Application of interpersonal psychotherapy for late-life depression in China: A case report

Objectives Interpersonal psychotherapy (IPT) is an effective treatment for late-life depression, but little is known about its acceptability and efficacy in Chinese patients. This case report describes the use of IPT in a depressed elderly Chinese man. Methods The patient was a 79-year-old widower who lives alone in a large city in China. This was his first contact with a mental health specialist. His wife died one ago, and his only child lives in the United States with her husband and children. Due to the COVID-19 pandemic, his daughter could not visit him, and his usual social interactions decreased, leaving him feeling isolated, lonely, and depressed. He was diagnosed with a major depressive episode and initially prescribed venlafaxine. However, he failed to show an adequate response to medication and the side effects were intolerable. He was switched to a low dose of Duloxetine (60 mg) combined with IPT. Results The patient's baseline score on the 17-item Hamilton depression rating scale (HAM-D) was 29, suggesting severe levels of depression. He received 12 sessions of IPT. Role transition was the focus of therapy. Although the patient expressed discomfort in therapy, he developed a good rapport with the therapist and was compliant with treatment. Clinical recovery was achieved at the end of acute IPT treatment (HAM-D score = 1). Conclusion Response to IPT was excellent in this elderly patient, but several points should be noted. First, mental health-related stigma in China can affect treatment engagement. Second, older Chinese are reluctant to speak openly about their personal experiences and feelings. Hence, repeated emphasis on the principles of confidentiality in psychotherapy and forming a strong therapeutic alliance are important. Third, the "empty-nest” household is an emergent phenomenon in China. Helping elderly Chinese navigate changes in traditional Chinese living arrangements and negotiate filial piety with offspring who have moved away are important issues to address in therapy.

Aptamer-based nanointerferometer enables amplification-free ultrasensitive detection and differentiation of SARS-CoV-2 variants.

The state-of-the-art SARS-CoV-2 detection methods include qRT-PCR and antibody-based lateral flow assay (LFA) point-of-care tests. Despite the high sensitivity and selectivity, qRT-PCR is slow, expensive and needs well-trained operators. On the other extreme, LFA suffers from low sensitivity albeit its fast detection speed, low detection cost and ease of use. Therefore, the continuing COVID-19 pandemic calls for a SARS-CoV-2 detection method that is rapid, convenient and cost-effective without compromise in sensitivity. Here we provide a proof-of-principle demonstration of an optimized aptamer-based nanointerferometer that enables rapid and amplification-free detection of SARS-CoV-2 spike protein-coated pseudovirus directly from human saliva with the limit of detection (LOD) of about 400 copies per mL. This LOD is on par with that of qRT-PCR, making it 1000 to 100,000-fold more sensitive than commercial LFA tests. Using various combinations of negative selections during the screens for the aptamer targeting the receptor binding domain of the spike protein of SARS-CoV-2, we isolated two aptamers that can distinguish the Omicron and Delta variants. Integrating these two aptamers with LFA strips or the nanointerferometer sensors allows both detection and differentiation of the Omicron and Delta variants which has the potential to realize rapid triage of patients infected different SARS-CoV-2 variants.

Human antibody BD-218 has broad neutralizing activity against concerning variants of SARS-CoV-2.

As SARS-CoV-2 variants of concern (VOC) reduce the effectiveness of existing anti-COVID therapeutics, it is increasingly critical to identify highly potent neutralizing antibodies (nAbs) that bind to conserved regions across multiple variants, especially beta, delta, and omicron variants. Using single-cell sequencing with biochemical methods and pseudo-typed virus neutralization experiments, here we report the characterization of a potent nAb BD-218, identified from an early screen of patients recovering from the original virus. We have determined the cryo-EM structure of the BD-218/spike protein complex to define its epitope in detail, which revealed that BD-218 interacts with a novel epitope on the receptor-binding domain (RBD) of the spike protein. We concluded that BD-218 is a highly effective and broadly active nAb against SARS-CoV-2 variants with promising potential for therapeutic development.

