Critical Escalation of Rapid Response Team Activation Varies by Diagnosis

Introduction: Rapid Response Team (RRT) activations are associated with significant morbidity and mortality. In this study, we investigated RRT activations to determine which diagnoses and clinical factors were associated with deterioration and may provide a means for early intervention.


Methods: Data for all RRTs at Stanford Hospital from 3/30/23 to 2/26/25 was collected. “Critical escalation” was defined as an RRT end disposition in the ICU, operating room, or death. Diagnoses and resultant critical escalation were analyzed via chi square test. Diagnoses with high residuals were identified as “high-risk.” Within these diagnoses, logistic regression was performed on pertinent clinical variables within 24 hours leading up to RRT to identify association with escalation.


Results: Of the 2,426 RRT activations evaluated, the most common RRT coded diagnoses were hypotension (n=625), respiratory failure (n=507), arrhythmias (n=452), altered level of consciousness (n=243), syncope (n=132), seizure (n=100), bleeding (n=88), chest pain (n=32), fall (n=29), hypertension (n=11), and hypoglycemia (n=10). There were 728 critical escalations (30%). Critical escalation rates differed by diagnosis (p < 0.05). Diagnoses with the highest rates of escalation were bleeding (55.7%), hypotension (45%), and respiratory failure (42%). The diagnoses with the lowest rates of escalation were arrythmias (13%), hypertension (9%), syncope (2%), fall (0%) and hypoglycemia (0%). In the case of bleeding, hemoglobin (p=0.2), MAP (p=0.06), and heart rate (p=0.5) were not significantly associated with critical escalation, though this analysis may be underpowered. In the case of hypotension, MAP (p=0.034) and heart rate (p=0.008) were associated with escalation. For acute respiratory failure, respiratory rate (p=0.019), but not SpO2 (p=0.4), was associated with escalation.


Conclusions: Rapid response activations are common (>3 per day) with approximately 1 critical escalation per day. In our large, contemporary cohort, we found significant differences in rates of escalation based on diagnosis, which can be further refined by inclusion of clinical metrics. Given ICU congestion, these results could facilitate the development of prediction models to facilitate earlier intervention in high-risk events.