3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Albumin level as an independent predictive factor for adverse outcomes in COVID-19 patients: a retrospective cohort study.

INTRODUCTION: Research on the association between albumin (ALB) level and clinical outcomes of coronavirus disease 2019 (COVID-19) are limited. This study aimed to investigate the relationship between albumin level at the time of admission and adverse outcomes in patients with COVID-19. METHODOLOGY: This was a retrospective cohort study with 199 COVID-19 patients from five designated hospitals in Fujian Province who were enrolled between 22 January and 27 February, 2020. Clinical characteristics and laboratory values at the time of admission were collected. Adverse outcomes were defined as meeting at least one of the following criteria: development of acute respiratory distress syndrome (ARDS), respiratory failure, shock, multiple organ failure (MOF), intensive care unit (ICU) admission and in-hospital mortality event. The univariate and multivariate linear regression models and generalized additive models (GAM) were used to analyze the relationship between ALB and adverse outcomes. RESULTS: A non-linear relationship with an inflection point of 32.6g/L was detected between ALB and adverse outcomes after adjusting for potential confounders. The odds ratio and the confidence intervals on the left and right sides of the inflection point were 0.204 (0.061-0.681) and 0.908 (0.686-1.203), respectively. This suggested that ALB was negatively correlated with adverse outcomes when ALB was less than 32.6 g/L, and for every 1 unit increase in ALB, the risk of adverse outcomes was reduced by 79.6%. CONCLUSIONS: The relationship between ALB and adverse outcomes of COVID-19 is non-linear. ALB level is an independent predictive factor for adverse outcomes in COVID-19 patients.

The real-time remote testing and programming of cardiac implantable electronic devices: A case series report.

Minimizing the number of personnel in the cardiac catheterization laboratory (CCL) and the times of CCL door openings contribute to reduce the infection risk of medical staff and patients, particularly during the COVID-19 pandemic. The usage of 5G-CTP system enables device specialists to conduct remote parameter testing and programming without entering the CCL, potentially reducing the exposure risk of medical staff and patients to COVID-19 infection.

Application of Postoperative Remote Follow-Up of CIED Based on the 5G Cloud Technology Support Platform in Areas With Underdeveloped Medical Resources.

Postoperative follow-up is crucial for the clinical management of patients carrying cardiovascular implantable electronic devices (CIED). However, in a plethora of underdeveloped areas of China, due to limited medical resources and associated economic costs, geographical restrictions, the outbreak of the COVID-19 pandemic, and various other reasons, the medical system is unable to meet the ever-increasing demand for long-term clinical follow-up and telemedicine services. Based on these challenges, postoperative remote follow-up of CIED based on the 5G-cloud technology support platform (5G-CTP) may have the potential to optimize the allocation of medical resources and provide patients with high-quality CIED follow-up services locally. These unique characteristics of CIED follow-up utilizing 5G-CTP are qualified to protect the safety of the patients in terms of both clinical safety and cyber security. Furthermore, during the COVID-19 pandemic, remote follow-up of CIED significantly reduces the risk of viral exposure to patients and medical staff while having the potential to improve the current situation of CIED postoperative follow-up.

Realtime Remote Programming in Patients Carrying Cardiac Implantable Electronic Devices Requiring Emergent Reprogramming.

To protect cardiac implantable electronic device (CIED) patients with arrhythmia or possible device malfunction, it is important for health care professionals to provide emergent device evaluation and reprogramming. This case series illustrated the clinical application of realtime remote programming in CIED patients requiring emergent in-person evaluation and reprogramming (ChiCTR2100046883 chictr.org). All remote sessions were performed safely and efficiently by remote electrophysiologists without being in the physical presence of a patient. The implementation of realtime remote programming not only largely reduces the response time to urgent events but also greatly helps to minimize personnel exposure to COVID-19 infection.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Application of Postoperative Remote Follow-Up of CIED Based on the 5G Cloud Technology Support Platform in Areas With Underdeveloped Medical Resources

