Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability*. Issue 7 (July 2017)

• Record Type: Journal Article
• Authors: Nagaraj, Sunil B.; Biswal, Siddharth; Boyle, Emily J.; Zhou, David W.; McClain, Lauren M.; Bajwa, Ednan K.; Quraishi, Sadeq A.; Akeju, Oluwaseun; Barbieri, Riccardo; Purdon, Patrick L.; Westover, M. Brandon
• Is Part Of: Critical care medicine. Volume 45:Issue 7(2017)
• Publication Date: 2017-07
• Subjects: heart rate variability -- intensive care -- Richmond Agitation Sedation Scale -- sedation monitoring -- support vector machine; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000002364
• Languages: English
• ISSNs: 0090-3493

Abstract

Abstract : Objective: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. Design: Multicenter, pilot study. Setting: Several ICUs at Massachusetts General Hospital, Boston, MA. Patients: We gathered 21, 912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives. Measurements and Main Results: As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (–5), "deeply sedated" (–4 to –3), "lightly sedated" (–2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%. Conclusions: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation. Abstract : Supplemental Digital Content is available in the text.