# Diagnostic prediction models to identify patients at risk for healthcare-facility–onset Clostridioides difficile: A systematic review of methodology and reporting

## TL;DR

This paper reviews diagnostic models for predicting healthcare-facility onset Clostridioides difficile infection in hospitalized adults, finding limited validation and high risk of bias.

## Contribution

The study systematically evaluates the methodology and quality of HO-CDI diagnostic models, highlighting gaps in validation and reporting.

## Key findings

- Most models used age, high-risk antibiotics, and CDI history as predictors.
- Only a few studies reported model calibration or external validation.
- All studies had a high risk of bias due to poor reporting of methods.

## Abstract

To systematically review the methodology, performance, and generalizability of diagnostic models for predicting the risk of healthcare-facility–onset (HO) Clostridioides difficile infection (CDI) in adult hospital inpatients (aged ≥18 years).

CDI is the most common cause of healthcare-associated diarrhea. Prediction models that identify inpatients at risk of HO-CDI have been published; however, the quality and utility of these models remain uncertain.

Two independent reviewers evaluated articles describing the development and/or validation of multivariable HO-CDI diagnostic models in an inpatient setting. All publication dates, languages, and study designs were considered. Model details (eg, sample size and source, outcome, and performance) were extracted from the selected studies based on the CHARMS checklist. The risk of bias was further assessed using PROBAST.

Of the 3,030 records evaluated, 11 were eligible for final analysis, which described 12 diagnostic models. Most studies clearly identified the predictors and outcomes but did not report how missing data were handled. The most frequent predictors across all models were advanced age, receipt of high-risk antibiotics, history of hospitalization, and history of CDI. All studies reported the area under the receiver operating characteristic curve (AUROC) as a measure of discriminatory ability. However, only 3 studies reported the model calibration results, and only 2 studies were externally validated. All of the studies had a high risk of bias.

The studies varied in their ability to predict the risk of HO-CDI. Future models will benefit from the validation on a prospective external cohort to maximize external validity.