Defining Sepsis Phenotypes-Two Murine Models of Sepsis and Machine Learning.
Shock
; 57(6): 268-273, 2022 06 01.
Article
en En
| MEDLINE
| ID: mdl-35759307
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 72âh following infection. Low-risk responders (pLive) were expected to survive the initial 72âh of sepsis. Statistical modeling in R was used for statistical analysis and machine learning.RESULTS:
NIPP obtained at 6 and 24âh 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 (nâ=â55), 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 <620âbpm at 24âh as a univariate predictor of pDie. (AUC of ROC curveâ=â0.90). Subgroup analysis of PNA exposed mice (nâ=â121) 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 24âh after infection can identify different sepsis phenotypes. Stratification by sepsis phenotypes can transform future studies investigating novel therapies for sepsis.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Sepsis
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Shock
Asunto de la revista:
ANGIOLOGIA
/
CARDIOLOGIA
Año:
2022
Tipo del documento:
Article