Early recognition of acutely deteriorating patients in non-intensive care units: Assessment of an innovative monitoring technology

Eyal Zimlichman, Martine Szyper-Kravitz, Zvika Shinar, Tal Klap, Shiraz Levkovich, Avraham Unterman, Ronen Rozenblum, Jeffrey M. Rothschild, Howard Amital, Yehuda Shoenfeld

Research output: Contribution to journalArticlepeer-review

Abstract

Background:: Continuous vital sign monitoring has the potential to detect early clinical deterioration. While commonly employed in the intensive care unit (ICU), accurate and noninvasive monitoring technology suitable for floor patients has yet to be used reliably. Objective:: To establish the accuracy of the Earlysense continuous monitoring system in predicting clinical deterioration. Design:: Noninterventional prospective study with retrospective data analysis. SETTING:: Two medical wards in 2 academic medical centers. PATIENTS:: Patients admitted to a medical ward with a diagnosis of an acute respiratory condition. Intervention:: Enrolled patients were monitored for heart rate (HR) and respiration rate (RR) by the Earlysense monitor with the alerts turned off. MEASUREMENTS:: Retrospective analysis of vital sign data was performed on a derivation cohort to identify optimal cutoffs for threshold and 24-hour trend alerts. This was internally validated through correlation with clinical events recognized through chart review. RESULTS:: Of 113 patients included in the study, 9 suffered major clinical deterioration. Alerts were found to be infrequent (2.7 and 0.2 alerts per patient-day for threshold and trend alert, respectively). For the threshold alerts, sensitivity and specificity in predicting deterioration was found to be 82% and 67%, respectively, for HR and 64% and 81%, respectively, for RR. For trend alerts, sensitivity and specificity were 78% and 90% for HR, and 100% and 64% for RR, respectively. CONCLUSIONS:: The Earlysense monitor was able to continuously measure RR and HR, providing low alert frequency. The current study provides data supporting the ability of this system to accurately predict patient deterioration.

Original languageEnglish
Pages (from-to)628-633
Number of pages6
JournalJournal of Hospital Medicine
Volume7
Issue number8
DOIs
StatePublished - Oct 2012

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