Predicting pathogen-positive blood culture episodes at two hours using electronic health record data
Most blood cultures obtained early in hospitalization do not yield a pathogen. We evaluated prediction 2 h after the first blood-culture record using MIMIC-IV. Adults with an initial routine culture record within 24 h of hospital presentation were eligible if alive and still receiving care at prediction. The cohort included 145,914 encounters, 99.3% eventually admitted, and 6,404 pathogen-positive episodes (4.39%). Comparisons at 2, 4 and 6 h supported the earliest tested prediction time because later gains were small and uncertain. The primary XGBoost model was evaluated in 11,523 test encounters; 242 episodes were positive (2.10%). AUROC was 0.819 (95% CI 0.792–0.844), average precision 0.124 (0.096–0.167), and Brier score 0.0201. The top 5% of predicted risk contained 81 positives among 577 encounters, giving 14.0% positivity and 6.68-fold enrichment. Laboratory values, particularly lymphocyte percentage, contributed most to prediction. Secondary analyses assessed the added value of previous culture results. Mean predicted risk was 3.78%, compared with 2.10% observed. This 2-h model was able to concentrate pathogen-positive episodes in higher-risk groups, but calibration and transportability require further validation before clinical use.
Authors
- Radu C. Grovu (ORCID: https://orcid.org/0000-0002-7142-1027)
- Sean F. Monaghan (ORCID: https://orcid.org/0000-0003-1490-3043)
- Hala Soomro
- Michael Rossi (ORCID: https://orcid.org/0009-0001-2316-8952)
- Nathan Gelinas
Institutions
- Providence Health Care (CA)
- Brown University (US)
- Rhode Island Hospital (US)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-10-15
- DOI
- https://doi.org/10.1038/s41598-026-75707-6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator
- National Institutes of Health