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The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy
BACKGROUND: Selecting an empiric antimicrobial regimen can be difficult for early learners and misuse of antibiotics can lead to adverse events and antimicrobial resistance. There have been few interventions that have focused on improving antibiotic decision making, as a form of therapeutic reasonin...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163704/ https://www.ncbi.nlm.nih.gov/pubmed/37149569 http://dx.doi.org/10.1186/s12909-023-04286-1 |
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author | Davar, Kusha Vijayan, Tara |
author_facet | Davar, Kusha Vijayan, Tara |
author_sort | Davar, Kusha |
collection | PubMed |
description | BACKGROUND: Selecting an empiric antimicrobial regimen can be difficult for early learners and misuse of antibiotics can lead to adverse events and antimicrobial resistance. There have been few interventions that have focused on improving antibiotic decision making, as a form of therapeutic reasoning, for post-graduate trainees. We describe here an approach to aid internal medicine interns in their therapeutic reasoning, particularly when it comes to diagnosing and empirically treating infections. METHODS: The PEST (pathology, epidemiology, severity, treatment) model was created as a four-step approach to therapeutic reasoning and choosing an appropriate antimicrobial regimen for a given infectious disease syndrome. In February 2020, we conducted two independent teaching sessions for interns on the PEST approach. We assessed pre-and post-teaching responses to five clinical vignette-based questions. Results were presented as a percentage of interns who chose an appropriate antibiotic and provided sufficient therapeutic reasoning as defined by using at least three out of the four PEST criteria. Statistical analysis occurred via Fischer’s exact test to determine the level of statistical significance between responses. RESULTS: Twenty-seven interns participated in the activity. At baseline, several interns had incorporated aspects of the PEST approach in their pre-teaching responses. Ten interns commented on the usefulness of such a systematic approach. While there was no statistically significant difference in antibiotic selection, the teaching session demonstrated a trend towards significance in improving therapeutic reasoning as defined by the PEST strategy. CONCLUSION: Our results suggested an improvement in using a structured cognitive tool such as the PEST approach to reinforce therapeutic reasoning, but the method did little to improve antibiotic selection. Some interns used select “PEST” concepts prior to the intervention suggesting that the PEST approach may enhance prior knowledge or clinical reasoning skills. Continued incorporation of the PEST approach using a case-based framework may solidify conceptual and practical knowledge of antimicrobial selection. Further studies are needed to assess the impact of such teaching interventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04286-1. |
format | Online Article Text |
id | pubmed-10163704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101637042023-05-07 The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy Davar, Kusha Vijayan, Tara BMC Med Educ Research Article BACKGROUND: Selecting an empiric antimicrobial regimen can be difficult for early learners and misuse of antibiotics can lead to adverse events and antimicrobial resistance. There have been few interventions that have focused on improving antibiotic decision making, as a form of therapeutic reasoning, for post-graduate trainees. We describe here an approach to aid internal medicine interns in their therapeutic reasoning, particularly when it comes to diagnosing and empirically treating infections. METHODS: The PEST (pathology, epidemiology, severity, treatment) model was created as a four-step approach to therapeutic reasoning and choosing an appropriate antimicrobial regimen for a given infectious disease syndrome. In February 2020, we conducted two independent teaching sessions for interns on the PEST approach. We assessed pre-and post-teaching responses to five clinical vignette-based questions. Results were presented as a percentage of interns who chose an appropriate antibiotic and provided sufficient therapeutic reasoning as defined by using at least three out of the four PEST criteria. Statistical analysis occurred via Fischer’s exact test to determine the level of statistical significance between responses. RESULTS: Twenty-seven interns participated in the activity. At baseline, several interns had incorporated aspects of the PEST approach in their pre-teaching responses. Ten interns commented on the usefulness of such a systematic approach. While there was no statistically significant difference in antibiotic selection, the teaching session demonstrated a trend towards significance in improving therapeutic reasoning as defined by the PEST strategy. CONCLUSION: Our results suggested an improvement in using a structured cognitive tool such as the PEST approach to reinforce therapeutic reasoning, but the method did little to improve antibiotic selection. Some interns used select “PEST” concepts prior to the intervention suggesting that the PEST approach may enhance prior knowledge or clinical reasoning skills. Continued incorporation of the PEST approach using a case-based framework may solidify conceptual and practical knowledge of antimicrobial selection. Further studies are needed to assess the impact of such teaching interventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04286-1. BioMed Central 2023-05-06 /pmc/articles/PMC10163704/ /pubmed/37149569 http://dx.doi.org/10.1186/s12909-023-04286-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Davar, Kusha Vijayan, Tara The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title | The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title_full | The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title_fullStr | The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title_full_unstemmed | The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title_short | The PEST (Pathology, Epidemiology, Severity, Treatment) approach to optimizing antimicrobial therapy |
title_sort | pest (pathology, epidemiology, severity, treatment) approach to optimizing antimicrobial therapy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163704/ https://www.ncbi.nlm.nih.gov/pubmed/37149569 http://dx.doi.org/10.1186/s12909-023-04286-1 |
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