Postoperative follow-up is crucial for the clinical management of patients carrying cardiovascular implantable electronic devices (CIED). However, in a plethora of underdeveloped areas of China, due to limited medical resources and associated economic costs, geographical restrictions, the outbreak of the COVID-19 pandemic, and various other reasons, the medical system is unable to meet the ever-increasing demand for long-term clinical follow-up and telemedicine services. Based on these challenges, postoperative remote follow-up of CIED based on the 5G-cloud technology support platform (5G-CTP) may have the potential to optimize the allocation of medical resources and provide patients with high-quality CIED follow-up services locally. These unique characteristics of CIED follow-up utilizing 5G-CTP are qualified to protect the safety of the patients in terms of both clinical safety and cyber security. Furthermore, during the COVID-19 pandemic, remote follow-up of CIED significantly reduces the risk of viral exposure to patients and medical staff while having the potential to improve the current situation of CIED postoperative follow-up.

Cloud Follow-Up in Patients With Cardiovascular Implantable Electronic Devices: A Single-Region Study in China.

Background: Due to seriously imbalanced distribution of follow-up clinics in China, routine in-office visits are erratically attended by many cardiovascular implantable electronic device (CIED) patients. Meanwhile, remote monitoring is significantly underutilized. Novel tools to address the current predicament of routine in-office visits in China is urgently needed. Objectives: To assess the reliability and feasibility of cloud follow-up in CIED patients. Methods: A total of 325 CIED patients from 13 hospitals in Sichuan Province, China, were enrolled. Information on patients' sociodemographic and basic clinical characteristics was collected. All devices were tested and programmed with 5G-cloud follow-up platform in a real-time manner. All patients were surveyed about their acceptance of and preferences regarding cloud follow-up compared to routine in-office visits. Results: Compliance with routine in-office visits in this region was 60.6%. None of the patients were enrolled in remote monitoring services. Clinically important predictors of non-compliance were elderly age (≥75 years old), odds ratio (OR) 2.392 (95% confidence interval, 1.111-5.150); needing notification from a follow-up clinic, OR 2.518 (1.179-5.376); and being beyond 15 months post-implantation, OR 5.440 (2.563-11.543). All cloud follow-up sessions were performed safely and efficiently, without any adverse events. 292 (89.8%) patients preferred cloud follow-up for future device management. Conclusion: Compliance with routine in-office visits in this region has much room for improvement. Cloud follow-up addresses the limitations of an imbalanced distribution of follow-up clinics and geographic barriers for in-office CIED evaluation. Thus, cloud follow-up provides a potential solution to the current predicament of routine in-office visits in China.

Effect of Qingfei Paidu decoction combined with Western medicine treatments for COVID-19: A systematic review and meta-analysis.

BACKGROUND: Qingfei Paidu decoction (QFPDD) showed to be beneficial for the treatment of coronavirus disease 2019 (COVID-19) in China. PURPOSE: This study aimed to systematically assemble the evidence on the efficacy and safety of QFPDD combined with Western medicine treatments (WMT) for COVID-19. STUDY DESIGN: Systematic review and meta-analysis. METHODS: A comprehensive literature search was conducted in PubMed, Embase, Cochrane Library, CNKI, CSTJ, CBM, Wanfang Data for clinical trials with a control arm until January 13, 2022. Studies matched the selection criteria were included. Data extraction and quality assessment of the included studies were independently conducted by two reviewers. Review Manager 5.4 was used for meta-analysis. RESULTS: A total of 9 trials including 1108 COVID-19 patients met the selection criteria. Meta-analysis demonstrated that QFPDD combined with WMT reduced aggravation rate (AR) by 71% [risk ratio (RR) = 0.29, 95% confidence intervals (CI) (0.17, 0.51)], increased effective rate (ER) by 13% [RR = 1.13, 95%CI (1.04, 1.22)], shortened 4.78 days of viral shedding [95%CI (-5.79, -3.77)] and 4.45 days of hospital stay [95%CI (-6.05, -2.86)], also decreased the incidence of adverse events (AE) by 56% [RR = 0.44, 95%CI (0.22, 0.89)]. CONCLUSION: QFPDD combined with WMT might reduce the proportion of severe cases and the incidence of AE, shorten the duration of viral shedding and length of hospital stay. More randomized controlled trials (RCTs) are required to confirm our findings in the future.

Clinical use of real-time remote programming in pacemakers during the COVID-19 pandemic: A case report.

