Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses : Results from a prospective multi-center observational cohort
Background: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions.
Methods: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme.
Findings: Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes.
Interpretation: Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Research square - (2023) vom: 06. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Atreya, Mihir R [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 24.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.21203/rs.3.rs-3692289/v1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365966789 |
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| 100 | 1 | |a Atreya, Mihir R |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses |b Results from a prospective multi-center observational cohort |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Revised 24.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Background: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions | ||
| 520 | |a Methods: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme | ||
| 520 | |a Findings: Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes | ||
| 520 | |a Interpretation: Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill | ||
| 650 | 4 | |a Preprint | |
| 650 | 4 | |a Adaptive immunity | |
| 650 | 4 | |a Biomarkers | |
| 650 | 4 | |a Endophenotype | |
| 650 | 4 | |a Endothelial dysfunction | |
| 650 | 4 | |a Endotype | |
| 650 | 4 | |a Gene-expression | |
| 650 | 4 | |a Innate immunity | |
| 650 | 4 | |a Latent Profile Analyses | |
| 650 | 4 | |a Multiple Organ Dysfunction | |
| 650 | 4 | |a Phenotype | |
| 650 | 4 | |a Precision Medicine | |
| 650 | 4 | |a Sepsis | |
| 650 | 4 | |a Septic shock | |
| 700 | 1 | |a Huang, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Moore, Andrew R |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Hasin-Brumshtein, Yehudit |e verfasserin |4 aut | |
| 700 | 1 | |a Fitzgerald, Julie C |e verfasserin |4 aut | |
| 700 | 1 | |a Weiss, Scott L |e verfasserin |4 aut | |
| 700 | 1 | |a Cvijanovich, Natalie Z |e verfasserin |4 aut | |
| 700 | 1 | |a Bigham, Michael T |e verfasserin |4 aut | |
| 700 | 1 | |a Jain, Parag N |e verfasserin |4 aut | |
| 700 | 1 | |a Schwarz, Adam J |e verfasserin |4 aut | |
| 700 | 1 | |a Lutfi, Riad |e verfasserin |4 aut | |
| 700 | 1 | |a Nowak, Jeffrey |e verfasserin |4 aut | |
| 700 | 1 | |a Thomas, Neal J |e verfasserin |4 aut | |
| 700 | 1 | |a Quasney, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Dahmer, Mary K |e verfasserin |4 aut | |
| 700 | 1 | |a Baines, Torrey |e verfasserin |4 aut | |
| 700 | 1 | |a Haileselassie, Bereketeab |e verfasserin |4 aut | |
| 700 | 1 | |a Lautz, Andrew J |e verfasserin |4 aut | |
| 700 | 1 | |a Stanski, Natalja L |e verfasserin |4 aut | |
| 700 | 1 | |a Standage, Stephen W |e verfasserin |4 aut | |
| 700 | 1 | |a Kaplan, Jennifer M |e verfasserin |4 aut | |
| 700 | 1 | |a Zingarelli, Basilia |e verfasserin |4 aut | |
| 700 | 1 | |a Sweeney, Timothy E |e verfasserin |4 aut | |
| 700 | 1 | |a Khatri, Purvesh |e verfasserin |4 aut | |
| 700 | 1 | |a Sanchez-Pinto, L Nelson |e verfasserin |4 aut | |
| 700 | 1 | |a Kamaleswaran, Rishikesan |e verfasserin |4 aut | |
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