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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315710

ABSTRACT

Background: Understanding the long-term effects of coronavirus disease 2019 (COVID-19) on cognitive function is essential for the prevention of cognitive decline in elderly population. This study aims to assess cognitive status and longitudinal decline at 6 months post-infection in elderly patients recovered from COVID-19.Methods: This cross-sectional study recruited 1013 COVID-19 inpatients aged over 60 years who were discharged from three COVID-19-designated hospitals in Wuhan, China, from February 10 to March 13, 2020. In total, 262 uninfected living spouses of COVID-19 patients were selected as controls. Subjects were examined for their current cognitive status using a Chinese version of the Telephone Interview of Cognitive Status-40 (TICS-40) and longitudinal cognitive decline using an Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Cognitive assessments were performed 6 months after patient discharge.Findings: COVID-19 patients had significantly lower TICS-40 scores (patients: 29.73±6.13;controls: 30.74±5.95, p=0.016) and higher IQCODE scores (patients: 3.40±0.81;controls: 3.15±0.39, p<0.001) than the controls. Severe COVID-19 patients had lower TICS-40 scores and higher IQCODE scores than non-severe COVID-19 patients (TICS-40: 22.98±7.12 vs. 30.46±5.53, p<0.001;IQCODE: 4.06±1.39 vs. 3.33±0.68, p<0.001) and controls (TICS-40: 22.98±7.12 vs. 30.74±5.95, p<0.001;IQCODE: 4.06±1.39 vs. 3.15±0.39, p<0.001). Severe COVID-19 patients had a higher proportion of cases with a current cognitive impairment and longitudinal cognitive decline than non-severe COVID-19 patients and controls. COVID-19 severity (OR: 8.142, 95% CI: 5.007-13.239) was associated with worse current cognitive function. Older age (OR: 1.024, 95% CI: 1.003 to 1.046), COVID-19 severity (OR: 2.277, 95% CI: 1.308 to 3.964), mechanical ventilation (OR: 5.388, 95% CI: 3.007 to 9.656), and hypertension (OR: 1.866, 95% CI: 1.376 to 2.531) were associated with an increased risk of longitudinal cognitive decline.Interpretation: SARS-CoV-2 infection is associated with delayed cognitive decline in elderly population. COVID-19 patients with risk factors, including severe disease, older age, mechanical ventilation, and hypertension, should be intensively monitored for delayed cognitive decline. Funding: National Natural Science Foundation of China.Conflict of Interest: We declared no conflict of interests.Ethical Approval: The study protocols were approved by the institutional review boards of the hospitals. Verbal informed consent was obtained from all participants prior to the survey.

2.
Ann Med ; 53(1): 257-266, 2021 12.
Article in English | MEDLINE | ID: covidwho-1574445

ABSTRACT

OBJECTIVES: To appraise effective predictors for COVID-19 mortality in a retrospective cohort study. METHODS: A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores. RESULTS: Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets. CONCLUSION: We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.


Subject(s)
COVID-19/mortality , Clinical Decision Rules , Hospitalization , Machine Learning , Adult , Aged , Aged, 80 and over , C-Reactive Protein/metabolism , COVID-19/epidemiology , COVID-19/metabolism , COVID-19/physiopathology , Cardiovascular Diseases/epidemiology , China/epidemiology , Cohort Studies , Comorbidity , Diabetes Mellitus/epidemiology , Female , Ferritins/metabolism , Humans , Hypertension/epidemiology , Interleukin-10/metabolism , L-Lactate Dehydrogenase/metabolism , Male , Middle Aged , Prognosis , Reproducibility of Results , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
3.
Engineering (Beijing) ; 7(7): 958-965, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1482579

