A Predictive Model to Identify Complicated Clostridiodes difficile Infection.

ABSTRACT:

Background

Clostridioides difficile infection (CDI) is a leading cause of health care-associated infection and may result in organ dysfunction, colectomy, and death. Published risk scores to predict severe complications from CDI demonstrate poor performance upon external validation. We hypothesized that building and validating a model using geographically and temporally distinct cohorts would more accurately predict risk for complications from CDI.

Methods

We conducted a multicenter retrospective cohort study of adults diagnosed with CDI. After randomly partitioning the data into training and validation sets, we developed and compared 3 machine learning algorithms (lasso regression, random forest, stacked ensemble) with 10-fold cross-validation to predict disease-rela

SUBMITTER: Berinstein JA 

PROVIDER: S-EPMC9938520 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature