Dataset Information

Antivirals With Adjunctive Corticosteroids Prevent Clinical Progression of Early Coronavirus 2019 Pneumonia: A Retrospective Cohort Study.

ABSTRACT: This is a retrospective cohort study of hospitalized adults with coronavirus disease 2019 (COVID-19). Fifty-seven patients received treatment alone, and 35 patients received treatment with adjunctive prednisolone. A combination of corticosteroids and antivirals was associated with lower risk of clinical progression and invasive mechanical ventilation or death in early COVID-19 pneumonia.

SUBMITTER: Ooi ST

PROVIDER: S-EPMC7665708 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Antivirals With Adjunctive Corticosteroids Prevent Clinical Progression of Early Coronavirus 2019 Pneumonia: A Retrospective Cohort Study.

Ooi Say Tat ST Parthasarathy Purnima P Lin Yi Y Nallakaruppan Valliammai D/O VD Ng Shereen S Tan Teck Choon TC Low Serena S Tang Terence T

Open forum infectious diseases 20201014 11

This is a retrospective cohort study of hospitalized adults with coronavirus disease 2019 (COVID-19). Fifty-seven patients received treatment alone, and 35 patients received treatment with adjunctive prednisolone. A combination of corticosteroids and antivirals was associated with lower risk of clinical progression and invasive mechanical ventilation or death in early COVID-19 pneumonia. ...[more]

PMID: 33235888

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Project description:ObjectivesTo develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).MethodsPatients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.ResultsA total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%.ConclusionsOur radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.Advances in knowledgeRadiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.

Project description:BackgroundThe current worldwide pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, and the mortality rate of critical ill patients remains high. The purpose of this study was to identify factors that early predict the progression of COVID-19 from severe to critical illness.MethodsThis retrospective cohort study included adult patients with severe or critical ill COVID-19 who were consecutively admitted to the Zhongfaxincheng campus of Tongji Hospital (Wuhan, China) from February 8 to 18, 2020. Baseline variables, data at hospital admission and during hospital stay, as well as clinical outcomes were collected from electronic medical records system. The primary endpoint was the development of critical illness. A multivariable logistic regression model was used to identify independent factors that were associated with the progression from severe to critical illness.ResultsA total of 138 patients were included in the analysis; of them 119 were diagnosed as severe cases and 16 as critical ill cases at hospital admission. During hospital stay, 19 more severe cases progressed to critical illness. For all enrolled patients, longer duration from diagnosis to admission (odds ratio [OR] 1.108, 95% CI 1.022-1.202; P = 0.013), pulse oxygen saturation at admission <93% (OR 5.775, 95% CI 1.257-26.535; P = 0.024), higher neutrophil count (OR 1.495, 95% CI 1.177-1.899; P = 0.001) and higher creatine kinase-MB level at admission (OR 2.449, 95% CI 1.089-5.511; P = 0.030) were associated with a higher risk, whereas higher lymphocyte count at admission (OR 0.149, 95% CI 0.026-0.852; P = 0.032) was associated with a lower risk of critical illness development. For the subgroup of severe cases at hospital admission, the above factors except creatine kinase-MB level were also found to have similar correlation with critical illness development.ConclusionsHigher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.

Project description:BackgroundThis study aimed to determine the frequency of functional decline and to identify the factors related to a greater risk of functional decline among hospitalized older patients with coronavirus disease 2019 (COVID-19).MethodsWe reviewed the medical records of patients aged over 65 years who were admitted to a tertiary care hospital for COVID-19 over 1 year from February 2020. We evaluated the proportion of functional decline, which was defined as a decrease in the Barthel Index score from before the onset of COVID-19 to discharge. Multivariable logistic regression analyses were performed to evaluate the associations between the demographic and clinical characteristics of patients at admission and a greater risk of functional decline. Two sensitivity analyses with different inclusion criteria were performed: one in patients without very severe functional decline before the onset of COVID-19 (i.e., limited to those with Barthel Index score ≥ 25), and the other with a composite outcome of functional decline and death at discharge.ResultsThe study included 132 patients with COVID-19; of these, 72 (54.5%) developed functional decline. The severity of COVID-19 did not differ between patients with functional decline and those without (P = 0.698). Factors associated with a greater risk of functional decline included female sex (adjusted odds ratio [aOR], 3.14; 95% confidence interval [CI], 1.25 to 7.94), Barthel Index score < 100 before the onset of COVID-19 (aOR, 13.73; 95% CI, 3.29 to 57.25), and elevation of plasma D-dimer level on admission (aOR, 3.19; 95% CI, 1.12 to 9.07). The sensitivity analyses yielded similar results to those of the main analysis.ConclusionsOver half of the older patients who recovered from COVID-19 developed functional decline at discharge from a tertiary care hospital in Japan. Baseline activities of daily living impairment, female sex, and elevated plasma D-dimer levels at admission were associated with a greater risk of functional decline.

Dataset Information

Antivirals With Adjunctive Corticosteroids Prevent Clinical Progression of Early Coronavirus 2019 Pneumonia: A Retrospective Cohort Study.

Publications

Antivirals With Adjunctive Corticosteroids Prevent Clinical Progression of Early Coronavirus 2019 Pneumonia: A Retrospective Cohort Study.

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