Defining Sepsis Phenotypes-Two Murine Models of Sepsis and Machine Learning.

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.