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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-312191

ABSTRACT

Background: To analyze the clinical characteristics of the re-positive discharged COVID-19 patients and find markers to distinguish them. Methods: : The demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, illness severities and chest CT scan results of 267 patients were collected during 1st January and 15th February 2020. COVID-19 was diagnosed by RT-PCR. The subsequent clinical symptoms and nucleic acid test results was obtained during the 14 days post-hospitalization quarantine. Results: : 30 out of 267 COVID-19 patients were detected re-positive during the post-hospitalization quarantine. Re-positive patients couldn’t be distinguished by demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, chest CT scan results or subsequent clinical symptoms. However, the re-positive rate were found illness severity correlated, along with APACHE II and CURB-65. Conclusion: Common clinical characteristics arn’t able to distinguish re-positive patients. However, severe and critical cases with high APACHE II and CURB-65 scores are more likely to turn re-positive after discharge.Authors Shengyang He, Wenwu Sun, Kefu Zhou contributed equally to this work.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311721

ABSTRACT

Background: Pulmonary embolism is a severe condition prone to misdiagnosis given its nonspecific signs and symptoms. Previous studies on the pneumonia outbreak caused by coronavirus disease 2019 (COVID-19) showed a number of patients with elevated d-dimer, whether those patients combined with pulmonary embolism got our attention. Methods: Data on clinical manifestations, laboratory and radiological findings, treatment, and disease progression of 19 patients with laboratory-confirmed COVID-19 pneumonia,who completed computed tomographic pulmonary angiography (CTPA) during hospitalization in the Central Hospital of Wuhan from January 2 to March 26, 2020, were reviewed. Results: Of the 19 suspected pulmonary embolism and subjected to CTPA patients, six were diagnosed with pulmonary embolism. The Wells’ score of the six patients with pulmonary embolism was 0–1, which suggested a low risk of pulmonary embolism. The median level of d-dimers collected at the day before or on the day of CTPA completion in the patients with pulmonary embolism was 18.36 (interquartile range [IQR]: 6.69–61.46) µg/mL, which was much higher than that in the patients without pulmonary embolism (median 9.47 [IQR: 4.22–28.02] µg/mL). Of the 6 patients diagnosed with pulmonary embolism, all patients received anticoagulant therapy, 5 of which survived and were discharged and 1 died. Conclusion: A potential causal relationship exists between COVID-19 infection and pulmonary embolism, but whether this phenomenon is common remains uncertain. The clinical manifestations of COVID-19 patients who developed pulmonary embolism are similar to those of patients with increased d-dimer alone, prompting a significant challenge on differential diagnoses.

3.
Front Med (Lausanne) ; 8: 601941, 2021.
Article in English | MEDLINE | ID: covidwho-1231345

ABSTRACT

Background: During the epidemic, surgeons cannot identify infectious acute abdomen patients with suspected coronavirus disease 2019 (COVID-19) immediately using the current widely applied methods, such as double nucleic acid detection. We aimed to develop and validate a prediction model, presented as a nomogram and scale, to identify infectious acute abdomen patients with suspected COVID-19 more effectively and efficiently. Methods: A total of 584 COVID-19 patients and 238 infectious acute abdomen patients were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were conducted to develop the prediction model. The performance of the nomogram was evaluated through calibration curves, Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves in the training and validation cohorts. A simplified screening scale and a management algorithm were generated based on the nomogram. Results: Five potential COVID-19 prediction variables, fever, chest CT, WBC, CRP, and PCT, were selected, all independent predictors of multivariable logistic regression analysis, and the nomogram, named the COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration, and it was validated in the validation cohort. Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified as the CIAAD scale. Conclusion: We established an easy and effective screening model and scale for surgeons in the emergency department to use to distinguish COVID-19 patients. The algorithm based on the CIAAD scale will help surgeons more efficiently manage infectious acute abdomen patients suspected of having COVID-19.

4.
Diabetes Care ; 44(4): 865-873, 2021 04.
Article in English | MEDLINE | ID: covidwho-1041481

ABSTRACT

OBJECTIVE: To investigate the association of in-hospital early-phase glycemic control with adverse outcomes among inpatients with coronavirus disease 2019 (COVID-19) in Wuhan, China. RESEARCH DESIGN AND METHODS: The study is a large case series, and data were obtained regarding consecutive patients hospitalized with COVID-19 in the Central Hospital of Wuhan between 2 January and 15 February 2020. All patients with definite outcomes (death or discharge) were included. Demographic, clinical, treatment, and laboratory information were extracted from electronic medical records. We collected daily fasting glucose data from standard morning fasting blood biochemistry to determine glycemic status and fluctuation (calculated as the square root of the variance of daily fasting glucose levels) during the 1st week of hospitalization. RESULTS: A total of 548 patients were included in the study (median age 57 years; 298 [54%] were women, and n = 99 had diabetes [18%]), 215 suffered acute respiratory distress syndrome (ARDS), 489 survived, and 59 died. Patients who had higher mean levels of glucose during their 1st week of hospitalization were older and more likely to have a comorbidity and abnormal laboratory markers, prolonged hospital stays, increased expenses, and greater risks of severe pneumonia, ARDS, and death. Compared with patients with the lowest quartile of glycemic fluctuation, those who had the highest quartile of fluctuation magnitude had an increased risk of ARDS (risk ratio 1.97 [95% CI 1.01, 4.04]) and mortality (hazard ratio 2.73 [95% CI 1.06, 7.73]). CONCLUSIONS: These results may have implications for optimizing glycemic control strategies in COVID-19 patients during the early phase of hospitalization.


