ArticleJournal of clinical medicine2020
Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 6 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 6 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Associations between Serum Interleukins (IL-1BioMed research international · 2022Pooled it
- A systematic meta-analysis of immune signatures in patients with COVID-19.Reviews in medical virology · 2021Pooled it
- Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- The laboratory findings and different COVID-19 severities: a systematic review and meta-analysis.Annals of clinical microbiology and antimicrobials · 2021Pooled it
- Early changes in laboratory parameters are predictors of mortality and ICU admission in patients with COVID-19: a systematic review and meta-analysis.Medical microbiology and immunology · 2021Pooled it
- Circulating Levels of Interleukin-6 and Interleukin-10, But Not Tumor Necrosis Factor-Alpha, as Potential Biomarkers of Severity and Mortality for COVID-19: Systematic Review with Meta-analysis.Journal of clinical immunology · 2021Pooled it
- Calculating the Risk of Admission to Intensive Care Units in COVID-19 Patients Using Machine Learning.Journal of clinical medicine · 2025Article
- Sarcopenia, myosteatosis and inflammation are independent prognostic factors of SARS-CoV-2 pneumonia patients admitted to the ICU.Scientific reports · 2025Article
- A comparative study of federated learning methods for COVID-19 detection.Scientific reports · 2024Article
- Observational
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- Lung ultrasound in the COVID-19 era: a lesson to be learned for the future.Internal and emergency medicine · 2023Article
- Conversion and Obsessive-Phobic Symptoms Predict IL-33 and IL-28A Levels in Individuals Diagnosed with COVID-19.Brain sciences · 2023Article
- Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias.Scientific reports · 2023Article
- Predicting factors associated with prolonged intensive care unit stay of patients with COVID-19.Acute and critical care · 2023Article
- The relationship between C-reactive protein and levels of various cytokines in patients with COVID-19: A systematic review and correlation analysis.Health science reports · 2022Article
- A Real Pandora's Box in Pandemic Times: A Narrative Review on the Acute Cardiac Injury Due to COVID-19.Life (Basel, Switzerland) · 2022Review
- Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.Tomography (Ann Arbor, Mich.) · 2022Article
- Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.Emergency radiology · 2022Review
- Early reduction of SARS-CoV-2-replication in bronchial epithelium by kinin BJournal of molecular medicine (Berlin, Germany) · 2022Article
Corrections and comments
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Authors and funding
16 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.
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