Clinical characteristics of pediatric cases infected with the SARS-CoV-2 Omicron variant in a tertiary children's medical center in Shanghai, China.

BACKGROUND: The number of pediatric cases of infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has increased. Here, we describe the clinical characteristics of children in a tertiary children's medical center in Shanghai. METHODS: A total of 676 pediatric coronavirus disease 2019 (COVID-19) cases caused by the Omicron variant who were admitted to the Shanghai Children's Medical Center from March 28 to April 30, 2022 were enrolled in this single-center, prospective, observational real-world study. Patient demographics and clinical characteristics, especially COVID-19 vaccine status, were assessed. RESULTS: Children of all ages appeared susceptible to the SARS-CoV-2 Omicron variant, with no significant difference between sexes. A high SARS-CoV-2 viral load upon admission was associated with leukocytopenia, neutropenia, and thrombocytopenia (P = 0.003, P = 0.021, and P = 0.017, respectively) but not with physical symptoms or radiographic chest abnormalities. Univariable linear regression models indicated that comorbidities (P = 0.001) were associated with a longer time until viral clearance, and increasing age (P < 0.001) and two doses of COVID-19 vaccine (P = 0.001) were associated with a shorter time to viral clearance. Multivariable analysis revealed an independent effect of comorbidities (P < 0.001) and age (P = 0.003). The interaction effect between age and comorbidity showed that the negative association between age and time to virus clearance remained significant only in patients without underlying diseases (P < 0.001). CONCLUSION: This study describes the clinical characteristics of children infected with the Omicron variant of SARS-CoV-2 and calls for additional studies to evaluate the effectiveness and safety of vaccination against COVID-19 in children.

A trial of arbidol hydrochloride in adults with COVID-19.

BACKGROUND: To date, there is no effective medicine to treat coronavirus disease 2019 (COVID-19), and the antiviral efficacy of arbidol in the treatment for COVID-19 remained equivocal and controversial. The purpose of this study was to evaluate the efficacy and safety of arbidol tablets in the treatment of COVID-19. METHODS: This was a prospective, open-label, controlled and multicenter investigator-initiated trial involving adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients were stratified 1:2 to either standard-of-care (SOC) or SOC plus arbidol tablets (oral administration of 200 mg per time, three times a day for 14 days). The primary endpoint was negative conversion of SARS-CoV-2 within the first week. The rates and 95% confidential intervals were calculated for each variable. RESULTS: A total of 99 patients with laboratory-confirmed SARS-CoV-2 infection were enrolled; 66 were assigned to the SOC plus arbidol tablets group, and 33 to the SOC group. The negative conversion rate of SARS-CoV-2 within the first week in patients receiving arbidol tablets was significantly higher than that of the SOC group (70.3% [45/64] vs. 42.4% [14/33]; difference of conversion rate 27.9%; 95% confidence interval [CI], 7.7%-48.1%; P  = 0.008). Compared to those in the SOC group, patients receiving arbidol tablets had a shorter duration of clinical recovery (median 7.0 days vs. 12.0 days; hazard ratio [HR]: 1.877, 95% CI: 1.151-3.060, P = 0.006), symptom of fever (median 3.0 days vs. 12.0 days; HR: 18.990, 95% CI: 5.350-67.410, P < 0.001), as well as hospitalization (median 12.5 days vs. 20.0 days; P < 0.001). Moreover, the addition of arbidol tablets to SOC led to more rapid normalization of declined blood lymphocytes (median 10.0 days vs. 14.5 days; P > 0.05). The most common adverse event in the arbidol tablets group was the elevation of transaminase (5/200, 2.5%), and no one withdrew from the study due to adverse events or disease progression. CONCLUSIONS: SOC plus arbidol tablets significantly increase the negative conversion rate of SARS-CoV-2 within the first week anas, accelerate the recovery of COVID-19 patients. During the treatment with arbidol tablets, we find no significant serious adverse events. TRIAL REGISTRATION: Chinese Clinical Trial Registry, NCT04260594, www.clinicaltrials.gov/ct2/show/NCT04260594?term=NCT04260594&draw=2&rank=1.

Association between vaccinations and clinical manifestations in children with COVID-19.

