Dynamic and Personalized Risk Forecast in Step-Down Units: Implications for Monitoring Paradigms

Abstract

Rationale: Cardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored.

Objectives: To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual Step-Down Unit patients.

Methods: Using a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical-trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously-recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after stepdown unit admission, and again during the 4-hours immediately before the CRI event, between cases (ever had a CRI) and controls (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk.

Measurements and main results: Estimated risk was significantly higher for cases (918) than controls (1,053; p=<0.001) during the initial 4-hour stable periods. Among cases, the aggregated non-personalized risk trend increased 2 hours before the CRI, while the personalized risk trend became significantly different from controls 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for non-personalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset). Conclusions: Insights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful the dynamic allocation of technologic and clinical personnel resources in acute care hospitals.