1. Evaluation of a risk assessment model to predict infection with healthcare facility–onsetClostridioides difficile
- Author
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Chunhui Gu, Zhengjia Chen, Carrie S Tilton, Nicole L Metzger, Chad Robichaux, Mary Elizabeth Sexton, and Steven W. Johnson
- Subjects
Adult ,medicine.medical_specialty ,pharmacists ,Population ,Clostridium infections ,Malignancy ,Logistic regression ,Risk Assessment ,03 medical and health sciences ,0302 clinical medicine ,Clostridioides ,Internal medicine ,Humans ,risk factors ,Medicine ,030212 general & internal medicine ,education ,Retrospective Studies ,Pharmacology ,Cross Infection ,0303 health sciences ,education.field_of_study ,Clinical Report ,Receiver operating characteristic ,Clostridioides difficile ,030306 microbiology ,business.industry ,Health Policy ,Area under the curve ,Odds ratio ,medicine.disease ,ROC curve ,Confidence interval ,antimicrobial stewardship ,Case-Control Studies ,AcademicSubjects/MED00410 ,business ,Risk assessment ,Delivery of Health Care - Abstract
Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. 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.
- Published
- 2021