The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters
Open accessAbstract
Objectives: To find and validate generalizable sepsis subtypes using data-driven clustering. Design: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700). Setting: Retrospective analysis. Subjects: Persons admitted to the hospital with bacterial sepsis. Interventions: None. Measurements and Main Results: A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed “Inflammopathic, Adaptive, and Coagulopathic.” We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts. Conclusions: The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.
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Cites
The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Cluster analysis and clinical asthma phenotypes
Haldar, P., Pavord, I. D., Shaw, D. E., Berry, M. A., Thomas, M., Brightling, C. E., Wardlaw, A. J., & Green, R. H. (2008) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200711-1754oc · Cited by 1,987
Shakoory, B., Carcillo, J. A., Chatham, W. W., Amdur, R. L., Zhao, H., Dinarello, C. A., Cron, R. Q., & Opal, S. M. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001402 · Cited by 867
Sweeney, T. E., Wong, H. R., & Khatri, P. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf7165 · Cited by 334
Self-citationGene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans
Ahn, S. H., Tsalik, E. L., Cyr, D. D., Zhang, Y., van Velkinburgh, J. C., Langley, R. J., Glickman, S. W., Cairns, C. B., Zaas, A. K., Rivers, E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0048979 · Cited by 61
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Cited by
Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis
Seymour, C. W., Kennedy, J. N., Wang, S., Chang, C.-C. H., Elliott, C. F., Xu, Z., Berry, S., Clermont, G., Cooper, G., Gomez, H., et al. (2019) · JAMA · doi:10.1001/jama.2019.5791 · Cited by 1,567
Antcliffe, D. B., Burnham, K. L., Al-Beidh, F., Santhakumaran, S., Brett, S. J., Hinds, C. J., Ashby, D., Knight, J. C., & Gordon, A. C. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201807-1419oc · Cited by 365
Immunopathophysiology of human sepsis
Wiersinga, W. J., & van der Poll, T. (2022) · eBioMedicine · doi:10.1016/j.ebiom.2022.104363 · Cited by 322
Sepsis therapies: learning from 30 years of failure of translational research to propose new leads
Cavaillon, J., Singer, M., & Skirecki, T. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201810128 · Cited by 319
Putting the data before the algorithm in big data addressing personalized healthcare
Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149
Metadata
- Authors
- Sweeney, T. E., Azad, T. D., Donato, M., Haynes, W. A., Perumal, T. M., Henao, R., Bermejo-Martin, J. F., Almansa, R., Tamayo, E., Howrylak, J. A., Choi, A., Parnell, G. P., Tang, B., Nichols, M., Woods, C. W., Ginsburg, G. S., Kingsmore, S. F., Omberg, L., Mangravite, L. M., Wong, H. R., Tsalik, E. L., Langley, R. J., & Khatri, P.
- Venue
- Critical Care Medicine
- Published
- 2018-06
- Volume
- 46
- Issue
- 6
- Pages
- 915-925
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W2792583887
- Cited by
- 362
- References
- 6
- FWCI
- 20.11
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Sweeney, T. E., Azad, T. D., Donato, M., Haynes, W. A., Perumal, T. M., Henao, R., Bermejo-Martin, J. F., Almansa, R., Tamayo, E., Howrylak, J. A., Choi, A., Parnell, G. P., Tang, B., Nichols, M., Woods, C. W., Ginsburg, G. S., Kingsmore, S. F., Omberg, L., Mangravite, L. M., . . . Khatri, P. (2018). Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters. Critical Care Medicine. https://doi.org/10.1097/ccm.0000000000003084
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