Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI's efficacy as a clinical decision support system. The findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes. Author summary Recognizing patient deterioration remains challenging even for experienced clinicians and nurses. RRTs can help mobilize resources to respond to patients earlier. However, determining when to activate RRTs is difficult. We retrospectively evaluated a software as a medical device, AHI-PI, compared the vital signs of heart rate, blood pressure, and respiratory rate to understand if AHI-PI could provide an earlier indicator of patient deterioration than vital signs. Our findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events, before vital sign changes occur. This could potentially help clinicians recognize patients at risk for clinical deterioration and improve clinical outcomes through early targeted therapy or interventions.