BACKGROUND: The infection rate of Coronavirus Disease 2019 (COVID-19) in children was less than that in adults. However, the underlining reason is not well known. METHODS: Children with COVID-19 were recruited from two Children's Hospitals in Wuhan and Shanghai in this case-control study. The associations of initial symptoms with age, vaccinations of Bacillus Calmette Guerin (BCG), and influenza and pathogens were determined by Chi-square t-test. RESULTS: We evaluated 248 confirmed cases, and 56 suspected cases with COVID-19. The median age was 6.82 years old, and 118 cases (38.82%) were girls. Furthermore, 30.26% of all patients were asymptomatic cases. The percentage of asymptomatic cases vaccinated with BCG was not significantly higher than that without BCG vaccination [86/280 (30.71%) vs. 6/13 (46.15%), P=0.203], and initial symptoms were not related with immunized influenza vaccine (P=0.267). Compared to parameters in pediatric patients with normal body temperatures, patients with fever had higher C reactive protein (CRP) (P<0.001). CONCLUSIONS: Pediatric COVID-19 patients with BCG vaccinations exhibit similar clinical manifestations compared to those without BCG vaccinations, and the severity of symptoms in pediatric patients may be related to the maturity of immune function.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.

BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Longitudinal changes in COVID-19 clinical measures and correlation with the extent of CT lung abnormalities.

Rationale: To assess the longitudinal changes and relationships of clinical measures and extent of CT lung abnormalities in COVID-19. Methods: 81 patients with COVID-19 were prospectively enrolled and followed until discharge. CT scores were quantified on a basis of a CT scoring system where each lung was divided into 3 zones: upper (above the carina), middle, and lower (below the inferior pulmonary vein) zones; each zone was evaluated for percentage of lung involvement on a scale of 0-4 (0, 0%; 1, 0-24%; 2, 25% - 49%; 3, 50% -74%; 4, >74%).Temporal trends of CT scores and the laboratory parameters characteristic of COVID-19 were analyzed. Correlations between the two were determined at three milestones (initial presentation, worst CT manifestation, and recovery finding before discharge). Their correlations with duration to worst CT manifestation and discharge from symptom onset were evaluated. Results: CT scores peaked during illness days 6-11 (median: 5), and stayed steady. C-reactive protein and lactate dehydrogenase increased, peaked on illness days 6-8 and 8-11 (mean: 23.5 mg/L, 259.9 U/L), and gradually declined. Continual decrease and increase were observed in hemoglobin and lymphocyte count, respectively. Albumin reduced and remained at low levels with a nadir on illness days 12-15 (36.6 g/L). Both initial (r = 0.58, 0.64, p < 0.05) and worst CT scores (r = 0.47, 0.65, p < 0.05) were correlated with C-reactive protein and lactate dehydrogenase; and CT scores before discharge, only with albumin (r = -0.41, p < 0.05). Duration to worst CT manifestation was associated with initial and worst CT scores (r = 0.33, 0.29, p < 0.05). No parameters were related to timespan to discharge. Conclusion: Our results illustrated the temporal changes of characteristic clinical measures and extent of CT lung abnormalities in COVID-19. CT scores correlated with some important laboratory parameters, and might serve as prognostic factors.

Tracer-based characterization of source variations of PM2.5 and organic carbon in Shanghai influenced by the COVID-19 lockdown.

Air quality in megacities is significantly impacted by emissions from vehicles and other urban-scale human activities. Amid the outbreak of Coronavirus (COVID-19) in January 2020, strict policies were in place to restrict people's movement, bringing about steep reductions in pollution activities and notably lower ambient concentrations of primary pollutants. In this study, we report hourly measurements of fine particulate matter (i.e., PM2.5) and its comprehensive chemical speciation, including elemental and molecular source tracers, at an urban site in Shanghai spanning a period before the lockdown restriction (BR) (1 to 23 Jan. 2020) and during the restriction (DR) (24 Jan. to 9 Feb. 2020). The overall PM2.5 was reduced by 27% from 56.2 ± 40.9 (BR) to 41.1 ± 25.3 µg m-3 (DR) and the organic carbon (OC) in PM2.5 was similar, averaged at 5.45 ± 2.37 (BR) and 5.42 ± 1.75 µgC m-3 (DR). Reduction in nitrate was prominent, from 18.1 (BR) to 9.2 µg m-3 (DR), accounting for most of the PM2.5 decrease. Source analysis of PM2.5 using positive matrix factorization modeling of comprehensive chemical composition, resolved nine primary source factors and five secondary source factors. The quantitative source analysis confirms reduced contributions from primary sources affected by COVID-19, with vehicular emissions showing the largest drop, from 4.6 (BR) to 0.61 µg m-3 (DR) and the percentage change (-87%) in par with vehicle traffic volume and fuel sale statistics (-60% to -90%). In the same time period, secondary sources are revealed to vary in response to precursor reductions from the lockdown, with two sources showing consistent enhancement while the other three showing reductions, highlighting the complexity in secondary organic aerosol formation and the nonlinear response to broad primary precursor pollutants. The combined contribution from the two secondary sources to PM2.5 increased from 7.3 ± 6.6 (BR) to 14.8 ± 9.3 µg m-3 (DR), partially offsetting the reductions from primary sources and nitrate while their increased contribution to OC, from 1.6 ± 1.4 (BR) to 3.2 ± 2.0 µgC m-3 (DR), almost offset the decrease coming from the primary sources. Results from this work underscore challenges in predicting the benefits to PM2.5 improvement from emission reductions of common urban primary sources.

Epidemiological characteristics and transmission dynamics of paediatric cases with coronavirus disease 2019 in Hubei province, China.

AIM: This study aimed to identify the epidemiological characteristics and transmission dynamics of paediatric cases. METHODS: Information on 1369 paediatric cases with COVID-19 from 8 December 2019 to 7 March 2020 in Hubei province was extracted from the National Infectious Disease Surveillance System. The analysis included epidemic curves, temporal-spatial distribution, clinical classification and interval times between onset and diagnosis. RESULTS: Among 1369 paediatric cases, the median age was 9 years and 58.2% of them were males. The proportion of severe and critical cases in children was lower than that in adults and the proportion of asymptomatic cases in children was five times greater than for adult cases. The first paediatric case was reported on 2 January 2020, and the daily number of new paediatric cases remained high from 1 February through to 22 February. The epidemiological curve of paediatric cases lagged behind that of adults by 19 days, and the first spike of the epidemic curve in senior high school students occurred 1 week earlier than in other paediatric groups. The proportion of clustered cases among children was about twice that for adults. The median of the interval in paediatric cases between onset and diagnosis, isolation and notification were 3, 0 and 3 days, respectively, and all of those were significantly shorter than in adults. CONCLUSIONS: The epidemic curve of child cases lagged behind that of adult cases by 19 days, and the major form of transmission observed was in clusters.

Protecting Healthcare Professionals during the COVID-19 Pandemic.

OBJECTIVE: To understand how to implement proactive prevention measures among healthcare professionals for preventing potential nosocomial infection. METHODS: 91 healthcare professionals confirmed with the COVID-19 infection were collected, and clinical characteristics and epidemiological data were evaluated. RESULTS: Among the cases, 77 cases (84.6%) were confirmed by the viral nucleic acid test, and the other 14 cases were diagnosed by the clinical investigation. Ground glass opacity and bilateral shadows distribution were observed in 78 cases (85.6%). 56 cases (61.5%) were admitted into Zhongnan Hospital and subjected to antiviral treatment. 73 of a total of 91 cases (80.2%) with a median incubation period of 3 days (IQR, 2 to 6) reported close contact history with patients with the COVID-19 infection. The most common symptoms at the onset of illness were fever (66 cases, 72.5%) and cough (54 cases, 59.3%). The initial positive rate of the CT scan and RT-PCR assay were 84.6% and 48.4%, respectively (P < 0.01). There were 50 cases occurred during the early stage (before Jan 20, 2020), whereas 41 cases occurred at a late stage (after Jan 20, 2020). In the early stage, the most common route of exposure to COVID-19 was via direct care in the absence of any invasive procedure. By contrast, 37 healthcare professionals infected with COVID-19 in the late stage were confirmed to have been exposed via aerosol-generating procedures. CONCLUSION: Identification of the asymptomatic individuals in healthcare settings and prompt response when a suspicious case is considered may render effective control of the nosocomial infection during this pandemic.

COVID-19 and early-stage lung cancer both featuring ground-glass opacities: a propensity score-matched study.

BACKGROUND: Radiological manifestations of coronavirus disease 2019 (COVID-19) featured ground-glass opacities (GGOs), especially in the early stage, which might create confusion in differential diagnosis with early lung cancer. We aimed to specify the radiological characteristics of COVID-19 and early lung cancer and to unveil the discrepancy between them. METHODS: One hundred and fifty-seven COVID-19 patients and 374 early lung cancer patients from four hospitals in China were retrospectively enrolled. Epidemiological, clinical, radiological, and pathological characteristics were compared between the two groups using propensity score-matched (PSM) analysis. RESULTS: COVID-19 patients had more distinct symptoms, tended to be younger (P<0.0001), male (P<0.0001), and had a higher body mass index (P=0.014). After 1:1 PSM, 121 matched pairs were identified. Regarding radiological characteristics, patients with a single lesion accounted for 17% in COVID-19 and 89% in lung cancer (P<0.0001). Most lesions were peripherally found in both groups. Lesions in COVID-19 involved more lobes (median 3.5 vs. 1; P<0.0001) and segments (median 6 vs. 1; P<0.0001) and tended to have multiple types (67%) with patchy form (54%). Early lung cancer was more likely to have a single type (92%) with oval form (66%). Also, COVID-19 and early lung cancer either had some distinctive features on computed tomography (CT) images. CONCLUSIONS: Both COVID-19 and early lung cancers showed GGOs, with similar but independent features. The imaging characteristics should be fully understood and combined with epidemiological history, pathogen detection, laboratory tests, short-term CT reexamination, and pathological results to aid differential diagnosis.

Chinese medicine for coronavirus disease 2019 as complementary therapy: A protocol for a systematic review and meta-analysis.

BACKGROUND: The aim of this systematic review and meta-analysis is to assess effectiveness and safety of Chinese medicine (CM) as complementary therapy in treating coronavirus disease 2019 (COVID-19). METHODS: The following databases will be searched: PubMed, Cochrane, Embase, China National Knowledge Infrastructure, Chinese Science and Technology Periodical Database, and Wanfang database from October 1, 2019 to March 1, 2020. Randomized trials and quasi-randomized or prospective controlled clinical trials of CM that reported data on COVID-19 patients will be included. Study selection, data extraction, quality assessment, and assessment of risk bias will be performed by 2 reviewers independently. Odds ratios and correlative 95% confidence intervals will be calculated to present the association between the CM and CWM using Review Manager version 5.3 when there is sufficient available data. RESULTS: The results will be disseminated through a peer-reviewed journal publication. CONCLUSION: This systematic review findings will summarize up-to-date evidence for that CM is more effective and safe as adjunctive treatment for patients with COVID-19. ETHICS AND DISSEMINATION: Ethics approval and patient consent are not required as this study is a systematic review based on published articles. PROSPERO REGISTRATION NUMBER: CRD42020185382.

Clinical characteristics and chest computed tomography findings of severe and critical coronavirus disease 2019 in Shanghai, China

Objective To sum up the clinical characteristics and chest computed tomography (CT) findings of severe and critical coronavirus disease 2019 (COVID-19) patients, and to explore the factors affecting the outcomes, so as to provide experience for the clinical diagnosis and treatment of severe and critical COVID-19. Methods The data of 25 severe and critical COVID-19 patients, who were treated in our hospital from Jan. 23, 2020 to Mar. 5, 2020, were collected. The clinical characteristics were retrospectively analyzed, and the clinical and laboratory indexes were compared between cured patients and uncured patients. The laboratory indicators of cured patients were further compared between the progressive and recovery stages. The chest CT findings of the patients were observed, and the lesion volume was quantified to assess the evolution of lung lesions using the CT image-based intelligent pneumonia lesion quantitative analysis software. Results There were 19 male and six female COVID-19 patients, and there were three deaths. The median age of 25 patients was 65 (63, 75) years old, and the body mass index (BMI) was 25.60 (23.51, 28.65) kg/m2. Twenty-two patients had a clear epidemiological history. Fever (22 cases) and cough (14 cases) were the most common first symptoms, and 18 patients had underlying diseases. Twelve patients were cured and discharged (median hospital stay was 25.5 d), and 13 patients were not cured, including three deaths and 10 cases with hospital stay>25 d with no remission. Compared with the uncured patients, the cured patients had significantly lower BMI, longer time from onset to progression to severe or critical illness, and higher CD4 +T lymphocyte counts (all P<0.05). Multivariate logistic regression analysis showed that high CD4 +T lymphocyte count was an independent protective factor for the cure and discharge of severe and critical COVID-19 patients (P=0.031). Compared with those in the progressive stage, the lymphocyte count and CD4 +T lymphocyte count of 12 cured patients were significantly higher in the progression stage, and the C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR) and procalcitonin level were significantly lower (all P<0.01). Twenty-one patients received chest CT examination in the progressive stage;and all of them had multiple ground-glass opacities and consolidation shadows of the multiple-lobe lateral band and the dorsal side of bilateral lungs, 20 cases had pleural thickening, 9 cases had a small amount of bilateral pleural effusion, and 8 cases had mediastinal lymphadenopathy. The 12 cured patients received CT examination during the recovery period, and their lesions were all improved to different extents;some patients had irregular fiber grid shadows and stripe shadows;and the pleural thickening and pleural effusion were reduced to different extents. The quantitative analysis curves showed that lesion volume in the 12 cured patients obviously increased in the progressive stage and reduced in the absorption stage, showing an inverted V shape;and lesion volume in the uncured patients (nine cases received CT examination for two or more times) showed a rapid increase in the progressive stage. Conclusion Most severe and critical COVID-19 patients in Shanghai are older, with higher BMI and underlying diseases. Low BMI, slow disease progression, and high CD4 +T lymphocyte count are beneficial to the improvement of COVID-19. The main findings of chest CT include multiple ground-glass opacities and consolidation shadows, mainly distributing in the lateral band and the dorsal side of lungs and mostly involving the pleura. The laboratory indexes, including the lymphocyte, CRP, CD4 +T lymphocyte, ESR and procalcitonin, and chest CT examination play an important role in the diagnosis, disease monitoring and prognosis assessment of COVID-19

Research on the individualized short-term training model of nurses in emergency isolation wards during the outbreak of COVID-19.

AIM: To explore an effective personalized training model for nurses working in emergency isolation wards of COVID-19 in a short period. DESIGN: This study is a longitudinal study from 24 January 2020 to 28 February 2020. METHODS: There are 71 nursing staff working in the emergency isolation wards of Sichuan Provincial People's Hospital that participated in this study. The questionnaires were conducted with Likert scale. The operation assessment teachers have received standardized training. The self-rating anxiety scale (SAS) and self-rating depression Scale (SDS) were applied to assess the mental state of nurses. RESULTS: After short-term training, these nurses can handle the emergency tasks in a timely manner. The pass rate of nurse theory and operation assessment is 100%. The 111 suspected patients admitted to the emergency isolation ward have been scientifically diagnosed and treated, the three confirmed patients have received appropriate treatment. No nurses have been infected. CONCLUSIONS: In this study, the personalized emergency training mode was feasible in the emergency isolation ward during the COVID-19 epidemic, which rapidly improved the rescue ability of nurses and effectively avoid the occurrence of cross-infection. This mode can provide a valuable reference for the emergency training of nurses in the future.

Clinical characteristics and outcomes of coronavirus disease 2019 infections among diabetics: A retrospective and multicenter study in China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged in December 2019 and has spread globally. Diabetics are at increased risk of infections caused by a variety of pathogens including viruses. The present research aims to describe clinical characteristics and outcomes of COVID-19 patients with diabetes. METHODS: A retrospective multicenter study of COVID-19 patients with diabetes was conducted in four hospitals in Wuhan, Shanghai, and Anhui Province. Reverse transcription polymerase chain reaction or next-generation sequencing was carried out to confirm the existence of severe acute respiratory syndrome coronavirus 2 from respiratory specimens. RESULTS: A total of 54 diabetics (10.36%) were recruited from among 521 COVID-19 patients, with a median age of 63 (interquartile range, 52-70) years. Among them, 51 had been previously diagnosed with diabetes and 3 had been newly diagnosed based on glycosylated hemoglobin over 6.5%. For COVID-19, 47 of the 54 patients had an exposure history. Fever (47/54, 87.04%), dry cough (36/54, 66.67%), and expectoration (21/53, 39.62%) were among the top three symptoms. Lung infiltration was bilateral (46/52, 88.46%) and multilobe (47/52, 90.38%), and ground-glass opacity (36/37, 97.30%) was the most common pattern in radiological images. Moreover, COVID-19 patients with diabetes were prone to be classified as severe or critical cases (46.30%, 25/54) and had complications such as acute lung injury, acute respiratory distress syndrome, and acute kidney injury. The proportions of intensive care unit (ICU) admissions and deaths among the COVID-19 diabetics were 14.81% (8/54) and 12.96% (7/54), respectively. CONCLUSIONS: With older age, diabetics diagnosed as having COVID-19 were prone to develop into severe cases and exhibited a high rate of ICU admission and mortality.

A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study.

BACKGROUND: The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. OBJECTIVE: The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. METHODS: Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. RESULTS: DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients' demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro-area under the curve were all above 0.71 in each scenario. CONCLUSIONS: DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China.

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596-7.323), age of 40-69 years (OR = 1.586, 95% CI: 0.824-3.053), hypertension (OR = 3.372, 95% CI: 2.185-5.202), ALT >50 µ/l (OR = 3.304, 95% CI: 2.107-5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292-12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42-3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012-1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009-1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585-36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588-95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.