An 8-gene machine learning model improves clinical prediction of severe dengue progression

Medientyp: E-Artikel
Titel: An 8-gene machine learning model improves clinical prediction of severe dengue progression
Beteiligte: Liu, Yiran E.; Saul, Sirle; Rao, Aditya Manohar; Robinson, Makeda Lucretia; Agudelo Rojas, Olga Lucia; Sanz, Ana Maria; Verghese, Michelle; Solis, Daniel; Sibai, Mamdouh; Huang, Chun Hong; Sahoo, Malaya Kumar; Gelvez, Rosa Margarita; Bueno, Nathalia; Estupiñan Cardenas, Maria Isabel; Villar Centeno, Luis Angel; Rojas Garrido, Elsa Marina; Rosso, Fernando; Donato, Michele; Pinsky, Benjamin A.; Einav, Shirit; Khatri, Purvesh
Erschienen: Springer Science and Business Media LLC, 2022
Erschienen in: Genome Medicine
Sprache: Englisch
DOI: 10.1186/s13073-022-01034-w
ISSN: 1756-994X
Schlagwörter: Genetics (clinical) ; Genetics ; Molecular Biology ; Molecular Medicine
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Beschreibung: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Each 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.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We 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.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We 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.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The 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.</jats:p> </jats:sec>
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