ArticleGenome medicine2022
An 8-gene machine learning model improves clinical prediction of severe dengue progression.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Host Transcriptional Meta-signatures Reveal Diagnostic Biomarkers for Plasmodium falciparum Malaria.The Journal of infectious diseases · 2024Pooled it
- Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- IgA-driven neutrophil activation underlies severe dengue disease after primary Zika virus infection in humans.Nature immunology · 2026Article
- Multi-cohort analysis identifies a blood-based immune transcriptomic signature for early lung cancer detection.NPJ precision oncology · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
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- Proceedings of the 7Vaccines · 2025Article
- Machine learning for predicting severe dengue in Puerto Rico.Infectious diseases of poverty · 2025Article
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- Immune and vascular modulation by HERVs: the role ofFrontiers in immunology · 2025Article
- Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications.Diagnostics (Basel, Switzerland) · 2024Article
- Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans.Cell reports · 2024Article
- Article
- Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach.Nature immunology · 2023Article
- A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples.Genome medicine · 2023Article
- Preparing for the next viral threat with broad-spectrum antivirals.The Journal of clinical investigation · 2023Review
- Integrated systems immunology approach identifies impaired effector T cell memory responses as a feature of progression to severe dengue fever.Journal of biomedical science · 2023Article
- Magnitude and kinetics of the human immune cell response associated with severe dengue progression by single-cell proteomics.Science advances · 2023Article
- Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients.PLoS neglected tropical diseases · 2023Article
- Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue.PLoS neglected tropical diseases · 2023Article
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21 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundEach year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.
methodsWe integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.
resultsWe identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.
conclusionsThe 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.
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