We report a case in which real-time remote interrogation and reprogramming of the parameters of a dual-chamber pacemaker was performed during the COVID-19 pandemic. The described case demonstrated the safety and effectiveness of CIED remote programming based on the 5G cloud technology support platform (5G-CTP), and showed that the application of real-time remote programming would help in reducing the risk of cross-infection between doctors and patients.

Medication Rule Analysis of the Diagnosis and Treatment Programs of Chinese Medicine for the Prevention and Treatment of COVID-19 in China.

OBJECTIVES: To summarize the measures and rules of Chinese medicine (CM) and provide reference for clinical application in the prevention and treatment of coronavirus disease (COVID-19). METHODS: The data source was from CM COVID-19 prevention and treatment programs on government websites and official media websites of the different provinces and cities. The search lasted from December 8, 2019 to March 10, 2020. Main variables were medication frequency and combinations of medicines. Cluster analysis and complex network analysis were used by prevention and treatment stage and by area. RESULTS: Among 27 CM diagnosis and treatment plans, 203 therapeutic prescriptions were enrolled, of which the top 4 herbs were: Radix glycyrrhizae, Semen armeniacae amarum, Herba ephedrae, and Herba agastachis, respectively. The core combinations were Herba ephedrae and Semen armeniacae amarum. Forty-eight preventive formulae were identified. Ten herbs, including Radix Astragali seu hedysari, Radix glycyrrhizae, Radix saposhnikoviae, Flos lonicerae, etc. were most frequently used. The core prescription of CM compatibility was Radix astragali seu hedysari, Radix glycyrrhizae, and Radix saposhnikoviae, which is the main component of Yu Ping Feng San. There were 45 prevention and treatment prescriptions in East China; the most used CM was Radix glycyrrhizae, Herba agastachis, Pericarpium citri reticulatae, and Gypsum fibrosum. Fifty prescriptions were identified in North China. According to CM analyses, Herba agastachis, Semen armeniacae amarum, Herba ephedrae, and Poria were most frequently used. CONCLUSIONS: CM for COVID-19 prevention mainly focuses on improving human immunity; for treatment, prescription focuses on clearing the lungs and removing dampness. Prescriptions vary with regions, perhaps due to climatic and environmental differences, which help clinicians to quickly make CM plans and treat patients according to clinical status, further minimizing resource wastage.

Association between early treatment with Qingfei Paidu decoction and favorable clinical outcomes in patients with COVID-19: A retrospective multicenter cohort study.

The coronavirus disease 2019 (COVID-19) epidemic has been almost controlled in China under a series of policies, including "early diagnosis and early treatment". This study aimed to explore the association between early treatment with Qingfei Paidu decoction (QFPDD) and favorable clinical outcomes. In this retrospective multicenter study, we included 782 patients (males, 56 %; median age 46) with confirmed COVID-19 from 54 hospitals in nine provinces of China, who were divided into four groups according to the treatment initiation time from the first date of onset of symptoms to the date of starting treatment with QFPDD. The primary outcome was time to recovery; days of viral shedding, duration of hospital stay, and course of the disease were also analyzed. Compared with treatment initiated after 3 weeks, early treatment with QFPDD after less than 1 week, 1-2 weeks, or 2-3 weeks had a higher likelihood of recovery, with adjusted hazard ratio (HR) (95 % confidence interval [CI]) of 3.81 (2.65-5.48), 2.63 (1.86-3.73), and 1.92 (1.34-2.75), respectively. The median course of the disease decreased from 34 days to 24 days, 21 days, and 18 days when treatment was administered early by a week (P < 0.0001). Treatment within a week was related to a decrease by 1-4 days in the median duration of hospital stay compared with late treatment (P<0.0001). In conclusion, early treatment with QFPDD may serve as an effective strategy in controlling the epidemic, as early treatment with QFPDD was associated with favorable outcomes, including faster recovery, shorter time to viral shedding, and a shorter duration of hospital stay. However, further multicenter, prospective studies with a larger sample size should be conducted to confirm the benefits of early treatment with QFPDD.

Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19)

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.

[Application of three-in-one intelligent screening in outpatient department of children's hospital during COVID-19 epidemic].