ABSTRACT

The longitudinal immunologic status of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients and its association with the clinical outcome are barely known. Thus, we sought to analyze the temporal profiles of specific antibodies, as well as the associations between the antibodies, proinflammatory cytokines, and survival of patients with coronavirus disease 2019 (COVID-19). A total of 1830 laboratory-confirmed COVID-19 cases were recruited. The temporal profiles of the virus, antibodies, and cytokines of the patients until 12 weeks since illness onset were fitted by the locally weighted scatter plot smoothing method. The mediation effect of cytokines on the associations between antibody responses and survival were explored by mediation analysis. Of the 1830 patients, 1435 were detectable for SARS-CoV-2, while 395 were positive in specific antibodies only. Of the 1435 patients, 2.4% presented seroconversion for neither immunoglobulin G (IgG) nor immunoglobulin M (IgM) during hospitalization. The seropositive rates of IgG and IgM were 29.6% and 48.1%, respectively, in the first week, and plateaued within five weeks. For the patients discharged from the hospital, the IgM decreased slowly, while high levels of IgG were maintained at around 188 AU·mL-1 for the 12 weeks since illness onset. In contrast, in the patients who subsequently died, IgM declined rapidly and IgG dropped to 87 AU·mL-1 at the twelfth week. Elevated interleukin-6, interleukin-8, interleukin-10, interleukin-1ß, interleukin-2R, and tumor necrosis factor-α levels were observed in the deceased patients in comparison with the discharged patients, and 12.5% of the association between IgG level and mortality risk was mediated by these cytokines. Our study deciphers the temporal profiles of SARS-CoV-2-specific antibodies within the 12 weeks since illness onset and indicates the protective effect of antibody response on survival, which may help to guide prognosis estimation.

4.
International Journal of Infectious Diseases ; 95:436-440, 2020.
Article in English | CAB Abstracts | ID: covidwho-1409652

ABSTRACT

Background: The differential diagnosis between novel coronavirus pneumonia patients (NCPP) and influenza patients (IP) remains a challenge in clinical practice.

5.
Chemical Engineering Journal ; : 130869, 2021.
Article in English | ScienceDirect | ID: covidwho-1271587

ABSTRACT

Wearable strain sensors have generated considerable recent research interest due to their huge potential in the real-time detection of human body deformation. State-of-the-art strain sensors are normally fabricated through conductive networks with a single sensing element, which always faces the challenge of either limited stretchability or inferior quality in sensitivity. In this work, we report a highly sensitive strain sensor based on a multi-functionalized fabric through carbonization and polymer-assisted copper deposition. The sensor shows high sensitivity (Gauge factor∼3557.6 in the strain range from 0 to 48%), and outstanding stretchability up to the strain of 300%, which is capable of detecting different types of deformation of the human body. By integrating the high-performance sensor with a deep learning network, we demonstrate a high accuracy of respiration monitoring and emergency alarm system, showing the enormous application potential of the sensor in personal and public healthcare.

6.
Journal of Modern Laboratory Medicine ; 35(5):93-98, 2020.
Article in Chinese | GIM | ID: covidwho-1073554

ABSTRACT

The aim of the article was to analyze the characteristics of early peripheral blood laboratory examination results of patients with new coronavirus pneumonia (coronavirus disease 2019, COVID-19), and provide references for early clinical identification. From January 11, 2020 to February 18, 2020, all 626 patients who attended the fever clinic of Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology and tested positive for the new coronavirus (SARS-CoV-2) nucleic acid were selected as the research group In addition, 254 suspected patients who visited the fever clinic during the same period and the SARS-CoV-2 nucleic acid test was negative for two or more consecutive times were selected as the control group, and analyzed the blood cell test, biochemical routine, and inflammation markers of the two groups of patients at the fever clinic for the first time. The characteristics of 31 hematological indicators. Compared with the control group, the white blood cell (WBC), lymphocyte (LYMPH), platelet (PLT), serum calcium (serum calcium, Ca) of the study group were significantly reduced, and the hypersensitive C-reactive protein (hypersensitive C-reactive protein, hsCRP) significantly increased, the difference was statistically significant, and there was a difference in the distribution of results. In the study group, WBC was mostly normal or decreased. WBC was normal in 85.3%, decreased in 9.4%, LYMPH decreased in 43.1%, PLT decreased in 12.8%, Ca decreased in 61.8%, hsCRP was higher than 10mg/L accounted for 66.2%. The remaining 26 hematological indicators (Cl, Na, K, HCO3, Urea, UA, Cr, TBA, CHE, ALB, ALT, ALP, LDH, TP, PCT, DBIL, GLB, IBIL, TBIL, P-GGT, TCHOL, AST, Hb, RBC, NEUT, MON) There was no statistically significant difference between the two groups. WBC, LYMPH, PLT, Ca and hsCRP have significant changes in the early stage of COVID-19 patients. Joint detection and observation of the above indicators can provide important references for early clinical identification.