Subject(s)
Blood Glucose/metabolism , COVID-19/blood , COVID-19/diagnosis , COVID-19/mortality , Hospitalization , Adult , Aged , COVID-19/pathology , China/epidemiology , Comorbidity , Diabetes Mellitus/blood , Diabetes Mellitus/epidemiology , Disease Progression , Female , Hospitalization/statistics & numerical data , Hospitals/statistics & numerical data , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , SARS-CoV-2/physiology
5.
Sci Rep ; 10(1): 17365, 2020 10 15.
Article in English | MEDLINE | ID: covidwho-872730

ABSTRACT

To analyze the clinical characteristics of re-positive discharged COVID-19 patients and find distinguishing markers. The demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, illness severities and chest CT scan results of 267 patients were collected from 1st January to 15th February 2020. COVID-19 was diagnosed by RT-PCR. Clinical symptoms and nucleic acid test results were collected during the 14 days post-hospitalization quarantine. 30 out of 267 COVID-19 patients were detected re-positive during the post-hospitalization quarantine. Re-positive patients could not be distinguished by demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, chest CT scan results or subsequent clinical symptoms. However, re-positive rate was found to be correlated to illness severity, according the Acute Physiology and Chronic Health Evaluation II (APACHE II) severity-of-disease classification system, and the confusion, urea, respiratory rate and blood pressure (CURB-65) score. Common clinical characteristics were not able to distinguish re-positive patients. However, severe and critical cases classified high according APACHE II and CURB-65 scores, were more likely to become re-positive after discharge.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/pathology , Pneumonia, Viral/pathology , Adult , Aged , Betacoronavirus/isolation & purification , COVID-19 , China , Comorbidity , Coronavirus Infections/virology , Female , Follow-Up Studies , Humans , Logistic Models , Male , Middle Aged , Pandemics , Patient Discharge , Pneumonia, Viral/virology , Quarantine , RNA, Viral/metabolism , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Severity of Illness Index , Thorax/diagnostic imaging , Tomography, X-Ray Computed
6.
Mediators Inflamm ; 2020: 3764515, 2020.
Article in English | MEDLINE | ID: covidwho-852759

ABSTRACT

This study aimed at determining the relationship between baseline cystatin C levels and coronavirus disease 2019 (COVID-19) and investigating the potential prognostic value of serum cystatin C in adult patients with COVID-19. 481 patients with COVID-19 were consecutively included in this study from January 2, 2020, and followed up to April 15, 2020. All clinical and laboratory data of COVID-19 patients with definite outcomes were reviewed. For every measure, COVID-19 patients were grouped into quartiles according to the baseline levels of serum cystatin C. The highest cystatin C level was significantly related to more severe inflammatory conditions, worse organ dysfunction, and worse outcomes among patients with COVID-19 (P values < 0.05). In the adjusted logistic regression analyses, the highest cystatin C level and ln-transformed cystatin C levels were independently associated with the risks of developing critically ill COVID-19 and all-cause death either in overall patients or in patients without chronic kidney disease (P values < 0.05). As a potential inflammatory marker, increasing baseline levels of serum cystatin C might independently predict adverse outcomes for COVID-19 patients. Serum cystatin C could be routinely monitored during hospitalization, which showed clinical importance in prognosticating for adult patients with COVID-19.


Subject(s)
Betacoronavirus , Coronavirus Infections/blood , Cystatin C/blood , Pandemics , Pneumonia, Viral/blood , Adult , Aged , Biomarkers/blood , COVID-19 , China/epidemiology , Cohort Studies , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Critical Illness , Female , Humans , Inflammation Mediators/blood , Kaplan-Meier Estimate , Logistic Models , Male , Middle Aged , Models, Biological , Nonlinear Dynamics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2
7.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-648

ABSTRACT

Background: Coronavirus disease 2019 is an emerging infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has an average

8.
Eur Respir J ; 56(2)2020 08.
Article in English | MEDLINE | ID: covidwho-744960

ABSTRACT

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.


