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

BACKGROUND: 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. METHODS: We integrated 11 public...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959795/
https://www.ncbi.nlm.nih.gov/pubmed/35346346
http://dx.doi.org/10.1186/s13073-022-01034-w
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author 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
author_facet 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
author_sort Liu, Yiran E.
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01034-w.
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spelling pubmed-89597952022-03-29 An 8-gene machine learning model improves clinical prediction of severe dengue progression 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 Genome Med Research BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01034-w. BioMed Central 2022-03-29 /pmc/articles/PMC8959795/ /pubmed/35346346 http://dx.doi.org/10.1186/s13073-022-01034-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
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
An 8-gene machine learning model improves clinical prediction of severe dengue progression
title An 8-gene machine learning model improves clinical prediction of severe dengue progression
title_full An 8-gene machine learning model improves clinical prediction of severe dengue progression
title_fullStr An 8-gene machine learning model improves clinical prediction of severe dengue progression
title_full_unstemmed An 8-gene machine learning model improves clinical prediction of severe dengue progression
title_short An 8-gene machine learning model improves clinical prediction of severe dengue progression
title_sort 8-gene machine learning model improves clinical prediction of severe dengue progression
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959795/
https://www.ncbi.nlm.nih.gov/pubmed/35346346
http://dx.doi.org/10.1186/s13073-022-01034-w
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