Sentiment Analysis of Insomnia-Related Tweets via Combination of Transformers Using Dempster-Shafer Theory: A Pre-Peri COVID-19 Pandemic Retrospective Study.

BACKGROUND: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. OBJECTIVE: In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to assess the mental health condition of people experiencing insomnia after the outbreak of COVID-19. METHODS: We designed a retrospective study using public social media content from Twitter. We categorized insomnia-related tweets based on time into two intervals: pre-pandemic (01/01/2019 to 01/01/2020) and peri-pandemic (01/01/2020 to 01/01/2021). We performed sentiment analysis using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions into positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed temporal analysis to examine the effect of time on the users' insomnia experience, using logistic regression. RESULTS: We extracted 305,321 tweets containing the word insomnia (pre-pandemic, 139,561 and peri-pandemic, 165,760). The best combination of pretrained transformers via DST yielded 84% accuracy. Next, using this pipeline, we found that the odds of negative tweets (OR, 1.39; 95% CI, 1.37-1.41, P<.001) were higher peri-pandemic compared to pre-pandemic. The likelihood of negative tweets after midnight was 21% higher than before midnight (OR, 1.21; 95%CI: 1.19-1.23, P<.001). Peri-pandemic, while the odds of negative tweets were 2% higher after midnight compared to before midnight (OR, 1.02; 95%CI: 1.00-1.07, P = .008), they were 43% higher (OR, 1.43; 95%CI: 1.40-1.46, P<.001) peri-pandemic. CONCLUSIONS: The proposed novel sentiment analysis pipeline that combines pretrained transformers by DST is capable of classifying the emotion or sentiment of insomnia-related tweets. Twitter users shared more negative tweets about insomnia peri-pandemic than pre-pandemic. Future studies using a natural language processing framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

Factors Associated With COVID-19 Death in the United States: Cohort Study.

BACKGROUND: Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making. OBJECTIVE: This study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set. METHODS: A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic. RESULTS: A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19-related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46). CONCLUSIONS: This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19-related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.

Clinical Characteristics and Treatment Overview in Hand-Foot-and-Mouth Disease Using Real-World Evidence Based on Hospital Information System.

Objectives: To describe the epidemiological characteristics and medication overview of HFMD in Guangzhou and analyze the factors of length of stay (LOS) based on TCM usage. Method: From January 1, 2014, to June 30, 2019, clinical data of HFMD (ICD-10 B08.401) as the initial diagnosis, based on HIS of five medical institutions for outpatient and inpatient cases, was collected. The inpatient cases of the five hospitals in Guangzhou were utilized for hospitalization analysis. Information extracted from the warehouse was standardized. Descriptive analysis was used for baseline characteristics, medication usage, and inpatient characteristics. Potential factors were analyzed by bivariate analysis. COX regression analysis and Kaplan-Meier analysis for calculating HRs and 95% CIs were adopted to determine the predictors of LOS. Stratified COX regression was applied to analyze the relationship between predictors and LOS and to calculate interaction. Results: A total of 14172 patients with HFMD were included. It showed that HFMD would occur in males, infants, and summer. Cause and symptoms are the two aspects of conventional Western medicine treatments, while TCM treatment of HFMD took clearing heat and detoxification as the basic principle. Inpatients with HFMD were divided into two groups by the use ratio of TCM. Age, season, and disease severity were possible correlated factors of LOS, extrapolating from their disparity in distribution. By stratified Cox regression, three factors following presented as possible contributions to shortening LOS, including TCM ≥ 0.1 (HR = 1.79, 95% CI (1.67-1.92), P < 0.01), winter (HR = 1.28, 95% CI (1.12-1.47)), P < 0.01), mild HFMD (HR = 1.93, 95% CI (1.69-2.22), P < 0.01). Additive interaction of TCM use and disease severity was significant (RERI = 1.014 (0.493-1.534), P < 0.01). Conclusion: Young children and high temperature were the risk factors of HFMD infection, which suggests that increasing surveillance for susceptible particular-age individuals and season is indispensable. Favorable factors to decrease LOS included a higher proportion of TCM use, mild HFMD, and onset in winter. The proportion of TCM use had additive interaction with disease severity, indicating that TCM may have antiviral and other biological effects on HFMD. Increasing the proportion of TCM use was probably beneficial to shortening LOS.

