Derivation of four computable 24-hour pediatric sepsis phenotypes to facilitate personalized enrollment in early precise anti-inflammatory clinical trials
Authors: Qin Yidi , Kernan Kate F. , Fan Zhenjiang , Park Hyun-Jung , Kim Soyeon , Canna Scott W. , Kellum John A , Berg Robert A. , Wessel David , Pollack Murray M. , Meert Kathleen , Hall Mark , Newth Christopher , Lin John C. , Doctor Allan , Shanley Tom , Cornell Tim , Harrison Rick E. , Zuppa Athena F. , Banks Russell , Reeder Ron W. , Holubkov Richard , Notterman Daniel A. , Dean J. Michael , Carcillo Joseph A. 
Published: December 2021

Objective

Thrombotic microangiopathy inducedThrombocytopenia Associated Multiple Organ Failureand hyperinflammatoryMacrophage Activation Syndromeare important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. Our objective is to derive computable 24-hour sepsis phenotypes to facilitate enrollment in early precise anti-inflammatory trials targeting mortality from these conditions.

Design

Machine learning analysis using consensus k-means clustering.

Setting

Nine pediatric intensive care units.

Patients

404 children with severe sepsis.

Interventions

24-hour computable phenotypes derived using 25 bedside variables including C-reactive protein and ferritin.

Measurements and Main Results

Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to the overall population mean, PedSep-A has the least inflammation (median C-reactive protein 7.3 mg/dL, ferritin 125 ng/mL), younger age, less chronic illness, and more respiratory failure (n = 135; 2% mortality); PedSep-B (median C-reactive protein 13.2 mg/dL, ferritin 225 ng/ mL) has organ failure with intubated respiratory failure, shock, and Glasgow Coma Scale score < 7 (n = 102, 12% mortality); PedSep-C (median C-reactive protein 15.2 mg/dL, ferritin 405 ng/mL) has elevated ferritin, lymphopenia, more shock, more hepatic failure and less respiratory failure (n = 110; mortality 10%); and, PedSep D (median C-reactive protein 13.1 mg/dL ferritin 610 ng/mL), has hyperferritinemic, thrombocytopenic multiple organ failure with more cardiovascular, respiratory, hepatic, renal, hematologic, and neurologic system failures (n = 56, 34% mortality). PedSep-D has highest likelihood ofThrombocytopenia Associated Multiple Organ Failure(Adj OR 47.51 95% CI [18.83-136.83], p < 0.0001) andMacrophage Activation Syndrome(Adj OR 38.63 95% CI [13.26-137.75], p <0.0001), and an observed survivor interaction with combined methylprednisolone and intravenous immunoglobulin therapies (p < 0.05).

CONCLUSIONS AND RELEVANCE

Machine learning identifies four computable phenotypes (www.pedsepsis.pitt.edu). Membership in PedSep-D appears optimal for enrollment in early anti-inflammatory trials targetingThrombocytopenia Associated Multiple Organ FailureandMacrophage Activation Syndrome.

Author’s Comment

Question

Can machine learning methods derive 24-hour computable pediatric sepsis phenotypes that facilitate early identification of patients for enrollment in precise anti-inflammatory therapy trials?

Findings

Four distinct phenotypes (PedSep-A, B, C, and D) were derived by assessing 25 bedside clinical variables in 404 children with sepsis. PedSep-D patients had a thrombotic microangiopathy and hyperinflammatory macrophage activation biomarker response, and improved survival odds associated with combined methylprednisolone plus intravenous immunoglobulin therapy.

Meaning

Four novel computable 24-hour phenotypes are identifiable (www.pedsepsis.pitt.edu) that could potentially facilitate enrollment in early precise anti-inflammatory trials targeting thrombotic microangiopathy and macrophage activation in pediatric sepsis.