Dataset Information

COVID-19 patients in a tertiary US hospital: Assessment of clinical course and predictors of the disease severity.

ABSTRACT:

Introduction

Patients with severe COVID-19 can develop ventilator-dependent acute hypoxic respiratory failure (VDAHRF), which is associated with a higher mortality rate. We evaluated the clinical course of hospitalized COVID-19 patients and compared them with the patients who received invasive mechanical ventilation. Characteristics of intubated patients who were successfully weaned from the ventilator were compared with the patients who failed to be extubated or died in the hospital.

Objective

To investigate the clinical course of hospitalized COVID-19 patients, and assess the possible predictors of the disease severity leading to VDAHRF.

Methods

This is a single-center, retrospective study. The first 129 patients (18 years or older) with COVID-19 admitted to Monmouth Medical Center from March 1st to April 25th, 2020 were included.

Results

Out of 129 patients, 23.25% (n = 30) required invasive mechanical ventilation, and of those, six patients were successfully weaned from the ventilator. Multivariable logistic regression analysis showed increased odds of intubation associated with hypoxemia (odds ratio 17.23, 95% CI 5.206-57.088; p < 0.0001), elevated d-dimer by one unit mg/L of FEU (odds ratio 1.515, 95% CI 5.206-57.088; p = 0.0430) and elevated ferritin by one unit ng/ml (odds ratio 1.001, 95% CI 1.000-1.001, p = 0.0051) on admission, adjusted for other covariates.

Conclusions

Patients who required invasive mechanical ventilation were more likely to have older age, male gender, coronary artery disease, diabetes, and obesity. The patients who were successfully weaned from the ventilator were more likely to be younger in age, and none of them had heart failure or CAD.

SUBMITTER: Mughal MS

PROVIDER: S-EPMC7455149 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

COVID-19 patients in a tertiary US hospital: Assessment of clinical course and predictors of the disease severity.

Mughal Mohsin Sheraz MS Kaur Ikwinder Preet IP Jaffery Ali R AR Dalmacion Denise L DL Wang Chang C Koyoda Sai S Kramer Violet E VE Patton Chandler D CD Weiner Sharon S Eng Margaret H MH Granet Kenneth M KM

Respiratory medicine 20200828

<h4>Introduction</h4>Patients with severe COVID-19 can develop ventilator-dependent acute hypoxic respiratory failure (VDAHRF), which is associated with a higher mortality rate. We evaluated the clinical course of hospitalized COVID-19 patients and compared them with the patients who received invasive mechanical ventilation. Characteristics of intubated patients who were successfully weaned from the ventilator were compared with the patients who failed to be extubated or died in the hospital.<h4 ...[more]

PMID: 32896798

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Project description:BackgroundThe host immune response has a prominent role in the progression and outcome of SARS-CoV-2 infection. Lymphopenia has been described as an important feature of SARS-CoV-2 infection and has been associated with severe disease manifestation. Lymphocyte dysregulation and hyper-inflammation have been shown to be associated with a more severe clinical course; however, a T cell subpopulation whose dysfunction correlate with disease progression has yet to be identify.MethodsWe performed an immuno-phenotypic analysis of T cell sub-populations in peripheral blood from patients affected by different severity of COVID-19 (n=60) and undergoing a different clinical evolution. Clinical severity was established based on a modified WHO score considering both ventilation support and respiratory capacity (PaO2/FiO2 ratio). The ability of circulating cells at baseline to predict the probability of clinical aggravation was explored through multivariate regression analyses.ResultsThe immuno-phenotypic analysis performed by multi-colour flow cytometry confirmed that patients suffering from severe COVID-19 harboured significantly reduced circulating T cell subsets, especially for CD4+ T, Th1, and regulatory T cells. Peripheral T cells also correlated with parameters associated with disease severity, i.e., PaO2/FiO2 ratio and inflammation markers. CD4+ T cell subsets showed an important significant association with clinical evolution, with patients presenting markedly decreased regulatory T cells at baseline having a significantly higher risk of aggravation. Importantly, the combination of gender and regulatory T cells allowed distinguishing between improved and worsened patients with an area under the ROC curve (AUC) of 82%.ConclusionsThe present study demonstrates the association between CD4+ T cell dysregulation and COVID-19 severity and progression. Our results support the importance of analysing baseline regulatory T cell levels, since they were revealed able to predict the clinical worsening during hospitalization. Regulatory T cells assessment soon after hospital admission could thus allow a better clinical stratification and patient management.

Project description:BackgroundClinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes.MethodsIn- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st ('first wave', n = 198) and September 1st through November 16th 2020 ('second wave', n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC).ResultsSex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (- 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85-0.99, PPV = 0.90, NPV = 0.58).ConclusionWith a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

Dataset Information

COVID-19 patients in a tertiary US hospital: Assessment of clinical course and predictors of the disease severity.

Introduction

Objective

Methods

Results

Conclusions

Publications

COVID-19 patients in a tertiary US hospital: Assessment of clinical course and predictors of the disease severity.

Similar Datasets

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