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Updated: Jan 3, 2026
Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
Published on: December 10, 2016
Risk prediction for 30-day mortality among patients with Clostridium difficile infections: a retrospective cohort
Hsiu-Yin Chiang1, Han-Chun Huang1, Chih-Wei Chung1
11Big Data Center, China Medical University Hospital, Taichung, 404 Taiwan.
Insights
This study developed a new risk model for predicting 30-day mortality in patients with Clostridium difficile infection (CDI). The novel model, incorporating BUN-to-SCr ratio and glucose levels, demonstrated superior predictive performance compared to existing guidelines.
Area of Science:
- Infectious Diseases
- Clinical Epidemiology
- Biostatistics
Background:
- Current guidelines show limitations in predicting severe outcomes for Clostridium difficile infection (CDI).
- There is a need for improved risk prediction models for 30-day mortality in CDI patients.
Purpose of the Study:
- To develop and validate a risk prediction model for 30-day mortality in hospitalized patients with CDI.
- To evaluate the model's performance against existing clinical guidelines.
Main Methods:
- Retrospective cohort study at a tertiary medical center.
- Inclusion of adult inpatients with confirmed CDI and diarrhea.
- Development of multivariable Cox and logistic regression models using biochemical profiles (WBC, SCr, BUN, glucose, albumin) and clinical factors.
Main Results:
- Malignancy, elevated serum creatinine (SCr), BUN-to-SCr ratio > 20, and increased glucose were significant predictors of 30-day mortality.
- The developed model showed significantly superior predictive performance (Harrell's c-statistic = 0.727) for 30-day mortality compared to SHEA-IDSA and ESCMID guidelines.
- The model also demonstrated superior prediction for prolonged ICU stay.
Conclusions:
- A novel risk prediction model incorporating BUN-to-SCr ratio and glucose levels offers improved accuracy for predicting 30-day mortality in CDI patients.
- The new model outperforms current guidelines, potentially aiding in better patient management and resource allocation.
- This model can enhance the prediction of prolonged intensive care unit (ICU) stays following CDI.
Background:
Current guidelines have unsatisfied performance in predicting severe outcomes after Clostridium difficile infection (CDI). Our objectives were to develop a risk prediction model for 30-day mortality and to examine its performance among inpatients with CDI.
Methods:
This retrospective cohort study was conducted at China Medical University Hospital, a 2111-bed tertiary medical center in central Taiwan. We included adult inpatients who had a first positive C. difficile culture or toxin assay and had diarrhea as the study population. The main exposure of interest was the biochemical profiles of white blood cell count, serum creatinine (SCr), estimated glomerular filtration rate, blood urea nitrogen (BUN), serum albumin, and glucose. The primary outcome was the 30-day all-cause mortality and the secondary outcome was the length of stay in the intensive care units (ICU) following CDI. A multivariable Cox model and a logistic regression model were developed using clinically relevant and statistically significant variables for 30-day mortality and for length of ICU stay, respectively. A risk scoring system was established by standardizing the coefficients. We compared the performance of our models and the guidelines.
Results:
Of 401 patients, 23.4% died within 30 days. In the multivariable model, malignancy (hazard ratio [HR] = 1.95), ≥ 1.5-fold rise in SCr (HR = 2.27), BUN-to-SCr ratio > 20 (HR = 2.04), and increased glucose (≥ 193 vs < 142 mg/dL, HR = 2.18) were significant predictors of 30-day mortality. For patients who survived the first 30 days of CDI, BUN-to-SCr ratio > 20 (Odds ratio [OR] = 4.01) was the only significant predictor for prolonged (> 9 days) length of ICU stay following CDI. The Harrell's c statistic of our Cox model for 30-day mortality (0.727) was significantly superior to those of SHEA-IDSA 2010 (0.645), SHEA-IDSA 2018 (0.591), and ECSMID (0.650). Similarly, the conventional c statistic of our logistic regression model for prolonged ICU stay (0.737) was significantly superior to that of the guidelines (SHEA-IDSA 2010, c = 0.600; SHEA-IDSA 2018, c = 0.634; ESCMID, c = 0.645). Our risk prediction scoring system for 30-day mortality correctly reclassified 20.7, 32.1, and 47.9% of patients, respectively.
Conclusions:
Our model that included novel biomarkers of BUN-to-SCr ratio and glucose have a higher predictive performance of 30-day mortality and prolonged ICU stay following CDI than do the guidelines.
