Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

Yidi Qin, Kate F. Kernan, Zhenjiang Fan, Hyun Jung Park, Soyeon Kim, Scott W. Canna, John A. Kellum, Robert A. Berg, David Wessel, Murray M. Pollack, Kathleen Meert, Mark Hall, Christopher Newth, John C. Lin, Allan Doctor, Tom Shanley, Tim Cornell, Rick E. Harrison, Athena F. Zuppa, Russell BanksRon W. Reeder, Richard Holubkov, Daniel A. Notterman, J. Michael Dean, Joseph A. Carcillo

Research output: Contribution to journalArticlepeer-review

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Abstract

Background: Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions. Methods: We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-induced Multiple organ failure Study (PHENOMS) dataset of 404 children. 24-hour computable phenotypes are derived using 25 available bedside variables including C-reactive protein and ferritin. Results: Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to all other phenotypes, PedSep-A patients (n = 135; 2% mortality) were younger and previously healthy, with the lowest C-reactive protein and ferritin levels, the highest lymphocyte and platelet counts, highest heart rate, and lowest creatinine (p < 0.05); PedSep-B patients (n = 102; 12% mortality) were most likely to be intubated and had the lowest Glasgow Coma Scale Score (p < 0.05); PedSep-C patients (n = 110; mortality 10%) had the highest temperature and Glasgow Coma Scale Score, least pulmonary failure, and lowest lymphocyte counts (p < 0.05); and PedSep-D patients (n = 56, 34% mortality) had the highest creatinine and number of organ failures, including renal, hepatic, and hematologic organ failure, with the lowest platelet counts (p < 0.05). PedSep-D had the highest likelihood of developing thrombocytopenia-associated multiple organ failure (Adj OR 47.51 95% CI [18.83–136.83], p < 0.0001) and macrophage activation syndrome (Adj OR 38.63 95% CI [13.26–137.75], p < 0.0001). Conclusions: Four computable phenotypes are derived, with PedSep-D being optimal for enrollment in early personalized anti-inflammatory trials targeting thrombocytopenia-associated multiple organ failure and macrophage activation syndrome in pediatric sepsis. A computer tool for identification of individual patient membership (www.pedsepsis.pitt.edu) is provided. Reproducibility will be assessed at completion of two ongoing pediatric sepsis studies.

Original languageEnglish
Article number128
JournalCritical Care
Volume26
Issue number1
DOIs
StatePublished - Dec 2022

Keywords

  • Hyperferritinemic sepsis
  • Immunoparalysis-associated multiple organ failure
  • Macrophage activation syndrome
  • Multiple organ failure
  • Sequential multiple organ failure
  • Severe sepsis
  • Thrombocytopenia-associated multiple organ failure

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