ArticlePloS one2023
Early biochemical analysis of COVID-19 patients helps severity prediction.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- PD-L1 expression scoring using artificial intelligence algorithms in non-small cell lung cancer harbouring ROS1 fusions: potential clinical-biological significance.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 2025Review
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.
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Registered trials
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