ArticleAnalytical chemistry2021
Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers.International journal of molecular sciences · 2023Pooled it
- Systematic review with meta-analysis of diagnostic test accuracy for COVID-19 by mass spectrometry.Metabolism: clinical and experimental · 2022Pooled it
- Metabolic shift of chronic myeloid leukemia patients under imatinib-pioglitazone regimen and discontinuation.Medical oncology (Northwood, London, England) · 2021Trial
- Application of Mass Spectrometry-Based Metabolomics and Machine Learning in the Diagnostics of Lyme Neuroborreliosis.ACS omega · 2026Article
- Targeted and untargeted cross-sectional study for sex-specific identification of plasma biomarkers of COVID-19 severity.Analytical and bioanalytical chemistry · 2026Article
- Article
- Diagnostic and Screening AI Tools in Brazil's Resource-Limited Settings: Systematic Review.JMIR AI · 2025Review
- Metabolic Reprogramming in Respiratory Viral Infections: A Focus on SARS-CoV-2, Influenza, and Respiratory Syncytial Virus.Biomolecules · 2025Review
- Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID).Scientific reports · 2025Article
- A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome.Metabolites · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- COVID-19: A Systematic Review of Metabolomics Data and Predicting Potential Biomarkers Based on Pathway Analysis.Tanaffos · 2025Review
- Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data.Diagnostics (Basel, Switzerland) · 2024Article
- Multiplatform Metabolomics: Enhancing the Severity Risk Prognosis of SARS-CoV-2 Infection.ACS omega · 2024Article
- Enhancing Influenza Detection through Integrative Machine Learning and Nasopharyngeal Metabolomic Profiling: A Comprehensive Study.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis.International journal of molecular sciences · 2024Review
- Metabolic signatures of acute respiratory distress syndrome: COVID versus non-COVID.American journal of physiology. Lung cellular and molecular physiology · 2024Review
- Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment.Journal of molecular medicine (Berlin, Germany) · 2024Article
- Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.
Indexed as
Identifiers
What OpenQuestion holds
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.