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1.
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262

ABSTRACT

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/therapy , Logistic Models , Machine Learning , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Adolescent , Adult , Aged , COVID-19 , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Young Adult
2.
Evid Based Complement Alternat Med ; 2021: 4303380, 2021.
Article in English | MEDLINE | ID: covidwho-1455773

ABSTRACT

BACKGROUND: In view of the global efforts to develop effective treatments for the current worldwide coronavirus 2019 (COVID-19) pandemic, Qingfei Paidu decoction (QPD), a novel traditional Chinese medicine (TCM) prescription, was formulated as an optimized combination of constituents of classic prescriptions used to treat numerous febrile and respiratory-related diseases. This prescription has been used to treat patients with COVID-19 pneumonia in Wuhan, China. Hypothesis/Purpose. We hypothesized that QPD would have beneficial effects on patients with COVID-19. We aimed to prove this hypothesis by evaluating the efficacy of QPD in patients with COVID-19 pneumonia. METHODS: In this single-center, retrospective, observational study, we identified eligible participants who received a laboratory diagnosis of COVID-19 between January 15 and March 15, 2020, in the west campus of Union Hospital in Wuhan, China. QPD was supplied as an oral liquid packaged in 200-mL containers, and patients were orally administered one package twice daily 40 minutes after a meal. The primary outcome was death, which was compared between patients who did and did not receive QPD (QPD and NoQPD groups, respectively). Propensity score matching (PSM) was used to identify cohorts. RESULTS: In total, 239 and 522 participants were enrolled in the QPD and NoQPD groups, respectively. After PSM at a 1 : 1 ratio, 446 patients meeting the criteria were included in the analysis with 223 in each arm. In the QPD and NoQPD groups, 7 (3.2%) and 29 (13.0%) patients died, and those in the QPD group had a significantly lower risk of death (hazard ratio (HR) 0.29, 95% CI: 0.13-0.67) than those in the NoQPD group (p = 0.004). Furthermore, the survival time was significantly longer in the QPD group than in the NoQPD group (p < 0.001). CONCLUSION: The use of QPD may reduce the risk of death in patients with COVID-19 pneumonia.

3.
Epidemiol Infect ; 149: e61, 2021 02 24.
Article in English | MEDLINE | ID: covidwho-1124622

ABSTRACT

A fever clinic within a hospital plays a vital role in pandemic control because it serves as an outpost for pandemic discovery, monitoring and handling. As the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan was gradually brought under control, the fever clinic in the West Campus of Wuhan Union Hospital introduced a new model for construction and management of temporary mobile isolation wards. A traditional battlefield hospital model was combined with pandemic control regulations, to build a complex of mobile isolation wards that used adaptive design and construction for medical operational, medical waste management and water drainage systems. The mobile isolation wards allowed for the sharing of medical resources with the fever clinic. This increased the capacity and efficiency of receiving, screening, triaging and isolation and observation of patients with fever. The innovative mobile isolation wards also controlled new sudden outbreaks of COVID-19. We document the adaptive design and construction model of the novel complex of mobile isolation wards and explain its characteristics, functions and use.


Subject(s)
Fever/therapy , Models, Organizational , Patient Isolation/methods , COVID-19/complications , COVID-19/epidemiology , China/epidemiology , Fever/epidemiology , Humans , Infection Control/instrumentation , Infection Control/methods , Patient Isolation/trends
4.
World J Otorhinolaryngol Head Neck Surg ; 6: S40-S48, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-306374

ABSTRACT

OBJECTIVE: Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve control and prevention. METHODS: Using the Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020 and the epidemic data in Hubei province and the other top 9 impacted provinces in China. Data of 2020 were compared with those of the previous three years. Data of Hubei province were compared with those of the other 9 provinces. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlations between the SV of COVID-19 and the number of newly confirmed/suspected cases were analyzed. The lag effects were discussed. RESULTS: Comparing the SV from January 1, 2020 to February 20, 2020 with those for the same period of the previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea, and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P<0.001). The SV of COVID-19 in Hubei province was significantly correlated with the number of newly confirmed/suspected cases (r confirmed = 0.723, r suspected = 0.863, both p < 0.001). The results of the distributed lag model suggested that the patients who searched relevant symptoms on the Internet may begin to see doctors in 2-3 days later and be confirmed in 3-4 days later. CONCLUSION: The total SV of lower respiratory symptoms was higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warned us to pay attention to not only the symptoms of the lower respiratory tract but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. Internet search behavior had a positive correlation with the number of newly confirmed/suspected cases, suggesting that big data has an important role in the early warning of infectious diseases.

5.
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