Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study.
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ABSTRACT:
SUBMITTER: Yan C
PROVIDER: S-EPMC8101702 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-06-14 | GSE220078 | GEO
Project description:(1) Background: Extracorporeal membrane oxygenation (ECMO) therapy in intensive care units (ICUs) remains the last treatment option for Coronavirus disease 2019 (COVID-19) patients with severely affected lungs but is highly resource demanding. Early risk stratification for the need of ECMO therapy upon admission to the hospital using artificial intelligence (AI)-based computed tomography (CT) assessment and clinical scores is beneficial for patient assessment and resource management; (2) Methods: Retrospective single-center study with 95 confirmed COVID-19 patients admitted to the participating ICUs. Patients requiring ECMO therapy (n = 14) during ICU stay versus patients without ECMO treatment (n = 81) were evaluated for discriminative clinical prediction parameters and AI-based CT imaging features and their diagnostic potential to predict ECMO therapy. Reported patient data include clinical scores, AI-based CT findings and patient outcomes; (3) Results: Patients subsequently allocated to ECMO therapy had significantly higher sequential organ failure (SOFA) scores (p < 0.001) and significantly lower oxygenation indices on admission (p = 0.009) than patients with standard ICU therapy. The median time from hospital admission to ECMO placement was 1.4 days (IQR 0.2-4.0). The percentage of lung involvement on AI-based CT assessment on admission to the hospital was significantly higher in ECMO patients (p < 0.001). In binary logistic regression analyses for ECMO prediction including age, sex, body mass index (BMI), SOFA score on admission, lactate on admission and percentage of lung involvement on admission CTs, only SOFA score (OR 1.32, 95% CI 1.08-1.62) and lung involvement (OR 1.06, 95% CI 1.01-1.11) were significantly associated with subsequent ECMO allocation. Receiver operating characteristic (ROC) curves showed an area under the curve (AUC) of 0.83 (95% CI 0.73-0.94) for lung involvement on admission CT and 0.82 (95% CI 0.72-0.91) for SOFA scores on ICU admission. A combined parameter of SOFA on ICU admission and lung involvement on admission CT yielded an AUC of 0.91 (0.84-0.97) with a sensitivity of 0.93 and a specificity of 0.84 for ECMO prediction; (4) Conclusions: AI-based assessment of lung involvement on CT scans on admission to the hospital and SOFA scoring, especially if combined, can be used as risk stratification tools for subsequent requirement for ECMO therapy in patients with severe COVID-19 disease to improve resource management in ICU settings.
| S-EPMC8228774 | biostudies-literature
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220077 | GEO
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220076 | GEO
Project description:Triage is crucial for patient's management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient's admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73-0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56-0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.
| S-EPMC8329253 | biostudies-literature
Project description:BackgroundSeveral studies have recently addressed factors associated with severe Coronavirus disease 2019 (COVID-19); however, some medications and comorbidities have yet to be evaluated in a large matched cohort. We therefore explored the role of relevant comorbidities and medications in relation to the risk of intensive care unit (ICU) admission and mortality.MethodsAll ICU COVID-19 patients in Sweden until 27 May 2020 were matched to population controls on age and gender to assess the risk of ICU admission. Cases were identified, comorbidities and medications were retrieved from high-quality registries. Three conditional logistic regression models were used for risk of ICU admission and three Cox proportional hazards models for risk of ICU mortality, one with comorbidities, one with medications and finally with both models combined, respectively.ResultsWe included 1981 patients and 7924 controls. Hypertension, type 2 diabetes mellitus, chronic renal failure, asthma, obesity, being a solid organ transplant recipient and immunosuppressant medications were independent risk factors of ICU admission and oral anticoagulants were protective. Stroke, asthma, chronic obstructive pulmonary disease and treatment with renin-angiotensin-aldosterone inhibitors (RAASi) were independent risk factors of ICU mortality in the pre-specified primary analyses; treatment with statins was protective. However, after adjusting for the use of continuous renal replacement therapy, RAASi were no longer an independent risk factor.ConclusionIn our cohort oral anticoagulants were protective of ICU admission and statins was protective of ICU death. Several comorbidities and ongoing RAASi treatment were independent risk factors of ICU admission and ICU mortality.
| S-EPMC8013403 | biostudies-literature
Project description:BackgroundCorona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia.MethodsA total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia.ResultsThe volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU.ConclusionsQuantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.
| S-EPMC8374412 | biostudies-literature
Project description:OBJECTIVE:The main objective of this study was to investigate the prognostic value of early systematic chest computed tomography (CT) with quantification of lung lesions in coronavirus disease 2019 (COVID-19) patients. METHODS:We studied 572 patients diagnosed with COVID-19 (confirmed using polymerase chain reaction) for whom a chest CT was performed at hospital admission. Visual quantification was used to classify patients as per the percentage of lung parenchyma affected by COVID-19 lesions: normal CT, 0-10%, 11-25%, 26-50%, 51-75% and >75%. The primary endpoint was severe disease, defined by death or admission to the intensive care unit in the 7 days following first admission. RESULTS:The mean patient age was 66.0 ± 16.0 years, and 343/572 (60.0%) were men. The primary endpoint occurred in 206/572 patients (36.0%). The extent of lesions on initial CT was independently associated with prognosis (odds ratio = 2.35, 95% confidence interval 1.24-4.46; p < 0.01). Most patients with lung involvement >50% (66/95, 69.5%) developed severe disease compared to patients with lung involvement of 26-50% (70/171, 40.9%) and ?25% (70/306, 22.9%) (p < 0.01 and p < 0.01, respectively). None of the patients with normal CT (0/14) had severe disease. CONCLUSION:Chest CT findings at admission are associated with outcome in COVID-19 patients.
| S-EPMC7378475 | biostudies-literature
Project description:BackgroundPneumonia poses a significant burden to the U.S. health-care system. However, there are few data focusing on severe pneumonia, particularly cases of pneumonia associated with specialized care in intensive care units (ICU).MethodsWe used administrative and electronic medical record data from six integrated health care systems to estimate rates of pneumonia hospitalizations with ICU admissions among adults during 2006 through 2010. Pneumonia hospitalization was defined as either a primary discharge diagnosis of pneumonia or a primary discharge diagnosis of sepsis or respiratory failure with a secondary diagnosis of pneumonia in administrative data. ICU admissions were collected from internal electronic medical records from each system. Comorbidities were identified by ICD-9-CM codes coded during the current pneumonia hospitalization, as well as during medical visits that occurred during the year prior to the date of admission.ResultsWe identified 119,537 adult hospitalizations meeting our definition for pneumonia. Approximately 19% of adult pneumonia hospitalizations had an ICU admission. The rate of pneumonia hospitalizations requiring ICU admission during the study period was 76 per 100,000 population/year; rates increased for each age-group with the highest rates among adults aged ?85 years. Having a co-morbidity approximately doubled the risk of ICU admission in all age-groups.ConclusionsOur study indicates a significant burden of pneumonia hospitalizations with an ICU admission among adults in our cohort during 2006 through 2010, especially older age-groups and persons with underlying medical conditions. These findings reinforce current strategies aimed to prevent pneumonia among adults.
| S-EPMC5732529 | biostudies-literature
Project description: Not available
| S-EPMC8764887 | biostudies-literature