BACKGROUND: With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards. OBJECTIVE: To prospectively validate a predictive algorithm for clinical deterioration in general-medical ward patients, and to conduct a trial of real-time alerts based on this algorithm. DESIGN: Randomized, controlled crossover study. SETTING/PATIENTS: Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011. INTERVENTIONS: Real-time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards. MEASUREMENTS: Intensive care unit transfer, hospital mortality, and hospital length of stay. RESULTS: Patients meeting the alert threshold were at nearly 5.3-fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6-6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9-fold greater risk of death (95% CI: 7.4-10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group. CONCLUSIONS: Real-time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes.
RCT Entities:
BACKGROUND: With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards. OBJECTIVE: To prospectively validate a predictive algorithm for clinical deterioration in general-medical ward patients, and to conduct a trial of real-time alerts based on this algorithm. DESIGN: Randomized, controlled crossover study. SETTING/PATIENTS: Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011. INTERVENTIONS: Real-time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards. MEASUREMENTS: Intensive care unit transfer, hospital mortality, and hospital length of stay. RESULTS:Patients meeting the alert threshold were at nearly 5.3-fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6-6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9-fold greater risk of death (95% CI: 7.4-10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group. CONCLUSIONS: Real-time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes.
Authors: Jennifer C Ginestra; Heather M Giannini; William D Schweickert; Laurie Meadows; Michael J Lynch; Kimberly Pavan; Corey J Chivers; Michael Draugelis; Patrick J Donnelly; Barry D Fuchs; Craig A Umscheid Journal: Crit Care Med Date: 2019-11 Impact factor: 7.598
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