Using Latent Class Mixture Models to Define Sepsis Endotypes
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Severe sepsis is associated with high mortality and is a common problem in the United States. Recently, studies have shown that efforts focused on lowering cytokine levels improve survival. The aim of this work is to define sepsis endotypes using longitudinal cytokine measurements. Sepsis endotypes were defined using latent class mixture models. Latent class mixture models were modeled using a natural log transformation of the actual time measurements. No other covariates were modeled and a parameterized link function using a basis of I-splines was chosen over a linear transformation to increa...
Severe sepsis is associated with high mortality and is a common problem in the United States. Recently, studies have shown that efforts focused on lowering cytokine levels improve survival. The aim of this work is to define sepsis endotypes using longitudinal cytokine measurements. Sepsis endotypes were defined using latent class mixture models. Latent class mixture models were modeled using a natural log transformation of the actual time measurements. No other covariates were modeled and a parameterized link function using a basis of I-splines was chosen over a linear transformation to increase flexibility in the latent class trajectories. The number of latent classes were determined by a combination of the lowest BIC and clinical significance. After creating models for a variety of subsets derived from the source population, it was determined that mortality within a particular trajectory class is not only dependent upon the baseline cytokine value, but also dependent upon the rate of decent after baseline. A class with high baseline cytokine values that decrease quickly has lower mortality rates than classes who do not decline quickly.
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