Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
- University of Brescia, Italy
Abstract
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.
Data availability
We are unable to share the dataset as it contains sensitive personal data collected during the pandemic at Spedali Civili di Brescia. We cannot share the full data since are data from patients. Interested researchers should contact the authors for any query related to data sharing and submit a project proposal Once defined the goal of the study, and the data needed authors will submit the potential project of collaboration to the IRB of Spedali Civili di Brescia to receive approval to access a deidentified dataset. Please note that other informations related to patients can be acquired, always following approoval of IRB of Spedali Civili di Brescia, not only the ones studied in the paper.Anyway, following request to the authors, it will be possible to share processed version of the dataset ( e.g. an Excel sheet with numbers used to plot the graphs and charts of the manuscript).All code used to analyse the data can be found on GitHub at https://github.com/biostatUniBS/BS_EWS
Article and author information
Author details
Funding
Stefano Calza (PRIN 20178S4EK9)
- Stefano Calza
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The Institutional review board aprpoved the study with the entry code NP4000.
Copyright
© 2021, Garrafa et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,052
- views
-
- 137
- downloads
-
- 27
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
eLife 10:e70640.
https://doi.org/10.7554/eLife.70640
Further reading
-
- Biochemistry and Chemical Biology
- Cancer Biology
Cancer cells display high levels of oncogene-induced replication stress (RS) and rely on DNA damage checkpoint for viability. This feature is exploited by cancer therapies to either increase RS to unbearable levels or inhibit checkpoint kinases involved in the DNA damage response. Thus far, treatments that combine these two strategies have shown promise but also have severe adverse effects. To identify novel, better-tolerated anticancer combinations, we screened a collection of plant extracts and found two natural compounds from the plant, Psoralea corylifolia, that synergistically inhibit cancer cell proliferation. Bakuchiol inhibited DNA replication and activated the checkpoint kinase CHK1 by targeting DNA polymerases. Isobavachalcone interfered with DNA double-strand break repair by inhibiting the checkpoint kinase CHK2 and DNA end resection. The combination of bakuchiol and isobavachalcone synergistically inhibited cancer cell proliferation in vitro. Importantly, it also prevented tumor development in xenografted NOD/SCID mice. The synergistic effect of inhibiting DNA replication and CHK2 signaling identifies a vulnerability of cancer cells that might be exploited by using clinically approved inhibitors in novel combination therapies.
-
- Biochemistry and Chemical Biology
- Cell Biology
Stem cell differentiation involves a global increase in protein synthesis to meet the demands of specialized cell types. However, the molecular mechanisms underlying this translational burst and the involvement of initiation factors remains largely unknown. Here, we investigate the role of eukaryotic initiation factor 3 (eIF3) in early differentiation of human pluripotent stem cell (hPSC)-derived neural progenitor cells (NPCs). Using Quick-irCLIP and alternative polyadenylation (APA) Seq, we show eIF3 crosslinks predominantly with 3’ untranslated region (3’-UTR) termini of multiple mRNA isoforms, adjacent to the poly(A) tail. Furthermore, we find that eIF3 engagement at 3’-UTR ends is dependent on polyadenylation. High eIF3 crosslinking at 3’-UTR termini of mRNAs correlates with high translational activity, as determined by ribosome profiling, but not with translational efficiency. The results presented here show that eIF3 engages with 3’-UTR termini of highly translated mRNAs, likely reflecting a general rather than specific regulatory function of eIF3, and supporting a role of mRNA circularization in the mechanisms governing mRNA translation.