Internet-based cognitive therapy for women with antenatal depressive symptoms during the COVID-19 pandemic: protocol for a multi-center randomized controlled trial across China.

BACKGROUND: Depression and anxiety are common among pregnant women. Internet-delivered psychological therapies such as cognitive behavioral therapy (iCBT) have been developed to increase accessibility and address common help-seeking barriers, especially during pandemic period. The objective of this trial is to evaluate the short-term and long-term effects of iCBT on reducing depressive symptoms among pregnant women during the COVID-19 pandemic with the overall goal of preventing depression recurrence in the first 12 months postpartum. METHODS: A multi-site randomized controlled trial will be conducted where 300 pregnant women early in their third trimester will be screened for depression symptoms using the Edinburgh Postnatal Depression Scale (EPDS) during a routine obstetrical visit. Eligible and consenting women with a score greater than 9 will be randomly allocated (1:1) to either intervention group or control group. ICBT involving the completion of 7 weekly online modules will be delivered via a well-designed perinatal mental healthcare app. The primary objective is to evaluate the effect of iCBT on reducing depression symptoms among pregnant Chinese women starting from their third trimester. The secondary objectives are to examine the effect of iCBT on anxiety, sleep quality, social support, parenting stress, co-parenting relationship, and infant development. DISCUSSION: This multi-center randomized controlled trial has been planned in accordance with best practices in behavioral trial design. The internet-based intervention addressed the needs of pregnant women during a major pandemic where face-to-face therapy is not preferable. The trial has a relatively large sample size with sufficient power to evaluate the efficacy of iCBT intervention for the primary and secondary outcomes. One year follow-up evaluation in the study is designed to determine the longer-term effect of the intervention on both maternal and infant outcomes. Although a limitation is the assessment of depression and anxiety using self-report measures, these easily incorporated and maternal-preferred assessments allow for real-life scalability if the intervention is proven to be effective. ETHICS AND DISSEMINATION: Ethics was approved by the institutional review board of International Peace Maternity and Child Health Hospital (GKLW2020-25). Dissemination of results will be published in peer-reviewed academic journals and presented at scientific conferences. TRIAL STATUS: The first patient was enrolled on 19 August 2020. To date, 203 participants have met eligibility requirements and been randomized to either the intervention group or control group. Data collection aims to be complete in September 2022. Date and version identifier: 2020715-version1.0. TRIAL REGISTRATION: ChiCTR2000033433. Registered 31 May 2020, http://www.chictr.org.cn/showproj.aspx?proj=54482 .

[Analysis of clinical characteristics of patients with different vaccines and underlying diseases infected with novel coronavirus Omicron variant].

