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1.
J Hosp Med ; 7(8): 628-33, 2012 Oct.
Article in English | MEDLINE | ID: mdl-22865462

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.


Subject(s)
Diffusion of Innovation , Heart Rate/physiology , Inpatients , Monitoring, Physiologic/methods , Respiratory Rate/physiology , Academic Medical Centers , Acute Disease , Aged , Female , Humans , Male , Predictive Value of Tests , Retrospective Studies , Sensitivity and Specificity , Time Factors , Vital Signs
2.
J Patient Saf ; 7(4): 181-4, 2011 Dec.
Article in English | MEDLINE | ID: mdl-21926639

ABSTRACT

OBJECTIVES: To perform initial validation of a continuous motion monitoring technology that can potentially be used as a risk assessment tool to determine risk for developing pressure ulcers (PUs). METHODS: We have used the EverOn system (Earlysense LTD, Ramat Gan, Israel) as a bed movement and activity monitor. The EverOn is a contactless continuous measurement system based on a piezoelectric sensor that is placed under the patient's mattress. The study was a noninterventional study performed in 2 medical departments in 2 medical centers. Recorded movement data from enrolled patients were retrospectively analyzed, and patients were assigned a motion level score. Motion scores for the first night of hospitalization were correlated with the Norton scale as calculated per patient on admission. RESULTS: Overall, 116 patients were included in the study from the 2 sites. Motion score was significantly different between the PU risk groups as determined by the Norton scale (10.7 ± 6.2 for low, 5.4 ± 4.9 for intermediate, and 1.6 ± 3.2 for high risk; P < 0.001). Using the Norton scale as a gold standard to define high risk for developing PU (≤14), the sensitivity of the motion score was 85%, and the specificity was 93%. With regard to individual risk components, we found that activity, mobility, physical condition, and incontinence correlated highly with motion level. CONCLUSIONS: The high correlation between the EverOn motion score and the calculated Norton scale indicates the potential of this technology to serve as a risk assessment tool for the development of PUs.


Subject(s)
Movement , Pressure Ulcer/prevention & control , Risk Assessment/methods , Aged , Body Mass Index , Feasibility Studies , Female , Humans , Male , Pressure Ulcer/etiology , Reproducibility of Results , Retrospective Studies , Statistics, Nonparametric
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