Julien Lauzier Bigué1,2, Catherine Duclos1,3, Marie Dumont1,3, Jean Paquet1, Hélène Blais1, David K Menon4, Francis Bernard2,5, Nadia Gosselin1,6. 1. Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada. 2. Department of Medicine, Université de Montréal, Montreal, Canada. 3. Department of Psychiatry, Université de Montréal, Montreal, Canada. 4. Division of Anaesthesia, Cambridge University, Cambridge, United Kingdom. 5. Critical Care, Hôpital du Sacré-Coeur de Montréal, Montreal, Canada. 6. Department of Psychology, Université de Montréal, Montreal, Canada.
Abstract
STUDY OBJECTIVES: Sleep-wake disturbances are frequent among patients hospitalized for traumatic injuries but remain poorly documented because of the lack of tools validated for hospitalized patients. This study aimed to validate actigraphy for nighttime sleep monitoring of hospitalized patients with severe traumatic injuries, using ambulatory polysomnography (PSG). METHODS: We tested 17 patients (30.4 ± 14.7 years, 16.6 ± 8.2 days postinjury) who had severe orthopedic injuries and/or spinal cord injury, with or without traumatic brain injury. When medically stable, patients wore an actigraph on a nonparalyzed arm and underwent ambulatory PSG at the bedside. Data were converted to 1-minute epochs. The following parameters were calculated for the nighttime period: total sleep time, total wake time, sleep efficiency, and number of awakenings. Epoch-by-epoch concordance between actigraphy and PSG was analyzed to derive sensitivity, specificity, and accuracy. PSG sleep parameters were compared to those obtained from four actigraphy scoring algorithms by Bland-Altman plots. RESULTS: Sensitivity to detect sleep was ≥ 92% and accuracy was > 85% for all four actigraphy algorithms used, whereas specificity varied from 48% to 60%. The low-activity wake threshold (20 activity counts per epoch) was most closely associated with PSG on all sleep parameters. This scoring algorithm also had the highest specificity (59.9%) and strong sensitivity (92.8%). CONCLUSIONS: Actigraphy is valid for monitoring nighttime sleep and wakefulness in patients hospitalized with traumatic injuries, with sensitivity, specificity and accuracy comparable to actigraphic recordings in healthy individuals. A scoring algorithm using a low wake threshold is best suited for this population and setting.
STUDY OBJECTIVES: Sleep-wake disturbances are frequent among patients hospitalized for traumatic injuries but remain poorly documented because of the lack of tools validated for hospitalized patients. This study aimed to validate actigraphy for nighttime sleep monitoring of hospitalized patients with severe traumatic injuries, using ambulatory polysomnography (PSG). METHODS: We tested 17 patients (30.4 ± 14.7 years, 16.6 ± 8.2 days postinjury) who had severe orthopedic injuries and/or spinal cord injury, with or without traumatic brain injury. When medically stable, patients wore an actigraph on a nonparalyzed arm and underwent ambulatory PSG at the bedside. Data were converted to 1-minute epochs. The following parameters were calculated for the nighttime period: total sleep time, total wake time, sleep efficiency, and number of awakenings. Epoch-by-epoch concordance between actigraphy and PSG was analyzed to derive sensitivity, specificity, and accuracy. PSG sleep parameters were compared to those obtained from four actigraphy scoring algorithms by Bland-Altman plots. RESULTS: Sensitivity to detect sleep was ≥ 92% and accuracy was > 85% for all four actigraphy algorithms used, whereas specificity varied from 48% to 60%. The low-activity wake threshold (20 activity counts per epoch) was most closely associated with PSG on all sleep parameters. This scoring algorithm also had the highest specificity (59.9%) and strong sensitivity (92.8%). CONCLUSIONS: Actigraphy is valid for monitoring nighttime sleep and wakefulness in patients hospitalized with traumatic injuries, with sensitivity, specificity and accuracy comparable to actigraphic recordings in healthy individuals. A scoring algorithm using a low wake threshold is best suited for this population and setting.
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