OBJECTIVE: To analyze the clinical characteristics of patients inoculated with different vaccines and underlying diseases, infected with the novel coronavirus Omicron variant. METHODS: The data of 430 patients infected with the novel coronavirus Omicron variant who were admitted to Tianjin First Center Hospital from January 21, 2022 to March 7, 2022 were collected. A total of 108 patients with Omicron variant infection with underlying diseases were selected and enrolled. The gender, age, body mass index (BMI), history of underlying diseases, vaccination status (vaccination times, vaccination type), clinical symptoms, laboratory test indicators, imaging data, hospitalization time, nucleic acid negative conversion time, re-positivity and antibody titer from the two groups of the patients were collected and analyzed. RESULTS: In the 108 patients, 93 cases received inactivated vaccine and 15 cases received adenovirus vaccine. There was no statistically significant difference between the two groups in terms of gender, age, BMI, disease types, whether completed the fully vaccinated, whether had prime boost and underlying diseases. Both groups had fever, dry cough, sore throat, runny nose and other clinical symptoms, but there were no statistical difference between the two groups. There were no statistically significant differences in laboratory blood routine tests, biochemical indexes, C-reactive protein (CRP) level and the results of chest computed tomography (CT) imaging between the two groups. There were no statistically significant differences in hospitalization days, nucleic acid negative conversion time, whether admission to intensive care unit (ICU), turn re-positive on nucleic acid tests and immunoglobulin M (IgM) antibody titer expression between the two groups, but immunoglobulin G (IgG) antibody titer in adenovirus group was higher than that in inactivated group (g/L: 229.67±26.13 vs. 194.33±61.56, P = 0.020). There were also no significant differences in laboratory examinations, hospitalization days, nucleic acid negative conversion time, turn re-positive on nucleic acid tests and Novel coronavirus antibody titers expression of the patients with booster shots between the inactivated vaccine group and the adenovirus vaccine group. CONCLUSIONS: The protection of inactivated virus vaccine is equivalent to adenovirus vaccine in patients with underlying disease Omicron variant infection, and the titer of IgG antibody in patients with adenovirus vaccine is higher than that in patients with inactivated virus vaccine after one week of recovery.

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.

BACKGROUND: Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19. METHODS: In this study, we developed recurrent neural network-based models (CovRNN) to predict the outcomes of patients with COVID-19 by use of available electronic health record data on admission to hospital, without the need for specific feature selection or missing data imputation. CovRNN was designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and prolonged hospital stay (>7 days). For in-hospital mortality and mechanical ventilation, CovRNN produced time-to-event risk scores (survival prediction; evaluated by the concordance index) and all-time risk scores (binary prediction; area under the receiver operating characteristic curve [AUROC] was the main metric); we only trained a binary classification model for prolonged hospital stay. For binary classification tasks, we compared CovRNN against traditional machine learning algorithms: logistic regression and light gradient boost machine. Our models were trained and validated on the heterogeneous, deidentified data of 247 960 patients with COVID-19 from 87 US health-care systems derived from the Cerner Real-World COVID-19 Q3 Dataset up to September 2020. We held out the data of 4175 patients from two hospitals for external validation. The remaining 243 785 patients from the 85 health systems were grouped into training (n=170 626), validation (n=24 378), and multi-hospital test (n=48 781) sets. Model performance was evaluated in the multi-hospital test set. The transferability of CovRNN was externally validated by use of deidentified data from 36 140 patients derived from the US-based Optum deidentified COVID-19 electronic health record dataset (version 1015; from January, 2007, to Oct 15, 2020). Exact dates of data extraction were masked by the databases to ensure patient data safety. FINDINGS: CovRNN binary models achieved AUROCs of 93·0% (95% CI 92·6-93·4) for the prediction of in-hospital mortality, 92·9% (92·6-93·2) for the prediction of mechanical ventilation, and 86·5% (86·2-86·9) for the prediction of a prolonged hospital stay, outperforming light gradient boost machine and logistic regression algorithms. External validation confirmed AUROCs in similar ranges (91·3-97·0% for in-hospital mortality prediction, 91·5-96·0% for the prediction of mechanical ventilation, and 81·0-88·3% for the prediction of prolonged hospital stay). For survival prediction, CovRNN achieved a concordance index of 86·0% (95% CI 85·1-86·9) for in-hospital mortality and 92·6% (92·2-93·0) for mechanical ventilation. INTERPRETATION: Trained on a large, heterogeneous, real-world dataset, our CovRNN models showed high prediction accuracy and transferability through consistently good performances on multiple external datasets. Our results show the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering. FUNDING: Cancer Prevention and Research Institute of Texas.

Combining human and machine intelligence for clinical trial eligibility querying.

