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Related Experiment Video

Updated: Mar 24, 2026

Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles

Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles

Published on: May 24, 2024

Cultryx: Precision Diagnostic Stewardship for Blood Cultures Using Machine Learning.

Nicholas P Marshall¹, Wenyuan Chen², Fatemeh Amrollahi²

¹Division of Pediatric Infectious Diseases, Department of Pediatrics, School of Medicine, Stanford University, Palo Alto, California, USA.

Medrxiv : the Preprint Server for Health Sciences

|March 23, 2026

Summary

This summary is machine-generated.

A new machine learning model, Cultryx, effectively predicts bacteremia, outperforming traditional methods. This advancement can significantly reduce blood cultures and unnecessary antibiotic use, enhancing patient safety.

Keywords:

Antimicrobial Resistance and Stewardship Clinical Practice and Policy Infection Control and Hospital Epidemiology bacteremia prediction blood culture stewardship clinical decision support clinical informatics diagnostic stewardship electronic health record large language model machine learning resource conservation

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Area of Science:

Clinical diagnostics
Machine learning in healthcare
Infectious disease management

Background:

The 2024 blood culture bottle shortage highlighted issues in diagnostic resource allocation.
Persistent challenges exist with low-value testing and empiric treatment under clinical uncertainty.

Purpose of the Study:

To evaluate a machine learning (ML) approach using electronic medical record data for bacteremia prediction.
To determine if ML can guide diagnostic testing and empiric treatment more effectively than current practices.

Main Methods:

A retrospective cohort of 101,812 adult emergency department encounters (2015-2025) was analyzed.
An XGBoost model (Cultryx) was trained to predict bacteremia.
Performance was benchmarked against clinical heuristics (SIRS, Shapiro Rule) and an idealized cognitive baseline (physicians and GPT-5 using Fabre framework).

Main Results:

Cultryx (AUROC 0.810) outperformed clinical heuristics; SIRS lacked specificity (41.2%), Shapiro Rule lacked sensitivity (70.2%).
Physicians achieved 95.7% sensitivity with the Fabre framework, while GPT-5 achieved 71.6%.
Cultryx achieved a 26.2% culture volume deferral rate (deferring ~15,872 bottles) with 98.9% negative predictive value, and Cultryx score retained a 20.8% deferral rate.

Conclusions:

Machine learning offers a data-driven alternative to clinical heuristics for bacteremia prediction.
Cultryx can conserve diagnostic resources and reduce unnecessary antibiotic exposure by maximizing culture deferment.
This approach enhances patient safety by improving pathogen detection and reducing empiric antibiotic use.