Subject(s)
Coronavirus Infections/diagnosis , Hospital Mortality/trends , Machine Learning , Pneumonia, Viral/diagnosis , Triage/methods , Adult , Age Factors , Aged , Area Under Curve , Belgium , COVID-19 , COVID-19 Testing , China , Clinical Laboratory Techniques , Cohort Studies , Coronavirus Infections/epidemiology , Decision Support Systems, Clinical , Female , Hospitalization/statistics & numerical data , Humans , Internationality , Italy , Male , Middle Aged , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Predictive Value of Tests , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment , Severity of Illness Index , Sex Factors , Survival Analysis
9.
Stroke ; 51(9): 2674-2682, 2020 09.
Article in English | MEDLINE | ID: covidwho-697017

ABSTRACT

BACKGROUND AND PURPOSE: No studies have reported the effect of the coronavirus disease 2019 (COVID-19) epidemic on patients with preexisting stroke. We aim to study the clinical course of COVID-19 patients with preexisting stroke and to investigate death-related risk factors. METHODS: We consecutively included 651 adult inpatients with COVID-19 from the Central Hospital of Wuhan between January 2 and February 15, 2020. Data on the demography, comorbidities, clinical manifestations, laboratory findings, treatments, complications, and outcomes (ie, discharged or death) of the participants were extracted from electronic medical records and compared between patients with and without preexisting stroke. The association between risk factors and mortality was estimated using a Cox proportional hazards regression model for stroke patients infected with severe acute respiratory syndrome coronavirus 2. RESULTS: Of the 651 patients with COVID-19, 49 with preexisting stroke tended to be elderly, male, had more underlying comorbidities and greater severity of illness, prolonged length of hospital stay, and greater hospitalization expenses than those without preexisting stroke. Cox regression analysis indicated that the patients with stroke had a higher risk of developing critical pneumonia (adjusted hazard ratio, 2.01 [95% CI, 1.27-3.16]) and subsequent mortality (adjusted hazard ratio, 1.73 [95% CI, 1.00-2.98]) than the patients without stroke. Among the 49 stroke patients, older age and higher score of Glasgow Coma Scale or Sequential Organ Failure Assessment were independent risk factors associated with in-hospital mortality. CONCLUSIONS: Preexisting stroke patients infected with severe acute respiratory syndrome coronavirus 2 were readily predisposed to death, providing an important message to individuals and health care workers that preventive measures must be implemented to protect and reduce transmission in stroke patients in this COVID-19 crisis.


Subject(s)
Coronavirus Infections/complications , Coronavirus Infections/mortality , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Stroke/complications , Stroke/mortality , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19 , China/epidemiology , Comorbidity , Coronavirus Infections/therapy , Disease Progression , Electronic Health Records , Female , Glasgow Coma Scale , Hospital Mortality , Humans , Male , Middle Aged , Multiple Organ Failure/epidemiology , Multiple Organ Failure/etiology , Pandemics , Pneumonia/etiology , Pneumonia, Viral/therapy , Retrospective Studies , Risk Factors , Sex Factors , Stroke/therapy , Treatment Outcome
11.
Platelets ; 31(4): 490-496, 2020 May 18.
Article in English | MEDLINE | ID: covidwho-66223

ABSTRACT

BACKGROUND: Thrombocytopenia has been implicated in patients infected with severe acute respiratory syndrome coronavirus 2, while the association of platelet count and changes with subsequent mortality remains unclear. METHODS: The clinical and laboratory data of 383 patients with the definite outcome by March 1, 2020 in the Central Hospital of Wuhan were reviewed. The association between platelet parameters and mortality risk was estimated by utilizing Cox proportional hazard regression models. RESULTS: Among the 383 patients, 334 (87.2%) were discharged and survived, and 49 (12.8%) died. Thrombocytopenia at admission was associated with mortality of almost three times as high as that for those without thrombocytopenia (P < 0.05). Cox regression analyses revealed that platelet count was an independent risk factor associated with in-hospital mortality in a dose-dependent manner. An increment of per 50 × 109/L in platelets was associated with a 40% decrease in mortality (hazard ratio: 0.60, 95%CI: 0.43, 0.84). Dynamic changes of platelets were also closely related to death during hospitalization. CONCLUSIONS: Baseline platelet levels and changes were associated with subsequent mortality. Monitoring platelets during hospitalization may be important in the prognosis of patients with coronavirus disease in 2019.


Subject(s)
Betacoronavirus , Coronavirus Infections , Pandemics , Pneumonia, Viral , Thrombocytopenia , Adult , Aged , COVID-19 , Cohort Studies , Coronavirus Infections/blood , Coronavirus Infections/complications , Coronavirus Infections/mortality , Female , Hospital Mortality , Humans , Male , Middle Aged , Platelet Count , Pneumonia, Viral/blood , Pneumonia, Viral/complications , Pneumonia, Viral/mortality , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2 , Thrombocytopenia/etiology , Thrombocytopenia/mortality
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