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Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for
Insights
A new artificial intelligence (AI) model, SEPD, effectively predicts critical sepsis in pediatric emergency department (ED) patients. This AI tool enhances early detection and treatment for critically ill children, outperforming existing sepsis prediction models.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Informatics
- Artificial Intelligence in Healthcare
Background:
- Pediatric sepsis is a leading cause of mortality and morbidity, with many hospitals facing challenges in timely identification and treatment.
- Effective sepsis management in pediatric emergency departments (EDs) is crucial for improving patient outcomes.
Purpose of the Study:
- To develop and validate a local artificial intelligence (AI) model, termed "Sepsis on ED to PICU Disposition" (SEPD), for predicting critical sepsis in pediatric ED patients.
- To evaluate the SEPD model's performance against a vendor-developed sepsis model.
Main Methods:
- A retrospective observational study was conducted using data from a quaternary-care, academic healthcare system.
- The SEPD model was developed and validated on a dataset of 5,534 pediatric ED patient encounters, with an additional 1,058 encounters used for silent implementation validation.
- Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUROC) and precision-recall metrics.
Main Results:
- The SEPD model achieved an AUROC of 81.8%, significantly outperforming a vendor-developed model (57.5%).
- The SEPD model demonstrated superior precision-recall performance, indicating a more balanced identification of true positive cases.
- During silent implementation, the SEPD model maintained high sensitivity (85.29%) and specificity (60.45%).
Conclusions:
- The SEPD AI model effectively enhances the detection of critical sepsis in high-risk pediatric ED patients with a known Pediatric Intensive Care Unit (PICU) disposition.
- This AI tool shows promise for facilitating timely sepsis identification and treatment within complex ED workflows, potentially improving outcomes for critically ill children.
Importance:
Pediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to promptly identify and treat sepsis. This study demonstrates the feasibility of a multi-tiered artificial intelligence (AI) to enhance sepsis clinical decision-making within a complex emergency department (ED) workflow.
Objectives:
To develop and validate a local AI model predicting critical sepsis among ED patients who received a fluid bolus and a disposition to the Pediatric Intensive Care Unit (PICU) but had not yet received antibiotics.
Design:
Retrospective observational cross-section study.
Setting:
Urban, quaternary-care, academic healthcare system.
Patients:
Pediatric ED patients.
Interventions:
None.
Measures And Main Results:
The "Sepsis on ED to PICU Disposition" (SEPD) model aimed to predict critical sepsis within 72 hours of PICU disposition using a dataset totaling 5,534 patient encounters for model training and testing. During silent implementation, 1,058 encounters were used for validation. The SEPD model outperformed a vendor-developed sepsis model with an AUROC of 81.8%, compared to 57.5%. The model also demonstrated better precision-recall performance, showing more balanced identification of true positives. During silent implementation, the SEPD model maintained similar sensitivity (85.29%) and specificity (60.45%) to those observed during model testing.
Conclusion:
The SEPD model improved detection of critical sepsis among high-risk pediatric ED patients with a known PICU disposition, outperforming a vendor-developed sepsis model. Within a complex ED workflow, this model may facilitate timely sepsis identification and treatment in critically ill patients, who may have been missed during earlier stages of their ED course.
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