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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-320695

ABSTRACT

Background: Since 2020 COVID-19 pandemic became an emergent public sanitary incident. The epidemiology data and the impact on prognosis of secondary infection in severe and critical COVID-19 patients in China remained largely unclear. Methods: . We retrospectively reviewed medical records of all adult patients with laboratory-confirmed COVID-19 who were admitted to ICUs from January 18 th 2020 to April 26 th 2020 at two hospitals in Wuhan, China and one hospital in Guangzhou, China. We measured the frequency of bacteria and fungi cultured from respiratory tract, blood and other body fluid specimens. The risk factors for and impact of secondary infection on clinical outcomes were also assessed. Results: . Secondary infections were very common (86.6%) when patients were admitted to ICU for >72 hours. The majority of infections were respiratory, with the most common organisms being Klebsiella pneumoniae (24.5%), Acinetobacter baumannii (21.8%), Stenotrophomonas maltophilia (9.9%), Candida albicans (6.8%), and Pseudomonas spp. (4.8%). Furthermore, the proportions of multidrug resistant (MDR) bacteria and carbapenem resistant Enterobacteriaceae (CRE) were high. We also found that age ≥60 years and mechanical ventilation ≥13days independently increased the likelihood of secondary infection. Finally, patients with positive cultures had reduced ventilator free days in 28 days and patients with CRE and/or MDR bacteria positivity showed lower 28 day survival rate. Conclusions: . In a retrospective cohort of severe and critical COVID-19 patients admitted to ICUs in China, the prevalence of secondary infection was high, especially with CRE and MDR bacteria, resulting in poor clinical outcomes.

2.
Eur Radiol ; 32(4): 2235-2245, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1606144

ABSTRACT

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.


Subject(s)
Artificial Intelligence , COVID-19 , Algorithms , Humans , Radiologists , Tomography, X-Ray Computed/methods
3.
Ann Palliat Med ; 10(8): 8557-8570, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1353025

ABSTRACT

BACKGROUND: Since 2020 COVID-19 pandemic became an emergent public sanitary incident. The epidemiology data and the impact on prognosis of secondary infection in severe and critical COVID-19 patients in China remained largely unclear. METHODS: We retrospectively reviewed medical records of all adult patients with laboratory-confirmed COVID-19 who were admitted to ICUs from January 18th 2020 to April 26th 2020 at two hospitals in Wuhan, China and one hospital in Guangzhou, China. We measured the frequency of bacteria and fungi cultured from respiratory tract, blood and other body fluid specimens. The risk factors for and impact of secondary infection on clinical outcomes were also assessed. RESULTS: Secondary infections were very common (86.6%) when patients were admitted to ICU for >72 hours. The majority of infections were respiratory, with the most common organisms being Klebsiella pneumoniae (24.5%), Acinetobacter baumannii (21.8%), Stenotrophomonas maltophilia (9.9%), Candida albicans (6.8%), and Pseudomonas spp. (4.8%). Furthermore, the proportions of multidrug resistant (MDR) bacteria and carbapenem resistant Enterobacteriaceae (CRE) were high. We also found that age ≥60 years and mechanical ventilation ≥13 days independently increased the likelihood of secondary infection. Finally, patients with positive cultures had reduced ventilator free days in 28 days and patients with CRE and/or MDR bacteria positivity showed lower 28-day survival rate. CONCLUSIONS: In a retrospective cohort of severe and critical COVID-19 patients admitted to ICUs in China, the prevalence of secondary infection was high, especially with CRE and MDR bacteria, resulting in poor clinical outcomes.


Subject(s)
COVID-19 , Coinfection , Cross Infection , Adult , Anti-Bacterial Agents/therapeutic use , Coinfection/drug therapy , Cross Infection/drug therapy , Cross Infection/epidemiology , Humans , Middle Aged , Pandemics , Retrospective Studies , SARS-CoV-2
6.
Exp Hematol Oncol ; 10(1): 6, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-1058277

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with coagulation abnormalities which are indicators of higher mortality especially in severe cases. METHODS: We studied patients with proven COVID-19 disease in the intensive care unit of Jinyintan Hospital, Wuhan, China from 30 to 2019 to 31 March 2020. RESULTS: Of 180 patients, 89 (49.44 %) had died, 85 (47.22 %) had been discharged alive, and 6 (3.33 %) were still hospitalised by the end of data collection. A D-dimer concentration of > 0.5 mg/L on admission was significantly associated with 30 day mortality, and a D-dimer concentration of > 5 mg/L was found in a much higher proportion of non-survivors than survivors. Sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC) scoring systems were dichotomised as < 4 or ≥ 4 and < 5 or ≥ 5, respectively, and the mortality rate was significantly different between the two stratifications in both scoring systems. Enoxaparin was administered to 68 (37.78 %) patients for thromboembolic prophylaxis, and stratification by the D-dimer concentration and DIC score confirmed lower mortality in patients who received enoxaparin when the D-dimer concentration was > 2 than < 2 mg/L or DIC score was ≥ 5 than < 5. A low platelet count and low serum calcium concentration were also related to mortality. CONCLUSIONS: A D-dimer concentration of > 0.5 mg/L on admission is a risk factor for severe disease. A SIC score of > 4 and DIC score of > 5 may be used to predict mortality. Thromboembolic prophylaxis can reduce mortality only in patients with a D-dimer concentration of > 2 mg/L or DIC score of ≥ 5.

