Predictors of In-Hospital Mortality After Rapid Response Team Calls in a 274 Hospital Nationwide Sample

Abstract

Objectives: Despite wide adoption of rapid response teams across the United States, predictors of in-hospital mortality for patients receiving rapid response team calls are poorly characterized. Identification of patients at high risk of death during hospitalization could improve triage to intensive care units and prompt timely reevaluations of goals of care. We sought to identify predictors of in-hospital mortality in patients who are subjects of rapid response team calls and to develop and validate a predictive model for death after rapid response team call.

Design: Analysis of data from the national Get with the Guidelines-Medical Emergency Team event registry.

Setting: Two-hundred seventy four hospitals participating in Get with the Guidelines-Medical Emergency Team from June 2005 to February 2015.

Patients: 282,710 hospitalized adults on surgical or medical wards who were subjects of a rapid response team call.

Interventions: None.

Measurements and main results: The primary outcome was death during hospitalization; candidate predictors included patient demographic- and event-level characteristics. Patients who died after rapid response team were older (median age 72 vs 66 yr), were more likely to be admitted for noncardiac medical illness (70% vs 58%), and had greater median length of stay prior to rapid response team (81 vs 47 hr) (p < 0.001 for all comparisons). The prediction model had an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.78-0.79), with systolic blood pressure, time since admission, and respiratory rate being the most important variables.

Conclusions: Patients who die following rapid response team calls differ significantly from surviving peers. Recognition of these factors could improve postrapid response team triage decisions and prompt timely goals of care discussions.

Figure 1

In-hospital mortality by trigger type and number of triggers for Rapid Response Team calls. ^aIncludes respiratory depression, tachypnea, difficulty breathing, and hypoxia. ^bIncludes bradycardia, tachycardia, hypotension, and chest pain. ^cIncludes mental status change, agitation or delirium, loss of consciousness, seizure, and suspected stroke

Figure 2

Importance of the predictor variables in the gradient boosted machine model, scaled to a maximum of 100

Figure 3

Area under the receiver operator characteristic curves (AUCs) of the gradient boosted machine algorithm by institution type and in all hospitals (solid vertical line) in the validation cohorts.