7.
Front Med (Lausanne) ; 7: 374, 2020.
Article in English | MEDLINE | ID: covidwho-646639

ABSTRACT

Background: The predictive value of prealbumin for the prognosis of coronavirus disease 2019 (COVID-19) has not been extensively investigated. Methods: A total of 1,115 patients with laboratory-confirmed COVID-19 were enrolled at Tongji hospital from February to April 2020 and classified into fatal (n = 129) and recovered (n = 986) groups according to the patient's outcome. Prealbumin and other routine laboratory indicators were measured simultaneously. Results: The level of prealbumin on admission was significantly lower in fatal patients than in recovered patients. For predicting the prognosis of COVID-19, the performance of prealbumin was better than most routine laboratory indicators, such as albumin, lymphocyte count, neutrophil count, hypersensitive C-reactive protein, d-dimer, lactate dehydrogenase, creatinine, and hypersensitive cardiac troponin I. When a threshold of 126 mg/L was used to discriminate between fatal and recovered patients, the sensitivity and specificity of prealbumin were, respectively, 78.29 and 90.06%. Furthermore, a model based on the combination of nine indexes showed an improved performance in predicting the death of patients with COVID-19. Using a cut-off value of 0.19, the prediction model was able to distinguish between fatal and recovered individuals with a sensitivity of 86.82% and a specificity of 90.37%. Conclusions: A lower level of prealbumin on admission may indicate a worse outcome of COVID-19. Immune and nutritional status may be vital factors for predicting disease progression in the early stage of COVID-19.

8.
J Clin Immunol ; 40(7): 960-969, 2020 10.
Article in English | MEDLINE | ID: covidwho-641161

ABSTRACT

BACKGROUND: There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. METHODS: A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal (n = 51) and survived (n = 688) groups according to the patient's outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously. RESULTS: The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4+ T cells, CD8+ T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4+ T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death. CONCLUSIONS: Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.


Subject(s)
Betacoronavirus/immunology , Coronavirus Infections/mortality , Cytokines/blood , Lymphocyte Subsets/immunology , Models, Biological , Pneumonia, Viral/mortality , Aged , Aged, 80 and over , Betacoronavirus/genetics , Betacoronavirus/isolation & purification , Biomarkers/blood , COVID-19 , COVID-19 Testing , China/epidemiology , Clinical Laboratory Techniques/methods , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Cytokines/immunology , Female , Humans , Length of Stay , Lymphocyte Count , Male , Middle Aged , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/epidemiology , Pneumonia, Viral/immunology , Prognosis , RNA, Viral/isolation & purification , Reverse Transcriptase Polymerase Chain Reaction , Risk Assessment/methods , SARS-CoV-2
9.
Int J Infect Dis ; 95: 436-440, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-155290

ABSTRACT

BACKGROUND: The differential diagnosis between novel coronavirus pneumonia patients (NCPP) and influenza patients (IP) remains a challenge in clinical practice. METHODS: Between January 2018 and March 2020, 1,027 NCPP and 1,140 IP were recruited from Tongji hospital. Routine blood examination, biochemical indicators and coagulation function analysis were simultaneously performed in all participants. RESULTS: There was no sex predominance in NCPP. The NCPP were frequently encountered in the sixth and seventh decades of life. The mean age of NCPP (56±16 years) was higher than IP (47±17 years), but without statistical difference. Although most results of routine laboratory tests between NCPP and IP had no significant differences, some laboratory tests showed an obvious change in NCPP. It was observed that NCPP had significantly decreased white blood cells, alkaline phosphatase and d-dimer compared with IP. However, the results of lactate dehydrogenase, erythrocyte sedimentation rate and fibrinogen were significantly increased in NCPP compared with IP. The diagnostic model based on a combination of 18 routine laboratory indicators showed an area under the curve of 0.796 (95% CI, 0.777-0.814), with a sensitivity of 46.93% and specificity of 90.09% when using a cut-off value of 0.598. CONCLUSIONS: Some routine laboratory results had statistical difference between NCPP and IP. A diagnostic model based on a combination of routine laboratory results provided an adjunct approach in the differential diagnosis between NCPP and IP.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnosis , Influenza, Human/diagnosis , Pneumonia, Viral/diagnosis , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Diagnosis, Differential , Female , Humans , Leukocyte Count , Male , Middle Aged , Pandemics , SARS-CoV-2
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