7.
Nat Commun ; 11(1): 3543, 2020 07 15.
Article in English | MEDLINE | ID: covidwho-974925

ABSTRACT

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.


Subject(s)
Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Deep Learning/statistics & numerical data , Pneumonia, Viral/diagnosis , Pneumonia, Viral/pathology , Triage/methods , Betacoronavirus , COVID-19 , Critical Illness , Hospitalization , Humans , Middle Aged , Models, Theoretical , Pandemics , Prognosis , Risk , SARS-CoV-2 , Survival Analysis
8.
Front Public Health ; 8: 576528, 2020.
Article in English | MEDLINE | ID: covidwho-953930

ABSTRACT

In December 2019, human infection with a novel coronavirus, known as SARS-CoV-2, was confirmed in Wuhan, China, and spread rapidly beyond Wuhan and around the world. By 7 May 2020, a total of 84,409 patients were infected in mainland China, with 4,643 deaths, according to a Chinese Center for Disease Control and Prevention report. Recent studies reported that critically ill patients were presented with high mortality. However, the clinical experiences of patients with coronavirus disease 2019 (COVID-19) have not been described in Guangdong Province, where by 7 May 2020, 1,589 people had been confirmed as having COVID-19 but with a very low mortality of 8 death (0.5%). Here, we describe the experience of critical care response to the outbreak of SARS-CoV-2 in Guangdong Province in the following points: Early intervention by the government, Establishment of a Multidisciplinary Working Group, Prompt intensive care interventions, Adequate ICU beds and Human resource in ICU, Infection control practices.


Subject(s)
COVID-19 , China/epidemiology , Critical Care , Disease Outbreaks/prevention & control , Humans , SARS-CoV-2
10.
Sleep Med ; 75: 294-300, 2020 11.
Article in English | MEDLINE | ID: covidwho-744275

ABSTRACT

PURPOSE: To explore the relationship between symptomless multi-Variable apnea prediction (sMVAP) index and adverse outcomes of patients with Corona Virus Disease 2019 (COVID-19). METHODS: According to the sMVAP quartiles, we divided all patients into four groups. The clinical electronic medical records, nursing records, laboratory findings, and radiological examinations for all patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection were reviewed. Cox proportional hazard ratio (HR) models were used to determine the risk factors associated with in hospital death. RESULTS: A total of 97 patients were included in this study. The "Quartile 4" group 's ICU transfer rate was significantly higher than the "Quartile 1" group. Coronary heart disease, high d-dimer and sMVAP at admission were associated with increased odds of death. CONCLUSIONS: Using the sMVAP index for obstructive sleep apnea hypopnea syndrome (OSAHS) risk assessment, and then predicting the adverse outcomes of COVID-19 patients, is an effective method. Therefore, the use of sMVAP index for OSAHS screening for inpatients with COVID-19 should be vigorously promoted, and high-risk patients should be effectively managed.


Subject(s)
COVID-19/mortality , Hospital Mortality , Sleep Apnea, Obstructive/diagnosis , Adult , Aged , COVID-19/physiopathology , Comorbidity , Female , Humans , Male , Middle Aged , Proportional Hazards Models , Retrospective Studies , Risk Assessment , SARS-CoV-2 , Severity of Illness Index , Sleep Apnea, Obstructive/epidemiology , Sleep Apnea, Obstructive/physiopathology
11.
Sleep Med ; 75: 354-360, 2020 11.
Article in English | MEDLINE | ID: covidwho-716942

ABSTRACT

PURPOSE: To determine the relationship between the improved night shift schedule and the mortality of critically ill patients with Corona Virus Disease 2019 (COVID-19). METHODS: According to the time of the implementation of the new night shift schedule, we divided all patients into two groups: initial period group and recent period group. The clinical electronic medical records, nursing records, laboratory findings, and radiological examinations for all patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection were reviewed. Cox proportional hazard ratio (HR) models were used to determine the risk factors associated with in hospital death. RESULTS: A total of 75 patients were included in this study. Initial period group includes 45 patients and recent period group includes 30 patients. The difference in mortality between the two groups was significant, 77.8% and 36.7%, respectively. Leukocytosis at admission and admitted to hospital before the new night shift schedule were associated with increased odds of death. CONCLUSIONS: Shift arrangement of medical staff are associated with the mortality of critically ill patients with COVID-19. The new night shift schedule might improve the continuity of treatment, thereby improving the overall quality of medical work and reducing the mortality of critically ill patients.


Subject(s)
COVID-19/mortality , Shift Work Schedule/statistics & numerical data , Aged , Case-Control Studies , Comorbidity , Female , Hospital Mortality , Humans , Male , Middle Aged , Proportional Hazards Models , Quality Improvement , Retrospective Studies , SARS-CoV-2
12.
JAMA Intern Med ; 180(8): 1081-1089, 2020 08 01.
Article in English | MEDLINE | ID: covidwho-245503

ABSTRACT

Importance: Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/). Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/standards , Coronavirus Infections/physiopathology , Critical Care/organization & administration , Critical Illness/therapy , Pneumonia, Viral/physiopathology , Adult , Aged , COVID-19 , COVID-19 Testing , China , Cohort Studies , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Risk Assessment/standards , SARS-CoV-2
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