# A Data-Driven Analysis of Pediatric Organ Dysfunction Patterns To Discover Sepsis Phenotypes

> **NIH NIH R21** · LURIE CHILDREN'S HOSPITAL OF CHICAGO · 2020 · $209,261

## Abstract

PROJECT SUMMARY
 Sepsis is a common cause of pediatric multiple organ dysfunction syndrome (MODS), which, in turn, is
a final common pathway for death in most critically ill children. Unfortunately, approaching sepsis as a
uniform clinical syndrome has contributed to dozens of failed drug trials and hundreds of single biomarkers
demonstrating poor performance. In light of this, some sepsis researchers have focused their efforts in
discovering sepsis subtypes that represent the specific pathobiological pattern underlying the syndrome in
different subgroups of patients. These subtypes, in theory, are associated with therapy response and outcomes
and could be used to guide individualized therapy. While optimal methods to uncover sepsis subtypes are still
unclear, there are strong advantages to using a phenotype-based approach, where the clinical characteristics of
patients are used as surrogates of the underlying pathobiology. We hypothesize that the clinical characteristics
of children with sepsis can be adequately quantified using calibrated pediatric organ dysfunction scores and
that the early patterns of organ dysfunction can be used to identify novel phenotypes of sepsis with prognostic
and therapeutic relevance. Existing pediatric organ dysfunction scores are weighted in a multivariable model to
predict mortality and are not designed to represent the progressive loss of function of each individual organ
system. In light of this, we recently adapted and validated a pediatric version of the Sequential Organ Failure
Assessment score (pSOFA) which allocates similar weight to each failing organ system. However, the validation
of pSOFA was performed in a single center and no attempts were made to calibrate the organ-specific
subscores to optimize the grading of each organ dysfunction (i.e. respiratory, cardiovascular, hepatic, renal,
neurologic, and hematologic). In this study, we aim to: 1) re-calibrate and validate the pSOFA subscores using a
multi-center observational cohort of critically ill children with confirmed or suspected infection; and (2)
analyze the early patterns of organ dysfunction in critically ill children with sepsis using the pSOFA subscores
in order to identify novel phenotypes of sepsis with prognostic and therapeutic relevance. We will use Subgraph
Augmented Non-Negative Matrix Factorization to model and visualize the patterns of organ dysfunction during
the acute phase of sepsis. These patterns will form the basis for the characterization of novel sepsis phenotypes.
We will then evaluate whether the novel sepsis phenotypes are associated with outcomes in a validation cohort
and independently associated with response to two common adjuvant treatments: corticosteroids and albumin
infusions. The results from this study will be used as the foundation for follow-up studies to characterize the
underlying molecular and cellular perturbations associated with the identified phenotypes.

## Key facts

- **NIH application ID:** 9895827
- **Project number:** 5R21HD096402-02
- **Recipient organization:** LURIE CHILDREN'S HOSPITAL OF CHICAGO
- **Principal Investigator:** Lazaro Nelson Sanchez-Pinto
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $209,261
- **Award type:** 5
- **Project period:** 2019-04-09 → 2021-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895827

## Citation

> US National Institutes of Health, RePORTER application 9895827, A Data-Driven Analysis of Pediatric Organ Dysfunction Patterns To Discover Sepsis Phenotypes (5R21HD096402-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9895827. Licensed CC0.

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