OBJECTIVE: To evaluate the application of three-in-one intelligent screening in outpatient pre-inspection in children's hospital. METHODS: We randomly enrolled 100 children pre-screened by traditional method in the outpatient department of Children's Hospital of Zhejiang University from February 6th to 16th, 2020, and another 100 children by the intelligent three-in-one mode from February 17th to 27th, 2020. The traditional triage was conducted by nurses based on face-to-face, one-by-one interview of the epidemiological history and consultation department, and the temperature was measured before manual triage. The intelligent three-in-one model combined online rapid pre-inspection and triage, on-site manual confirmation, as well as synchronized online health education system. For on-line registered patients, the system automatically sent the COVID-19 epidemiological pre-screening triage questionnaire one hour before the appointment, requiring parents to complete and submit online before arriving at the hospital. The on-site registered patients were controlled at 100 m away from the hospital entrance. The nurses guided the parents to scan the QR code and fill in the COVID-19 epidemiological pre-examination triage questionnaire. At the entrance of the hospital, the nurse checked the guidance sheet and took the temperature again. The children with red guidance sheet were checked again and confirmed by pre-examination nurses, and accompanied to the isolation clinic through COVID-19 patients-only entrance. The children with yellow guidance sheet were guided to fever clinic. The children with green guidance sheet could go with their parents to the designated area, and then went to the corresponding consultation area. Health education was carried out throughout the treatment, and the system automatically posted the corresponding outpatient instructions and education courses. Parents would read the courses on their mobile phones and counsel online. The time of pre-examination and the coincidence rate of triage were compared between the two groups. RESULTS: The three-in-one intelligent pre-inspection mode took an average of (25.6±8.0) s for each child, which was significantly shorter than the traditional pre-inspection mode (74.8±36.4) s (t=13.182, P<0.01). The triage coincidence rate of the intelligent pre-inspection model was 98%, which was similar to that of traditional model (97%, χ2=0.251, P>0.05). CONCLUSIONS: The three-in-one intelligent pre-inspection model can effectively shorten the patient pre-check time, with similar triage coincidence rate to traditional model.

[Analysis of potential role of Chinese classic prescriptions in treatment of COVID-19 based on TCMATCOV platform].

With the global outbreak of coronavirus disease 2019(COVID-19), screening of effective drugs has became the emphasis of research today; furthermore, screening of Chinese classic prescriptions has became one of the directions for drug development. This study analyzed the application of classic prescriptions in the diagnosis and treatment schemes based on the Diagnosis and Treatment Schemes for Coronavirus Disease at the country, provincial and municipal levels, and further explored its disrobing effect on COVID-19 disease severe phase network, and selected representative prescriptions for core target screening and gene enrichment analysis, so as to reveal its mechanism of action. Among them, 13 prescriptions were found to be used for 10 times or more, including Maxing Shigan Tang, Yinqiao San, Shengjiang San, Dayuan Drink, Xuanbai Chengqi Decoction. In addition, the COVID-19 efficacy prediction analysis platform(TCMATCOV platform) was used to calculate the network disturbances of the Chinese classic prescriptions involved. Based on the prediction results, 68 classic prescriptions were assessed on the COVID-19 disease network robustness disturbance. The average disturbance scores for the interaction confidence scores were ranked to be 0.4, 0.5, and 0.6 from the highest to the lowest. There were 7 prescriptions with a score of 17 or more, and 50 prescriptions with a score of 13 or more. Among them, the top three prescriptions were Ganlu Xiaodu Dan(18.19), Lengxiao Wan(17.74), and Maxing Shigan Tang(17.62). After further mining the action targets of these three prescriptions, it was found that COVID-19 disease-specific factors Ccl2, IL10, IL6 and TNF were all the targets of three prescriptions. Through the enrichment analysis of the biological processes of the core targets, it was found that the three prescriptions may prevent the development of the disease by affecting cell-to-cell adhesion, cytokine-mediated signaling pathway, and chronic inflammatory responses to COVID-19 at the severe phase. This study showed that the TCMATCOV platform could evaluate the disturbance effect of different prescriptions on the COVID-19 disease network, and predict potential effectiveness based on the robustness of drug-interfered pneumonia disease networks, so as to provide a reference for further experiments or clinical verification.