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Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study
Background: The rise of multi-drug-resistant pathogens and nosocomial infections among hospitalized patients is partially attributed to the increased use of antibiotic therapy. A prediction model for in-hospital antibiotic treatment could be valuable to target preventive strategies. Methods: This wa...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219723/ https://www.ncbi.nlm.nih.gov/pubmed/35740219 http://dx.doi.org/10.3390/antibiotics11060813 |
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author | Poran, Itamar Elbaz, Michal Turjeman, Adi Huberman Samuel, Maayan Eliakim-Raz, Noa Nashashibi, Jeries Paul, Mical Leibovici, Leonard |
author_facet | Poran, Itamar Elbaz, Michal Turjeman, Adi Huberman Samuel, Maayan Eliakim-Raz, Noa Nashashibi, Jeries Paul, Mical Leibovici, Leonard |
author_sort | Poran, Itamar |
collection | PubMed |
description | Background: The rise of multi-drug-resistant pathogens and nosocomial infections among hospitalized patients is partially attributed to the increased use of antibiotic therapy. A prediction model for in-hospital antibiotic treatment could be valuable to target preventive strategies. Methods: This was a retrospective cohort study, including patients admitted in 2018 to medical departments and not treated with antibiotics during the first 48 h. Data available at hospital admission were used to develop a logistic model to predict the probability of antibiotic treatment during hospitalization. The performance of the model was evaluated in two independent validation cohorts. Results: In the derivation cohort, antibiotic treatment was initiated in 454 (8.1%) out of 5592 included patients. Male gender, lower functional capacity, prophylactic antibiotic treatment, medical history of atrial fibrillation, peripheral vascular disease, solid organ transplantation, chronic use of a central venous catheter, urinary catheter and nasogastric tube, albumin level, mental status and vital signs at presentation were identified as predictors for antibiotic use during hospitalization and were included in the prediction model. The area under the ROC curve (AUROC) was 0.72 (95% CI 0.70–0.75). In the highest probability group, the percentage of antibiotic treatment was 18.2% (238/1,307). In the validation cohorts, the AUROC was 0.73 (95% CI 0.68–0.77) and 0.75 (95% CI 0.72–0.78). In the highest probability group, the percentage of antibiotic treatment was 12.5% (66/526) and 20.7% (244/1179) of patients. Conclusions: Our prediction model performed well in the validation cohorts and was able to identify a subgroup of patients at high risk for antibiotic treatment. |
format | Online Article Text |
id | pubmed-9219723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92197232022-06-24 Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study Poran, Itamar Elbaz, Michal Turjeman, Adi Huberman Samuel, Maayan Eliakim-Raz, Noa Nashashibi, Jeries Paul, Mical Leibovici, Leonard Antibiotics (Basel) Article Background: The rise of multi-drug-resistant pathogens and nosocomial infections among hospitalized patients is partially attributed to the increased use of antibiotic therapy. A prediction model for in-hospital antibiotic treatment could be valuable to target preventive strategies. Methods: This was a retrospective cohort study, including patients admitted in 2018 to medical departments and not treated with antibiotics during the first 48 h. Data available at hospital admission were used to develop a logistic model to predict the probability of antibiotic treatment during hospitalization. The performance of the model was evaluated in two independent validation cohorts. Results: In the derivation cohort, antibiotic treatment was initiated in 454 (8.1%) out of 5592 included patients. Male gender, lower functional capacity, prophylactic antibiotic treatment, medical history of atrial fibrillation, peripheral vascular disease, solid organ transplantation, chronic use of a central venous catheter, urinary catheter and nasogastric tube, albumin level, mental status and vital signs at presentation were identified as predictors for antibiotic use during hospitalization and were included in the prediction model. The area under the ROC curve (AUROC) was 0.72 (95% CI 0.70–0.75). In the highest probability group, the percentage of antibiotic treatment was 18.2% (238/1,307). In the validation cohorts, the AUROC was 0.73 (95% CI 0.68–0.77) and 0.75 (95% CI 0.72–0.78). In the highest probability group, the percentage of antibiotic treatment was 12.5% (66/526) and 20.7% (244/1179) of patients. Conclusions: Our prediction model performed well in the validation cohorts and was able to identify a subgroup of patients at high risk for antibiotic treatment. MDPI 2022-06-16 /pmc/articles/PMC9219723/ /pubmed/35740219 http://dx.doi.org/10.3390/antibiotics11060813 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Poran, Itamar Elbaz, Michal Turjeman, Adi Huberman Samuel, Maayan Eliakim-Raz, Noa Nashashibi, Jeries Paul, Mical Leibovici, Leonard Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title | Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title_full | Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title_fullStr | Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title_full_unstemmed | Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title_short | Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study |
title_sort | predicting in-hospital antibiotic use in the medical department: derivation and validation study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219723/ https://www.ncbi.nlm.nih.gov/pubmed/35740219 http://dx.doi.org/10.3390/antibiotics11060813 |
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