โก Quick Summary
This study developed a machine learning model to predict positive blood cultures in febrile ICU patients using only vital signs. The model demonstrated a moderate predictive performance with an area under the receiver operating characteristic curve (AUC) of 0.700 for internal validation and 0.679 for external validation.
๐ Key Details
- ๐ Dataset: 597 blood cultures for model development, 366 for external validation
- ๐งฉ Features used: Body temperature, heart rate, mean arterial pressure
- โ๏ธ Technology: Balanced Random Forest model
- ๐ Performance: AUC 0.700 ยฑ 0.072 (internal), AUC 0.679 ยฑ 0.010 (external)
๐ Key Takeaways
- ๐ก Vital signs can be used to predict bloodstream infections, potentially speeding up diagnosis.
- ๐ค Machine learning offers a novel approach to clinical decision-making in ICU settings.
- ๐ Predictive performance was moderate, indicating room for improvement.
- ๐ก๏ธ Temperature range was a significant predictor of positive blood cultures.
- ๐ฉบ Continuous monitoring can aid in timely interventions for patients.
- ๐ฅ Study conducted at two university hospitals.
- ๐ SHapley Additive exPlanations analysis provided insights into feature importance.
๐ Background
Bloodstream infections are a critical concern in intensive care units (ICUs), where timely diagnosis and treatment are essential to reduce mortality rates. Traditional predictive models often rely on laboratory test results, which may not be immediately available. This study addresses the need for a more accessible predictive tool by utilizing vital signs alone.
๐๏ธ Study
The research involved clinical data from ICU patients at two university hospitals, focusing on those with a maximum body temperature of โฅ 38 ยฐC. The study aimed to develop a machine learning model that could effectively predict positive blood cultures based solely on vital signs collected over three days.
๐ Results
The Balanced Random Forest model achieved an AUC of 0.700 ยฑ 0.072 during internal validation and 0.679 ยฑ 0.010 during external validation. The analysis revealed that a larger temperature range was associated with positive predictions, while stable blood pressure and decreased heart rate were linked to negative predictions.
๐ Impact and Implications
This study’s findings highlight the potential of using machine learning to enhance clinical decision-making in ICUs. By continuously monitoring vital signs, healthcare providers can better assess the need for blood cultures, ultimately leading to quicker interventions and improved patient outcomes. The model serves as an adjunctive tool, paving the way for more efficient management of bloodstream infections.
๐ฎ Conclusion
The development of this machine learning model represents a significant step forward in predicting bloodstream infections using vital signs alone. While the predictive performance is moderate, the implications for clinical practice are profound. Further research and refinement of the model could enhance its accuracy and utility in real-world settings, making it a valuable asset in critical care.
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Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.
Abstract
Bloodstream infections require timely and appropriate diagnosis and treatment, as inadequate management is associated with higher mortality. Previous predictive models for bloodstream infection have generally incorporated laboratory test results in addition to vital signs, although laboratory results are not always immediately available in clinical practice. This study aimed to develop and validate a machine learning model to predict positive blood cultures using only vital signs in febrile intensive care unit (ICU) patients (maximum BTโโฅโ38ย ยฐC). Clinical data from ICU patients at two university hospitals were included, with 597 blood cultures used for model development and 366 for external validation. Six explanatory variables derived from body temperature, heart rate, and mean arterial pressure measured over three days were used to construct a Balanced Random Forest model. The area under the receiver operating characteristic curve was 0.700โยฑโ0.072 for internal validation and 0.679โยฑโ0.010 for external validation. SHapley Additive exPlanations value analysis indicated that a larger temperature range contributed to positive predictions, while no increase in temperature, increases in blood pressure, and a decrease in heart rate contributed to negative predictions. The model demonstrates moderate predictive performance and shows comparable results across different datasets. It can continuously monitor the need for blood cultures, thereby serving as an adjunctive tool to support clinical decision-making.
Author: [‘Seike I’, ‘Baba H’, ‘Sonobe S’, ‘Iwazaki J’, ‘Miyauchi M’, ‘Kobayashi T’, ‘Takaya E’, ‘Ikumi S’, ‘Miyamoto T’, ‘Kawamoto S’, ‘Oyama S’, ‘Imaizumi T’, ‘Morohashi A’, ‘Oshima K’, ‘Nakayama A’, ‘Aoyagi T’]
Journal: Sci Rep
Citation: Seike I, et al. Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients. Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients. 2026; (unknown volume):(unknown pages). doi: 10.1038/s41598-026-50400-w