ArticleHospital pediatrics2026
Understanding Pediatric Clinical Deterioration Through Rapid Response System Trigger Clusters.
Article in Hospital pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesMedical emergency teams (METs) are activated in response to signs and symptoms, or triggers, of clinical deterioration in acute care settings. However, the patterns in which triggers manifest and impact outcomes are poorly understood. We identified and described the patterns in which multiple triggers cluster to activate pediatric METs and examined the associations between these clusters and outcomes.
methodsPediatric MET events from January 2015 to December 2019 in the Get With The Guidelines®-Resuscitation national registry focused on METs (N = 4289) were grouped into MET trigger clusters using cluster analyses based on triggers used to activate the MET. Differences in patient characteristics across MET trigger clusters were compared using Pearson χ2 and analysis of variance (ANOVA) tests. Hierarchical logistic regressions tested associations between trigger clusters and outcomes.
resultsA total of 4 MET trigger clusters were identified. The triggers that predominantly defined each cluster were as follows: Cluster 1, decreased oxygen saturation and mental status changes; Cluster 2, tachypnea, tachycardia, and staff concern; Cluster 3, new onset difficulty in breathing and staff concern; and Cluster 4, the reference cluster, tachypnea, new onset difficulty in breathing, and decreased oxygen saturation. Patients in Cluster 1 were more likely to experience acute respiratory compromise (need for emergent assisted ventilation), and patients in Clusters 1 and 3 were more likely to be transferred to critical care.
conclusionsA total of 4 MET trigger clusters were identified and have varying associations with outcomes. MET trigger clusters could guide bedside care and triage in clinical emergencies and help develop more accurate predictive models for detecting clinical deterioration.
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