Key result
Detecting a ~5-point reduction in missed ACS via AI triage requires 54 emergency department clusters.
Why the study?
AI triage models for ACS are typically trained on a single troponin assay platform, yet EDs deploy heterogeneous high-sensitivity and point-of-care assays with different limits of detection and calibration.
What are the operating characteristics and sample size requirements of a calibration-transfer evaluation design for artificial intelligence rule-out safety in acute coronary syndrome across emergency departments with heterogeneous troponin assay platforms?
What are the operating characteristics and sample size requirements of a calibration-transfer evaluation design for artificial intelligence rule-out safety in acute coronary syndrome across emergency departments with heterogeneous troponin assay platforms?
This simulation study establishes that evaluating the rule-out safety of AI triage models for acute coronary syndrome across emergency departments with different troponin assays requires large-scale multi-site studies (at least 54 clusters for 80% power to detect a 5-percentage-point reduction in missed ACS) that explicitly model between-cluster variance.
Large multi-site trials (≥54 clusters) are required to certify AI rule-out safety across troponin assays; leaves open safe multi-platform deployment pending such evaluation.
Artificial intelligence (AI) triage models for acute coronary syndrome (ACS) are typically trained on a single troponin assay platform, yet emergency departments (EDs) deploy heterogeneous high-sensitivity and point-of-care assays with different limits of detection and calibration. the present analysis characterises the operating characteristics of a calibration-transfer evaluation design for AI rule-out safety under assay-platform heterogeneity, using a replication-based before-after-control-impact (BACI) framework with a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point reduction in missed ACS at 80% power under the declared variance structure, and that power falls steeply below this threshold. The contribution is a design-level specification of the evidence required before AI triage models can be certified as platform-agnostic, together with a formal statement of the calibration-transfer estimand and its identification conditions. The framework reframes assay heterogeneity from a nuisance to be averaged over into a structured source of between-cluster variance that must be explicitly modelled in any multi-site evaluation of rule-out safety.
No takes yet. Share an insight, caveat, or question.
Mahlangu et al. (2026) studied Acute coronary syndrome. AI triage models for acute coronary syndrome under variable troponin assay platforms vs. Control period/sites in a before-after-control-impact (BACI) evaluation framework was evaluated on Statistical power to detect a reduction in missed acute coronary syndrome (rule-out safety). AI triage calibration-transfer evaluation requires 54 emergency department clusters to detect a 5-percentage-point reduction in missed ACS at 80% power (intraclass correlation 0.64).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: