Key result
A clinical decision support model for emergency department ECG screening demonstrated 95.5% (95% CI, 95.2%-95.9%) raw agreement with in vitro screening decisions across 18,335 encounters.
Why the study?
Integrating predictive models into live clinical care requires scientific testing prior to implementation to ensure patient safety.
Does an AI predictive model implemented as clinical decision support accurately replicate in vitro screening decisions for acute coronary syndrome in the Emergency Department?
Observational (n=18,335)
Does an AI predictive model implemented as clinical decision support accurately replicate in vitro screening decisions for acute coronary syndrome in the Emergency Department?
A structured framework for translating an AI predictive model into clinical decision support demonstrated high fidelity (95.5% agreement) for identifying patients needing an ECG for acute coronary syndrome screening.
OBJECTIVE: The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We built and technically implemented a model that predicts which patients require an electrocardiogram (ECG) to screen for heart attacks within 10 minutes of their arrival to the Emergency Department. We developed a structured framework for the in vitro to in vivo translation of the model through implementation as clinical decision support (CDS). MATERIALS AND METHODS: The CDS ran as a silent pilot for 2 months. We conducted (1) a Technical Component Analysis to ensure each part of the CDS coding functioned as planned, and (2) a Technical Fidelity Analysis to ensure agreement between the CDS's in vivo and the model's in vitro screening decisions. RESULTS: The Technical Component Analysis indicated several small coding errors in CDS components that were addressed. During this period, the CDS processed 18 335 patient encounters. CDS fidelity to the model reflected raw agreement of 95.5% (CI, 95.2%-95.9%) and kappa of 87.6% (CI, 86.7%-88.6%). Additional coding errors were identified and were corrected. DISCUSSION: Our structured framework for the in vitro to in vivo translation of our predictive model uncovered ways to improve performance in vivo and the validity of risk assessment decisions. Testing predictive models on live care data and accompanying analyses is necessary to safely implement a predictive model for clinical use. CONCLUSION: We developed a method for the translation of our model from in vitro to in vivo that can be utilized with other applications of predictive modeling in healthcare.
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Bunney et al. (2025) conducted an observational in Acute coronary syndrome (n=18,335). Clinical decision support (CDS) predictive model vs. In vitro model screening decisions was evaluated on Agreement between the CDS's in vivo and the model's in vitro screening decisions (kappa 87.6%, 95% CI 86.7%-88.6%). A clinical decision support model for emergency department ECG screening demonstrated 95.5% (95% CI, 95.2%-95.9%) raw agreement with in vitro screening decisions across 18,335 encounters.
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