Cultryx: Precision Diagnostic Stewardship for Blood Cultures Using Machine Learning
Cultryx, a machine learning model for blood culture stewardship, outperforms standard clinical heuristics in predicting bacteremia. This approach could reduce culture utilization by over 26% while preserving pathogen detection, conserving diagnostic resources, reducing unnecessary antibiotic exposure, and elevating patient safety.
Authors
- Kameron Collin Black (ORCID: https://orcid.org/0000-0002-3696-4050)
- P. Stephen (ORCID: https://orcid.org/0000-0003-3738-9569)
- Fatemeh Amrollahi
- Fateme Nateghi Haredasht (ORCID: https://orcid.org/0000-0002-8874-8835)
- Aydin Zahedivash (ORCID: https://orcid.org/0000-0001-6835-1139)
- Niaz Banaei (ORCID: https://orcid.org/0000-0001-8501-3000)
- Amy Chang
- Stanley C. Deresinski
- Manoj Maddali
- Steven Asch
- Wenyuan Chen
- Mary Kane Goldstein
- Jonathan H Chen
- Nicholas P Marshall
Institutions
- Palo Alto University (US)
- Stanford Health Care (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-03-04
- DOI
- https://doi.org/10.64898/2026.02.27.26347214
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- Stanford Bio-X
- National Institute of Allergy and Infectious Diseases
- National Center for Advancing Translational Sciences
- Stanford Maternal and Child Health Research Institute