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1.
Front Med (Lausanne) ; 9: 1018516, 2022.
Article in English | MEDLINE | ID: covidwho-2198992

ABSTRACT

Background: The Omicron variant is characterized by striking infectivity and antibody evasion. The analysis of Omicron variant BA.2 infection risk factors is limited among geriatric individuals and understanding these risk factors would promote improvement in the public health system and reduction in mortality. Therefore, our research investigated BA.2 infection risk factors for discriminating severe/critical from mild/moderate geriatric patients. Methods: Baseline characteristics of enrolled geriatric patients (aged over 60 years) with Omicron infections were analyzed. A logistic regression analysis was conducted to evaluate factors correlated with severe/critical patients. A receiver operating characteristic (ROC) curve was constructed for predicting variables to discriminate mild/moderate patients from severe/critical patients. Results: A total of 595 geriatric patients older than 60 years were enrolled in this study. Lymphocyte subset levels were significantly decreased, and white blood cells (WBCs) and D-dimer levels were significantly increased with disease progression from a mild/moderate state to a severe/critical state. Univariate and multivariate logistic regression analyses identified a panel of WBCs, CD4+ T cell, and D-dimer values that were correlated with good diagnostic accuracy for discriminating mild/moderate patients from severe/critical patients with an area under the curve of 0.962. Conclusion: Some key baseline laboratory indicators change with disease development. A panel was identified for discriminating mild/moderate patients from severe/critical patients, suggesting that the panel could serve as a potential biomarker to enable physicians to provide timely medical services in clinical practice.

2.
Emerg Microbes Infect ; 11(1): 2045-2054, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1967814

ABSTRACT

Shanghai has been experiencing the Omicron wave since March 2022. Though several studies have evaluated the risk factors of severe infections, the analyses of BA.2 infection risk and protective factors among geriatric people were much limited. This multicentre cohort study described clinical characteristics, and assessed risk and protective factors for geriatric Omicron severe infections. A total of 1377 patients older than 60 were enrolled, with 75.96% having comorbidities. The median viral shedding time and hospitalization time were nine and eight days, respectively. Severe and critical were associated with longer virus clearance time (aOR [95%CI]:0.706 (0.533-0.935), P = .015), while fully vaccinated/booster and paxlovid use shortened viral shedding time (1.229 [1.076-1.402], P = .002; 1.140 [0.019-1.274], P = .022, respectively). Older age (>80), cerebrovascular disease, and chronic kidney disease were risk factors of severe/critical. Fully vaccination was a significant protective factor against severe infections (0.237 [0.071-0.793], P = .019). We found patients with more than two comorbidities were more likely to get serious outcomes. These findings demonstrated that in the elderly older than 60 years old, older age (aged over 80), cerebrovascular disease, and chronic kidney disease were risk factors for severe infection. Patients with more than two comorbidities were more likely to get serious outcomes. Fully vaccinated/booster patients were less likely to be severe and vaccinations could shorten viral shedding time. The limitation of lacking an overall spectrum of COVID-19 infections among elders could be compensated in other larger-scale studies in the future.


Subject(s)
COVID-19 , Renal Insufficiency, Chronic , Aged , Aged, 80 and over , COVID-19/epidemiology , China/epidemiology , Cohort Studies , Humans , Middle Aged , Protective Factors
3.
Medicine (Baltimore) ; 100(24): e26279, 2021 Jun 18.
Article in English | MEDLINE | ID: covidwho-1269620

ABSTRACT

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/diagnosis , Mass Screening/methods , SARS-CoV-2/isolation & purification , Adult , China , Female , Humans , Male , Middle Aged , Prospective Studies , ROC Curve , Sensitivity and Specificity
4.
Sci Rep ; 11(1): 3863, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087494

ABSTRACT

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Mass Screening , Models, Biological , Pneumonia/diagnosis , SARS-CoV-2/physiology , Adult , China/epidemiology , Female , Humans , Male , Middle Aged , ROC Curve
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