment and data extraction . Results: A total of 418 articles were retrieved, and 17 articles were finally included. The results showed that the combined sensitivity of CRISPR technology for diagnosing new coronary pneumonia infection was 0.96 [95% CI (0.93, 0.98)], and the combined specificity was 1.00 [95% CI (0.92, 1.00)], the combined positive likelihood ratio is 458.69 [95%CI (11.51, 18280.8)], the combined negative likelihood ratio is 0.04 [95% CI (0.02, 0.07)], the area under the SROC curve is 0.99 [95%CI(0.97,0.99)]. The combined sensitivity of chest CT in diagnosing new coronary pneumonia infection was 0.94 [95%CI (0.83, 0.98)], combined specificity was 0.55 [95% CI (0.22, 0.83)], combined diagnostic odds ratio was 19.90 [95% CI (7.88, 50.25)], the combined positive likelihood ratio is 2.08 [95%CI (1.00, 4.32)], the combined negative likelihood ratio is 0.10 [95% CI (0.05, 0.23)], the area under the SROC curve is 0.91 [95% CI (0.88, 0.93)]. The Deek funnel chart indicates that there is no potential publication bias among the included studies (PCRISPR = 0.03, P chest CT = 0.55). Conclusion: CRISPR technology has a better ability to detect infections in patients with COVID-19, and is better than chest CT in disease diagnosis. CRISPR technology, especially non-SHERLOCK type and multi-target gene detection, can be used to diagnose COVID-19 with higher accuracy ,and can be used for large-scale population screening.

Early Fever Is Associated With Clinical Outcomes in Patients With Coronavirus Disease.

Fever is one of the typical symptoms of coronavirus disease (COVID-19). We aimed to investigate the association between early fever (EF) and clinical outcomes in COVID-19 patients. A total of 1,014 COVID-19 patients at the Leishenshan Hospital were enrolled and classified into the EF and non-EF groups based on whether they had fever within 5 days of symptom onset. Risk factors for clinical outcomes in patients with different levels of disease severity were analyzed using multivariable analyses. Time from symptom onset to symptom alleviation, CT image improvement, and discharge were longer for patients with moderate and severe disease in the EF group than in the non-EF group. Multivariable analysis showed that sex, EF, eosinophil number, C-reactive protein, and IL-6 levels were positively correlated with the time from symptom onset to hospital discharge in moderate cases. The EF patients showed no significant differences in the development of acute respiratory distress syndrome, compared with the non-EF patients. The Kaplan-Meier curve showed no obvious differences in survival between the EF and non-EF patients. However, EF patients with increased temperature showed markedly lower survival than the non-EF patients with increased temperature. EF had no significant impact on the survival of critically ill patients, while an increase in temperature was identified as an independent risk factor. EF appears to be a predictor of longer recovery time in moderate/severe COVID-19 infections. However, its value in predicting mortality needs to be considered for critically ill patients with EF showing increasing temperature.

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

BACKGROUND: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading 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 volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, 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 built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) 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. As for the SVM models 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 classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

Machine Learning-based CT Radiomics Model Distinguishes COVID-19 From Non-COVID-19 Pneumonia (preprint)

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.

Compare the epidemiological and clinical features of imported and local COVID-19 cases in Hainan, China.