OBJECTIVE: To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. MATERIALS AND METHODS: Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for criteria selection and simplification, parsing error correction, and concept mapping. The accuracy, precision, recall, and F1 score of enhanced modules for negation scope detection, temporal and value normalization were evaluated using a previously curated gold standard, the annotated eligibility criteria of 1010 COVID-19 clinical trials. The usability and usefulness were evaluated by 10 research coordinators in a task-oriented usability evaluation using 5 Alzheimer's disease trials. Data were collected by user interaction logging, a demographic questionnaire, the Health Information Technology Usability Evaluation Scale (Health-ITUES), and a feature-specific questionnaire. RESULTS: The accuracies of negation scope detection, temporal and value normalization were 0.924, 0.916, and 0.966, respectively. C2Q 2.0 achieved a moderate usability score (3.84 out of 5) and a high learnability score (4.54 out of 5). On average, 9.9 modifications were made for a clinical study. Experienced researchers made more modifications than novice researchers. The most frequent modification was deletion (5.35 per study). Furthermore, the evaluators favored cohort queries resulting from modifications (score 4.1 out of 5) and the user engagement features (score 4.3 out of 5). DISCUSSION AND CONCLUSION: Features to engage domain experts and to overcome the limitations in automated machine output are shown to be useful and user-friendly. We concluded that human-computer collaboration is key to improving the adoption and user-friendliness of natural language processing.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

Analysis of clinical characteristics of 66 pediatric patients with B.1.617.2 (Delta) variant of COVID-19.

BACKGROUND: The aim of this study was to analyze the clinical characteristics of 66 pediatric patients with B.1.617.2 (Delta) variant of coronavirus disease 2019 (COVID-19). METHODS: Sixty-six pediatric patients with B.1.617.2 (Delta) variant of COVID-19 admitted to the hospital from July to August 2021 were classified into mild (n = 41) and moderate groups (n = 25). Clinical characteristics, laboratory data and dynamic trends in different time periods were analyzed retrospectively. RESULTS: There were no statistically significant differences in age, gender ratios and clinical symptoms between the mild group and the moderate group. All the patients in the moderate group had clusters of onsets, and the incubation period was shorter than that of the mild group. Within 24 hours of admission, the levels of erythrocyte sedimentation rate, cardiac troponin I, D-dimer in the moderate group were higher than that in the mild group (P < 0.05). The titers of immunoglobulin (Ig) G and IgM antibodies gradually increased after disease onset. Thirty-five (53.03%) children were tested positive for antibodies in 4-12 days. IgG increased gradually, while IgM decreased obviously in about 15 days after disease onset. The cycle threshold values of open reading frame 1ab and nucleocapsid protein gene in the severe acute respiratory syndrome coronavirus 2 genomes increased gradually on the 3rd, 6th, 9th, and 12th days after disease onset, compared with those in day 0. CONCLUSIONS: The symptoms of children with B.1.617.2 (Delta) variant of COVID-19 were mild. The description and analysis of the clinical characteristics and laboratory data can help medical staff to evaluate the condition of children with COVID-19 and to accumulate more clinical experience.

Chest CT features of children infected by B.1.617.2 (Delta) variant of COVID-19.

BACKGROUND: This study aimed to explore the imaging characteristics, diversity and changing trend in CT scans of pediatric patients infected with Delta-variant strain by studying imaging features of children infected with Delta and comparing the results to those of children with original COVID-19. METHODS: A retrospective, comparative analysis of initial chest CT manifestations between 63 pediatric patients infected with Delta variant in 2021 and 23 pediatric patients with COVID-19 in 2020 was conducted. Corresponding imaging features were analyzed. In addition, the changing trend in imaging features of COVID-19 Delta-variant cases were explored by evaluating the initial and follow-up CT scans. RESULTS: Among 63 children with Delta-variant COVID-19 in 2021, 34 (53.9%) showed positive chest CT presentation; and their CT score (1.10 ± 1.41) was significantly lower than that in 2020 (2.56 ± 3.5) (P = 0.0073). Lesion distribution: lung lesions of Delta cases appear mainly in the lower lungs on both sides. Most children had single lobe involvement (18 cases, 52.9%), 14 (41.2%) in the right lung alone, and 14 (41.2%) in both lungs. A majority of Delta cases displayed initially ground glass (23 cases, 67.6%) and nodular shadows (13 cases, 38.2%) in the first CT scan, with few extrapulmonary manifestations. The 34 children with abnormal chest CT for the first time have a total of 92 chest CT examinations. These children showed a statistically significant difference between the 0-3 day group and the 4-7 day group (P = 0.0392) and a significant difference between the 4-7 day group and the more than 8 days group (P = 0.0003). CONCLUSIONS: The early manifestations of COVID-19 in children with abnormal imaging are mostly small subpleural nodular ground glass opacity. The changes on the Delta-variant COVID-19 chest CT were milder than the original strain. The lesions reached a peak on CT in 4-7 days and quickly improved and absorbed after a week. Dynamic CT re-examination can achieve a good prognosis.

