• Sunil B Nagaraj, Siddharth Biswal, Emily J Boyle, David W Zhou, Lauren M McClain, Ednan K Bajwa, Sadeq A Quraishi, Oluwaseun Akeju, Riccardo Barbieri, Patrick L Purdon, and M Brandon Westover.
• 1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.2Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.3Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.4Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy.
• Crit. Care Med. 2017 Jul 1; 45 (7): e683-e690.

ObjectiveTo develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.DesignMulticenter, pilot study.SettingSeveral ICUs at Massachusetts General Hospital, Boston, MA.PatientsWe 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 ResultsAs "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%.ConclusionsWith 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.

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