BACKGROUND: Effective management of imported cases is an important part of epidemic prevention and control. Hainan Province, China reported 168 coronavirus disease 2019 (COVID-19), including 112 imported cases on February 19, 2020, but successfully contained the epidemic within 1 month. We described the epidemiological and clinical characteristics of COVID-19 in Hainan and compared these features between imported and local cases to provide information for other international epidemic areas. METHODS: We included 91 patients (56 imported and 35 local cases) from two designated hospitals for COVID-19 in Haikou, China, from January 20 to February 19, 2020. Data on the demographic, epidemiological, clinical and laboratory characteristics were extracted from medical records. Patients were followed until April 21, 2020, and the levels of antibodies at the follow-ups were also analysed by the Wilcoxon matched-pairs signed ranks test. RESULTS: Of the 91 patients, 78 (85.7%) patients were diagnosed within the first three weeks after the first case was identified (Day 1: Jan 22, 2020), while the number of local cases started to increase during the third week. No new cases occurred after Day 29. Fever and cough were two main clinical manifestations. In total, 15 (16.5%) patients were severe, 14 (15.4%) had complicated infections, nine (9.9%) were admitted to the intensive care unit, and three died. The median duration of viral shedding in feces was longer than that in nasopharyngeal swabs (19 days vs 16 days, P = 0.007). Compared with local cases, imported cases were older and had a higher incidence of fever and concurrent infections. There was no difference in outcomes between the two groups. IgG was positive in 92.8% patients (77/83) in the follow-up at week 2 after discharge, while 88.4% patients (38/43) had a reduction in IgG levels in the follow-up at week 4 after discharge, and the median level was lower than that in the follow-up at week 2 (10.95 S/Cut Off (S/CO) vs 15.02 S/CO, P <  0.001). CONCLUSION: Imported cases were more severe than local cases but had similar prognoses. The level of IgG antibodies declined from week 6 to week 8 after onset. The short epidemic period in Hainan suggests that the epidemic could be quickly brought under control if proper timely measures were taken.

Applications, phytochemistry, pharmacological effects, pharmacokinetics, toxicity of Scutellaria baicalensis Georgi. and its probably potential therapeutic effects on COVID-19: a review.

Scutellaria baicalensis Georgi. (SB) is a common heat-clearing medicine in traditional Chinese medicine (TCM). It has been used for thousands of years in China and its neighboring countries. Clinically, it is mostly used to treat diseases such as cold and cough. SB has different harvesting periods and processed products for different clinical symptoms. Botanical researches proved that SB included in the Chinese Pharmacopoeia (1st, 2020) was consistent with the medicinal SB described in ancient books. Modern phytochemical analysis had found that SB contains hundreds of active ingredients, of which flavonoids are its major components. These chemical components are the material basis for SB to exert pharmacological effects. Pharmacological studies had shown that SB has a wide range of pharmacological activities such as antiinflammatory, antibacterial, antiviral, anticancer, liver protection, etc. The active ingredients of SB were mostly distributed in liver and kidney, and couldn't be absorbed into brain via oral absorption. SB's toxicity was mostly manifested in liver fibrosis and allergic reactions, mainly caused by baicalin. The non-medicinal application prospects of SB were broad, such as antibacterial plastics, UV-resistant silk, animal feed, etc. In response to the Coronavirus Disease In 2019 (COVID-19), based on the network pharmacology research, SB's active ingredients may have potential therapeutic effects, such as baicalin and baicalein. Therefore, the exact therapeutic effects are still need to be determined in clinical trials. SB has been reviewed in the past 2 years, but the content of these articles were not comprehensive and accurate. In view of the above, we made a comprehensive overview of the research progress of SB, and expect to provide ideas for the follow-up study of SB.

Epidemiological and clinical features of imported and local patients with coronavirus disease 2019 (COVID-19) in Hainan, China (preprint)

Background: Hainan Island, which is a popular tourist destination, received many imported cases of Coronavirus disease 2019 (COVID-19) but successfully contained the epidemic within one month. We described the epidemiological and clinical characteristics of COVID-19 in Hainan and compared these features between imported and local cases to provide information for other international epidemic areas. Methods: : We included 91 patients (56 imported and 35 local cases) from two designated hospitals for COVID-19 in Haikou, China, from January 20 to February 19, 2020. Data on the demographic, epidemiological, clinical and laboratory characteristics were extracted from medical records. Patients were followed until April 21, 2020, and the levels of antibodies at the follow-ups were also analyzed. Results: : Of the 91 patients, 78 (85.7%) patients were diagnosed within the first three weeks after the first case was identified (Day 1: Jan 22, 2020), while the number of local cases started to increase during the third week. No new cases occurred after Day 29. Fever and cough were two main clinical manifestations. In total, 15 (16.5%) patients were severe, 14 (15.4%) had complicated infections, nine (9.9%) were admitted to the ICU, and three died. The median duration of viral shedding in feces was longer than that in nasopharyngeal swabs (19 days vs 16 days, P =0.007). Compared with local cases, imported cases were older and had a higher incidence of fever and concurrent infections. There was no difference in outcomes between the two groups. IgG was positive in 92.8% patients (77/83) in the follow-up at week 2 after discharge, while 88.4% patients (38/43) had a reduction in IgG levels in the follow-up at week 4 after discharge, and the median level was lower than that in the follow-up at week 2 (10.95 S/CO vs 15.02 S/CO, P<0.001). Conclusion: Imported cases were more severe than local cases but had similar prognoses. The level of IgG antibodies declined from week 6 to week 8 after onset. The short epidemic period in Hainan suggests that the epidemic could be quickly brought under control if proper timely measures were taken.

Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis.

OBJECTIVE: To determine the consistency between CT findings and real-time reverse transcription-polymerase chain reaction (RT-PCR) and to 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 34 COVID-19 patients, confirmed by RT-PCR from January 20 to February 4 in Hainan Province. CT scores were compared between the discharged patients and the ICU patients. RESULTS: Fever (85%) and cough (79%) were most commonly seen. Ten (29%) patients demonstrated negative results on their first RT-PCR. Of the 34 (65%) patients, 22 showed pure ground-glass opacity. Of the 34 (50%) patients, 17 had five lobes of lung involvement, while the 23 (68%) patients had lower lobe involvement. The lesions of 24 (71%) patients were distributed mainly in the subpleural area. The initial CT lesions of ICU patients were distributed in both the subpleural area and centro-parenchyma (80%), and the lesions were scattered. Sixty percent of ICU patients had five lobes involved, while this was seen in only 25% of the discharged patients. The lesions of discharged patients were mainly in the subpleural area (75%). Of the discharged patients, 62.5% showed pure ground-glass opacities; 80% of the ICU patients were in the progressive stage, and 75% of the discharged patients were at an early stage. CT scores of the ICU patients were significantly higher than those of the discharged patients. CONCLUSION: Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. The initial features in CT may be associated with prognosis. KEY POINTS: • Chest CT is valuable for the early diagnosis of COVID-19, particularly for those patients with a negative RT-PCR. • The early CT findings of COVID-19 in ICU patients differed from those of discharged patients.

Clinical and Radiological Features of Patients with Coronavirus Disease 2019 (preprint)

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.

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia (preprint)

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.

Epidemiological and clinical features of imported and local patients with coronavirus disease 2019 (COVID-19) in Hainan, China (preprint)

Background Hainan Island, a popular tourist destination, had received many imported cases of Coronavirus disease 2019 (COVID-19), but successfully contained the epidemics in one month. We described epidemiological and clinical characteristics of COVID-19 in Hainan and compared these features between imported and local cases to provide information for other international epidemic areas. Methods We included 91 patients (56 imported and 35 local cases) from two designed hospitals for COVID-19 in Haikou, China, from January 20 to February 19, 2020. Data on demographic, epidemiological, clinical and laboratory characteristics were extracted from medical records. Results Of the 91 patients, 78 (85.7%) patients were diagnosed within the first three weeks after the first case identified (Day 1: Jan 22, 2020), while the number of local cases started to increase from the third week. No new cases occurred after Day 29. Fever and cough were two main clinical manifestations. 15 (16.5%) were severe, 14 (15.4%) had complicated infections, nine (9.9%) were admitted to ICU, and three died. Median duration of viral shedding in feces was longer than that in nasopharyngeal swabs (19 days vs 16 days, P =0.007). Compared with local cases, imported cases were older, have higher incidence of fever and concurrent infections. There was no difference in outcomes between the two groups. Conclusion Imported cases were more severe than local cases, but could have similar prognosis. The short epidemic period in Hainan suggests that the epidemics could be quickly brought under control if proper timely measures were taken.

Clinical Characteristics of SARS-CoV-2 Infection in Haikou: a study of a family cluster (preprint)

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 33–57 years) 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.

Clinical Characteristics of SARS-CoV-2 Infection in Haikou: a study of a family cluster (preprint)

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.