ArticleViruses2023
Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.
Article in Viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed, 12 citations in OpenAlex.
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
- Complex Pattern of Platelet Activation/Reactivity After SARS-CoV-2 Infection.International journal of molecular sciences · 2024Review
- Prehospital Pandemic Respiratory Infection Emergency System Triage score can effectively predict the 30-day mortality of COVID-19 patients with pneumonia.Annals of medicine · 2024Article
- The Relationship between the Laboratory Biomarkers of SARS-CoV-2 Patients with Type 2 Diabetes at Discharge and the Severity of the Viral Pathology.Journal of personalized medicine · 2024Article
- Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Article
- Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Article
- Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares.Bioengineering (Basel, Switzerland) · 2024Article
- External validation of the sICOP score for early mortality in mechanically ventilated patients with COVID-19.Acute medicine & surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO
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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.