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...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/11/6/813 |
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| author | Itamar Poran Michal Elbaz Adi Turjeman Maayan Huberman Samuel Noa Eliakim-Raz Jeries Nashashibi Mical Paul Leonard Leibovici |
| author_facet | Itamar Poran Michal Elbaz Adi Turjeman Maayan Huberman Samuel Noa Eliakim-Raz Jeries Nashashibi Mical Paul Leonard Leibovici |
| author_sort | Itamar Poran |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T00:35:03Z |
| format | Article |
| id | doaj.art-128c7ff9d9964336932f83f3f0f51b32 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-10T00:35:03Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-128c7ff9d9964336932f83f3f0f51b322023-11-23T15:17:46ZengMDPI AGAntibiotics2079-63822022-06-0111681310.3390/antibiotics11060813Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation StudyItamar Poran0Michal Elbaz1Adi Turjeman2Maayan Huberman Samuel3Noa Eliakim-Raz4Jeries Nashashibi5Mical Paul6Leonard Leibovici7Department of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelDepartment of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelDepartment of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelDepartment of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelDepartment of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelDepartment of Medicine D, Rambam Health Care Campus, Haifa 3436212, IsraelInfectious Diseases Institute, Rambam Health Care Campus, Haifa 3436212, IsraelDepartment of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, IsraelBackground: 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.https://www.mdpi.com/2079-6382/11/6/813antibiotic prescriptionantibiotic stewardshipepidemiologyprediction model |
| spellingShingle | Itamar Poran Michal Elbaz Adi Turjeman Maayan Huberman Samuel Noa Eliakim-Raz Jeries Nashashibi Mical Paul Leonard Leibovici Predicting In-Hospital Antibiotic Use in the Medical Department: Derivation and Validation Study Antibiotics antibiotic prescription antibiotic stewardship epidemiology prediction model |
| 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 | antibiotic prescription antibiotic stewardship epidemiology prediction model |
| url | https://www.mdpi.com/2079-6382/11/6/813 |
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