Factors associated with the time to return negative RT-PCR from COVID-19 in paediatric patients: a retrospective cohort study.

OBJECTIVE: This study aimed to describe the epidemiological and clinical features and potential factors related to the time to return negative reverse transcriptase (RT)-PCR in discharged paediatric patients with COVID-19. DESIGN: Retrospective cohort study. SETTING: Unscheduled admissions to 12 tertiary hospitals in China. PARTICIPANTS: Two hundred and thirty-three clinical charts of paediatric patients with confirmed diagnosis of COVID-19 admitted from 1 January 2020 to 17 April 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures: factors associated with the time to return negative RT-PCR from COVID-19 in paediatric patients. SECONDARY OUTCOME MEASURES: epidemiological and clinical features and laboratory results in paediatric patients. RESULTS: The median age of patients in our cohort was 7.50 (IQR: 2.92-12.17) years, and 133 (57.1%) patients were male. 42 (18.0%) patients were evaluated as asymptomatic, while 162 (69.5%) and 25 (10.7%) patients were classified as mild or moderate, respectively. In Cox regression analysis, longer time to negative RT-PCR was associated with the presence of confirmed infection in family members (HR (95% CI): 0.56 (0.41 to 0.79)). Paediatric patients with emesis symptom had a longer time to return negative (HR (95% CI): 0.33 (0.14 to 0.78)). During hospitalisation, the use of traditional Chinese medicine (TCM) and antiviral drugs at the same time is less conducive to return negative than antiviral drugs alone (HR (95% CI): 0.85 (0.64 to 1.13)). CONCLUSIONS: The mode of transmission might be a critical factor determining the disease severity of COVID-19. Patients with emesis symptom, complications or confirmed infection in family members may have longer healing time than others. However, there were no significant favourable effects from TCM when the patients have received antiviral treatment.

A retrospective analysis of medication in children with SARS-CoV-2 infection in Wuhan, China

OBJECTIVE: To study the medication in children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Wuhan, China, and to provide a reference for rational drug use in clinical practice. METHODS: A retrospective analysis was performed on the medical data of the children who were diagnosed with SARS-CoV-2 infection from January 26 to March 5, 2020. The children were divided into an asymptomatic group with 41 children and a symptomatic group with 73 children. A subgroup analysis was performed to investigate the effect of different antiviral regimens (monotherapy, double therapy, or triple therapy) and whether interferon α-1b was used in combination with azithromycin on the length of hospital stay and the clearance time of SARS-CoV-2 nucleic acid. RESULTS: A total of 114 children with SARS-CoV-2 infection (72 boys and 42 girls) were enrolled. The median age of the children was 7.1 years. The median length of hospital stay was 10 days and the clearance time of SARS-CoV-2 nucleic acid was 6 days. In either group, the subgroup analysis showed no significance differences in the length of hospital stay and the clearance time of SARS-CoV-2 nucleic acid between the subgroups treated with different combinations of antiviral drugs and the subgroups treated with interferon α-1b alone or in combination with azithromycin (P > 0.05). CONCLUSIONS: It is not recommended to use the routine combinations of antiviral drugs for children with SARS-COV-2 infection or combine with azithromycin for the purpose of antiviral therapy.