Temporal distribution of instability events in continuously monitored step-down unit patients: Implications for Rapid Response Systems

Abstract

AIM Medical Emergency Teams (MET) activations are more frequent during daytime and weekdays, but whether due to greater patient instability, proximity from admission time, or caregiver concentration is unclear. We sought to determine if instability events, when they occurred, varied in their temporal distribution. METHODS Monitoring data were recorded (frequency 1/20Hz) in 634 SDU patients (41,635 monitoring hours). Vital sign excursion beyond our MET trigger thresholds defined alerts. The resultant 1,399 alerts from 216 patients were tallied according to clock hour and time elapsed since admission. We fit patient ID (n=216), clock hour, time since SDU admission, and alert present into a null model and three mixed effect logistic regression models: clock hour, hours elapsed since admission, and both clock hour and time elapsed since admission as fixed effect covariates. We performed likelihood ratio tests on these models to assess if, among all alerts, there were proportionally more alerts for any given clock hour, or proximity to admission time. RESULTS Only time elapsed since admission (p<0.001), and not clock hour adjusting for time elapsed since admission (p=0.885), was significant for temporal disproportion. Results were unchanged if the first 24 hours following admission were excluded from the models. CONCLUSION Although instability alerts are distributed most frequently within 24 hours after SDU admission in unstable patients, they are otherwise not more likely to distribute proportionally more frequently during certain clock hours. If MET utilization peaks do not coincide with admission time peaks, other variables contributing to unrecognized instability should be explored.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

“…The same group also published a study utilizing electronic alerts with automated pages going to nurses for patients showing signs of clinical deterioration on the wards (29). Similar studies have been published in step-down and ICU patients (30,31), and these real-time alerts may also help bridge the gap between when rapid response teams are typically alerted and when instability events actually occur (32). Our results suggest that utilizing the random forest model has the potential to markedly decrease false alarms compared to logistic regression.…”

Predicting adverse hemodynamic events in critically ill patients

“…Newer data-driven analytic techniques using machine learning algorithms can be employed for this to address these complex and often subtle confounders, by discovering numerous untapped hidden signals and patterns once used to be thought of as noise. Recent literature suggests that a data-driven approach based on machine learning algorithms could reveal telltale signs of impending instability **(7–9). The inherent heterogeneity and complexity of ICU data can be addressed by acquiring multivariate, high frequency, multi-source data as hemodynamic changes display a time-sensitive nature and nonlinearity.…”

Risk for Cardiorespiratory Instability Following Transfer to a Monitored Step-Down Unit

“…[9][10][11][12] We have previously reported that CRI, when it occurs, is most likely to manifest in the hours closest to SDU admission. 13 However, there is limited guidance on what changes most commonly serve as the initial vital sign change (driver) preceding CRI or patient characteristics that confer added risk. Therefore, the purpose of this study was to describe admission characteristics, CRI drivers (defined as the first vital sign to cross threshold), and time to onset of initial CRI events in monitored SDU subjects.…”