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494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection
BACKGROUND: Clostridium difficile is a pathogen that may be a component of normal microbiota. In 2011, there were an estimated 453,000 cases of CDI in the United States and 29,300 deaths. Diagnosis of CDI is of often accomplished through nucleic acid amplification testing (NAAT) for C. difficile tox...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253395/ http://dx.doi.org/10.1093/ofid/ofy210.503 |
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author | Hawkins, Marten Baumgartner, Michael VanBeckum, Danielle Buck, Julia Holley, Crystal |
author_facet | Hawkins, Marten Baumgartner, Michael VanBeckum, Danielle Buck, Julia Holley, Crystal |
author_sort | Hawkins, Marten |
collection | PubMed |
description | BACKGROUND: Clostridium difficile is a pathogen that may be a component of normal microbiota. In 2011, there were an estimated 453,000 cases of CDI in the United States and 29,300 deaths. Diagnosis of CDI is of often accomplished through nucleic acid amplification testing (NAAT) for C. difficile toxin genes, which carries a risk of false-positive results. In 1996, Katz et al. created a screen for CDI that was positive if the patient had significant diarrhea and either abdominal pain or prior antibiotic usage. Today, we believe that this tool is worth revisiting with increased incidence of CDI and improved testing methods. Our aim is to determine the current usefulness of the Katz et al. 1996 clinical decision tool for CDI. METHODS: We conducted a retrospective cross-sectional chart review at a Midwestern teaching hospital. All patients tested for CDI between June 1, 2016 and May 31, 2017 were initially eligible. Participants were excluded from data collection on the basis of missing information, a previous positive CDI test in the last 8 weeks or age <18 years. Charts were reviewed for age, sex, diarrhea, abdominal pain, antibiotic use, prior positive testing for CDI, and length of hospitalization. Data were analyzed using SAS Software. RESULTS: Of the initial 432 charts analyzed, 202 (46.8%) had no documented amount of diarrhea and 16 more were missing other data points, leaving 214 of 432 (49.5%) charts that included all data to be used for analysis. Of these 18 of 214 (8.4%) were positive results. The Katz screen was positive in 85 of 214 (40.2%) cases. The sensitivity, specificity, positive predictive value, and negative predictive value, respectively, were 61, 62, 13, and 95. CONCLUSION: Katz et al. found a sensitivity, specificity, positive predictive value and negative predictive value of 80, 45, 18 and 94, respectively. The differences between these values and our own may be due to changes in the testing methodology and prevalence of CDI compared with a 1992 study population. The negative predictive value remains a strength. If this screening tool had been applied to our population, there may have been 128 (59.8%) fewer tests, but seven (38.9%) missed positive results. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-6253395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62533952018-11-28 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection Hawkins, Marten Baumgartner, Michael VanBeckum, Danielle Buck, Julia Holley, Crystal Open Forum Infect Dis Abstracts BACKGROUND: Clostridium difficile is a pathogen that may be a component of normal microbiota. In 2011, there were an estimated 453,000 cases of CDI in the United States and 29,300 deaths. Diagnosis of CDI is of often accomplished through nucleic acid amplification testing (NAAT) for C. difficile toxin genes, which carries a risk of false-positive results. In 1996, Katz et al. created a screen for CDI that was positive if the patient had significant diarrhea and either abdominal pain or prior antibiotic usage. Today, we believe that this tool is worth revisiting with increased incidence of CDI and improved testing methods. Our aim is to determine the current usefulness of the Katz et al. 1996 clinical decision tool for CDI. METHODS: We conducted a retrospective cross-sectional chart review at a Midwestern teaching hospital. All patients tested for CDI between June 1, 2016 and May 31, 2017 were initially eligible. Participants were excluded from data collection on the basis of missing information, a previous positive CDI test in the last 8 weeks or age <18 years. Charts were reviewed for age, sex, diarrhea, abdominal pain, antibiotic use, prior positive testing for CDI, and length of hospitalization. Data were analyzed using SAS Software. RESULTS: Of the initial 432 charts analyzed, 202 (46.8%) had no documented amount of diarrhea and 16 more were missing other data points, leaving 214 of 432 (49.5%) charts that included all data to be used for analysis. Of these 18 of 214 (8.4%) were positive results. The Katz screen was positive in 85 of 214 (40.2%) cases. The sensitivity, specificity, positive predictive value, and negative predictive value, respectively, were 61, 62, 13, and 95. CONCLUSION: Katz et al. found a sensitivity, specificity, positive predictive value and negative predictive value of 80, 45, 18 and 94, respectively. The differences between these values and our own may be due to changes in the testing methodology and prevalence of CDI compared with a 1992 study population. The negative predictive value remains a strength. If this screening tool had been applied to our population, there may have been 128 (59.8%) fewer tests, but seven (38.9%) missed positive results. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6253395/ http://dx.doi.org/10.1093/ofid/ofy210.503 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Hawkins, Marten Baumgartner, Michael VanBeckum, Danielle Buck, Julia Holley, Crystal 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title | 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title_full | 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title_fullStr | 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title_full_unstemmed | 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title_short | 494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection |
title_sort | 494. use of a clinical prediction tool to predict clostridium difficile infection |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253395/ http://dx.doi.org/10.1093/ofid/ofy210.503 |
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