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Related Experiment Video

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A Predictive Model to Identify Complicated Clostridiodes difficile Infection.

Jeffrey A Berinstein1, Calen A Steiner2,3, Samara Rifkin1

  • 1Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA.

Open Forum Infectious Diseases
|February 23, 2023
PubMed
Summary

This study developed accurate machine learning models to predict severe Clostridioides difficile infection complications. The models showed good performance but performed worse in non-White patients, highlighting a need for further research to reduce disparities.

Keywords:
Clostridiodes difficilemachine learningpredictive modelsevere disease

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Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Health Services Research

Background:

  • Clostridioides difficile infection (CDI) is a significant cause of healthcare-associated infections, leading to severe outcomes like organ dysfunction, colectomy, and death.
  • Existing risk scores for predicting severe CDI complications lack external validation and show poor performance.
  • A hypothesis was formed that models built and validated on distinct patient cohorts would improve prediction accuracy for CDI complications.

Purpose of the Study:

  • To develop and validate machine learning models for predicting severe complications of Clostridioides difficile infection.
  • To compare the performance of lasso regression, random forest, and stacked ensemble algorithms in predicting CDI-related complications.
  • To identify key variables associated with severe CDI outcomes.

Main Methods:

  • A multicenter retrospective cohort study included 3646 adult patients diagnosed with CDI.
  • Data were randomly split into training and validation sets, with 10-fold cross-validation used for model development.
  • Three machine learning algorithms were employed to predict intensive care unit admission, colectomy, or death within 30 days of CDI diagnosis.

Main Results:

  • All three developed models demonstrated strong predictive performance, with an area under the receiver operating curve (AUC) ranging from 0.88 to 0.89.
  • Key predictors of severe CDI complications included albumin, bicarbonate, creatinine change, non-CDI ICU admission, and concurrent non-CDI antibiotics.
  • Model performance remained robust across sensitivity analyses, but accuracy was notably lower for non-White patients compared to White patients.

Conclusions:

  • A validated prediction model using a large, diverse patient population accurately estimates the risk of severe complications from Clostridioides difficile infection.
  • Future research should focus on addressing the observed disparities in model accuracy between different racial groups.
  • Further efforts are needed to enhance the overall performance of CDI complication prediction models.