Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits.
Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-11-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000306&type=printable |
| _version_ | 1797633975239311360 |
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| author | Ghadeer O Ghosheh Terrence Lee St John Pengyu Wang Vee Nis Ling Lelan R Orquiola Nasir Hayat Farah E Shamout Y Zaki Almallah |
| author_facet | Ghadeer O Ghosheh Terrence Lee St John Pengyu Wang Vee Nis Ling Lelan R Orquiola Nasir Hayat Farah E Shamout Y Zaki Almallah |
| author_sort | Ghadeer O Ghosheh |
| collection | DOAJ |
| description | Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings. |
| first_indexed | 2024-03-11T12:01:15Z |
| format | Article |
| id | doaj.art-fb681b2a512649b0bf6b2c90da113051 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-11T12:01:15Z |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-fb681b2a512649b0bf6b2c90da1130512023-11-08T05:33:42ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-11-01211e000030610.1371/journal.pdig.0000306Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits.Ghadeer O GhoshehTerrence Lee St JohnPengyu WangVee Nis LingLelan R OrquiolaNasir HayatFarah E ShamoutY Zaki AlmallahUrine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000306&type=printable |
| spellingShingle | Ghadeer O Ghosheh Terrence Lee St John Pengyu Wang Vee Nis Ling Lelan R Orquiola Nasir Hayat Farah E Shamout Y Zaki Almallah Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. PLOS Digital Health |
| title | Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. |
| title_full | Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. |
| title_fullStr | Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. |
| title_full_unstemmed | Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. |
| title_short | Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits. |
| title_sort | development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000306&type=printable |
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