ArticleeLife2021
Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Article
- Blood Count and Renal Functionality Assessments in the Emergency Section Disclose Morbidity and Mortality in Omicron COVID-19 Patients: A Retrospective Study.Clinics and practice · 2024Article
- Machine learning approach to identify malaria risk in travelers using real-world evidence.Heliyon · 2024Article
- MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.Scientific reports · 2023Article
- Mitochondrial Dysfunction in Peripheral Blood Mononuclear Cells as Novel Diagnostic Tools for Non-Alcoholic Fatty Liver Disease: Visualizing Relationships with Known and Potential Disease Biomarkers.Diagnostics (Basel, Switzerland) · 2023Article
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
- Blood Biomarkers from the Emergency Department Disclose Severe Omicron COVID-19-Associated Outcomes.Microorganisms · 2023Article
- Deep Survival Analysis With Clinical Variables for COVID-19.IEEE journal of translational engineering in health and medicine · 2023Article
- Clinical characteristics and severity prediction score of Adenovirus pneumonia in immunocompetent adult.PloS one · 2023Article
- Predictive markers for clinical outcomes in a cohort of diabetic patients hospitalized for COVID-19.Diabetology & metabolic syndrome · 2022Article
- Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2022Article
- Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19.Cell reports. Medicine · 2022Article
- Role of a Chest X-ray Severity Score in a Multivariable Predictive Model for Mortality in Patients with COVID-19: A Single-Center, Retrospective Study.Journal of clinical medicine · 2022Article
- Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section.Diagnostics (Basel, Switzerland) · 2022Article
- The Role of Simple Blood Tests and a Modified Chest X-Ray Scoring System in Assessing the Severity Disease and Mortality Risk in COVID-19 Patients in a Secondary Hospital, Indonesia.International journal of general medicine · 2022Article
- Less is more: an ecological and economic point of view on appropriate use of lab testing for COVID-19 patients.Bioanalysis · 2021Article
- Temporal evolution of laboratory characteristics in patients critically ill with COVID‑19 admitted to the intensive care unit (Review).Medicine internationalReview
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated 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. Receiver operating characteristic (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 (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.