Evaluation of a risk assessment model to predict infection with healthcare facility-onset Clostridioides difficile.

PURPOSE: We evaluated a previously published risk model (Novant model) to identify patients at risk for healthcare facility-onset Clostridioides difficile infection (HCFO-CDI) at 2 hospitals within a large health system and compared its predictive value to that of a new model developed based on local findings. METHODS: We conducted a retrospective case-control study including adult patients admitted from July 1, 2016, to July 1, 2018. Patients with HCFO-CDI who received systemic antibiotics were included as cases and were matched 1 to 1 with controls (who received systemic antibiotics without developing HCFO-CDI). We extracted chart data on patient risk factors for CDI, including those identified in prior studies and those included in the Novant model. We applied the Novant model to our patient population to assess the model's utility and generated a local model using logistic regression-based prediction scores. A receiver operating characteristic area under the curve (ROC-AUC) score was determined for each model. RESULTS: We included 362 patients, with 161 controls and 161 cases. The Novant model had a ROC-AUC of 0.62 in our population. Our local model using risk factors identifiable at hospital admission included hospitalization within 90 days of admission (adjusted odds ratio [OR], 3.52; 95% confidence interval [CI], 2.06-6.04), hematologic malignancy (adjusted OR, 12.87; 95% CI, 3.70-44.80), and solid tumor malignancy (adjusted OR, 4.76; 95% CI, 1.27-17.80) as HCFO-CDI predictors and had a ROC-AUC score of 0.74. CONCLUSION: The Novant model evaluating risk factors identifiable at admission poorly predicted HCFO-CDI in our population, while our local model was a fair predictor. These findings highlight the need for institutions to review local risk factors to adjust modeling for their patient population.

Development of a risk prediction model for hospital-onset Clostridium difficile infection in patients receiving systemic antibiotics.

BACKGROUND: Clostridium difficile infection (CDI) is recognized as a significant challenge in health care. Identification of high-risk individuals is essential for the development of CDI prevention strategies. The objective of this study was to develop an easily implementable risk prediction model for hospital-onset CDI in patients receiving systemic antimicrobials. METHODS: This retrospective, case-control, multicenter study included adult patients admitted to Novant Health Forsyth Medical Center and Novant Health Presbyterian Medical Center from July 1, 2015, to July 1, 2017, who received systemic antibiotics. Cases were subjects with hospital-onset CDI; controls were subjects without a CDI diagnosis. Cases were matched 1:1 with controls by admitted medical unit type. Variables significantly associated with CDI were incorporated into a multivariate analysis. A logistic regression model was used to formulate a point-based risk prediction model. Positive predictive value, negative predictive value, sensitivity, specificity, and accuracy were determined at various point cutoffs of the model. A receiver operating characteristic-area under the curve was created to assess the discrimination of the model. RESULTS: A total of 200 subjects (100 cases and 100 controls) were included. Most patients were Caucasian and female. Risk factors for CDI identified and incorporated into the model included age ≥70 years (adjusted odds ratio, 1.89; 95% confidence interval 1.05-3.43; P = .0326) and recent hospitalization in the past 90 days (adjusted odds ratio, 3.55; 95% confidence interval 1.90-6.83; P < .0001). Sensitivity and specificity were 76% and 49%, respectively, for scores ≥2 and 20% and 93%, respectively, for a score of 6. Diagnostic performance of various score cutoffs for the model indicated that a score ≥2 was associated with the highest accuracy (63%). The receiver operating characteristic-area under the curve was 0.7. DISCUSSION: We developed a simple-to-implement hospital-onset CDI risk model that included only independent risks that can be obtained immediately on presentation to the health care facility. Despite this, the model had fair discriminatory power. Similar risk factors were found in previously developed models; however, the utility of these models is limited owing to the difficulty of assessing other included risk factors and the inclusion of risk factors that cannot be evaluated until the patient is discharged from the health care facility. CONCLUSIONS: Identification of hospitalized patients who are receiving systemic antibiotics, are ≥70 years old, and were recently admitted to the hospital in the past 90 days may allow for an easily implementable hospital-onset CDI risk prevention strategy.