Defining Sepsis Phenotypes—Two Murine Models of Sepsis and Machine Learning. Issue 6 (June 2022)

• Record Type: Journal Article
• Authors: Stolarski, Allan E.; Kim, Jiyoun; Nudel, Jacob; Gunn, Sophia; Remick, Daniel G.
• Is Part Of: Shock. Volume 57:Issue 6(2022)
• Page Start: 268
• Page End: 273
• Publication Date: 2022-06
• Subjects: Machine learning -- sepsis -- lasso regression -- septic shock -- IL-6; Shock -- Periodicals; Shock -- Periodicals; Choc (Pathologie) -- Périodiques; Shock; Periodicals; 616.0475
• DOI: 10.1097/SHK.0000000000001935
• Languages: English
• ISSNs: 1073-2322

Abstract

ABSTRACT: Introduction: The immunobiology defining the clinically apparent differences in response to sepsis remains unclear. We hypothesize that in murine models of sepsis we can identify phenotypes of sepsis using non-invasive physiologic parameters (NIPP) early after infection to distinguish between different inflammatory states. Methods: Two murine models of sepsis were used: gram-negative pneumonia (PNA) and cecal ligation and puncture (CLP). All mice were treated with broad spectrum antibiotics and fluid resuscitation. High-risk sepsis responders (pDie) were defined as those predicted to die within 72 h following infection. Low-risk responders (pLive) were expected to survive the initial 72 h of sepsis. Statistical modeling in R was used for statistical analysis and machine learning. Results: NIPP obtained at 6 and 24 h after infection of 291 mice (85 PNA and 206 CLP) were used to define the sepsis phenotypes. Lasso regression for variable selection with 10-fold cross-validation was used to define the optimal shrinkage parameters. The variables selected to discriminate between phenotypes included 6-h temperature and 24-h pulse distention, heart rate (HR), and temperature. Applying the model to fit test data (n = 55), area under the curve (AUC) for the receiver operating characteristics (ROC) curve was 0.93. Subgroup analysis of 120 CLP mice revealed a HR of <620 bpm at 24 h as a univariate predictor of pDie. (AUC of ROC curve = 0.90). Subgroup analysis of PNA exposed mice (n = 121) did not reveal a single predictive variable highlighting the complex physiological alterations in response to sepsis. Conclusion: In murine models with various etiologies of sepsis, non-invasive vitals assessed just 6 and 24 h after infection can identify different sepsis phenotypes. Stratification by sepsis phenotypes can transform future studies investigating novel therapies for sepsis.