Oxygen Desaturation Index from Nocturnal Oximetry: A Sensitive and Specific Tool to Detect Sleep-Disordered Breathing in Surgical Patients

Department of Anesthesia, University Health Network, University of Toronto, 399, Bathurst Street, Toronto, ON, Canada M5T 2S8.
Anesthesia and analgesia (Impact Factor: 3.47). 02/2012; 114(5):993-1000. DOI: 10.1213/ANE.0b013e318248f4f5
Source: PubMed


It is impractical to perform polysomnography (PSG) in all surgical patients suspected of having sleep disordered breathing (SDB). We investigated the role of nocturnal oximetry in diagnosing SDB in surgical patients.
All patients 18 years and older who visited the preoperative clinics for scheduled inpatient surgery were approached for study participation. Patients expected to have abnormal electroencephalographic findings were excluded. All patients underwent an overnight PSG at home with a portable device and a pulse oximeter. The PSG recordings were scored by a certified sleep technologist. The oximetry recordings were processed electronically.
Four hundred seventy-five patients completed the study: 217 males and 258 females, aged 60 ± 11 years, and body mass index 31 ± 7 kg/m(2). The apnea-hypopnea index (AHI), the average number of episodes of apnea and hypopnea per hour of sleep, was 9.1 (2.8 to 21.4) [median (interquartile range)] and 64% patients had AHI >5. There was a significant correlation between oxygen desaturation index (ODI, hourly average number of desaturation episodes) and cumulative time percentage with SpO(2) <90% (CT90) from nocturnal oximetry, with the parameters measuring sleep breathing disorders from PSG. Compared to CT90, ODI had a stronger correlation and was a better predictor for AHI. The area under receiver operator characteristics curve for ODI to predict AHI >5, AHI >15, and AHI >30 was 0.908 (CI: 0.880 to 0.936), 0.931 (CI: 0.090 to 0.952), and 0.958 (CI: 0.937 to 0.979), respectively. The cutoff value based on the maximal accuracy for ODI to predict AHI >5, AHI >15, and AHI >30 was ODI >5, ODI >15, and ODI >30. The accuracy was 86% (CI: 83%-88%), 86% (CI: 83%-89%), and 94% (CI: 92%-96%), respectively. The ODI >10 demonstrated a sensitivity of 93% and a specificity of 75% to detect moderate and severe SDB.
ODI from a high-resolution nocturnal oximeter is a sensitive and specific tool to detect undiagnosed SDB in surgical patients.

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Available from: Frances Chung, Oct 08, 2014
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    • "In terms of BMI, overweight participants may have had greater difficulty getting comfortable on the stretcher beds, which were a standard size. Nonetheless, the differences in sleep[7]and importantly their ODI values suggest that an underlying SBD was highly probable[15,18]. We hypothesised that those in the SDB group would exhibit signs of increased vulnerability to the sleep restriction, namely increased self-reported fatigue, poorer response time performance and more lapses. "
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    ABSTRACT: Adequate sleep is fundamental to workplace performance. For volunteer firefighters who work in safety critical roles, poor performance at work can be life threatening. Extended shifts and sleeping conditions negatively impact sleep during multi-day fire suppression campaigns. Having sleep disordered breathing (SDB) could contribute further to sleep deficits. Our aim was to investigate whether those with suspected SDB slept and performed more poorly during a fire ground simulation involving sleep restriction. Participants, n = 20 participated in a 3-day-4-night fire ground simulation. Based on oximetry desaturation index data collected during their participation, participants were retrospectively allocated to either a SDB (n = 8) or a non-SDB group (n = 12). The simulation began with an 8 h Baseline sleep (BL) followed by two nights of restricted (4 h) sleep and an 8 h recovery sleep (R). All sleeps were recorded using a standard electroencephalography (EEG) montage as well as oxygen saturation. During the day, participants completed neurobehavioral (response time, lapses and subjective fatigue) tasks. Mixed effects ANOVA were used to compare differences in sleep and wake variables. Analyses revealed a main effect of group for Total sleep (TST), REM , wake after sleep onset (WASO) and Arousals/h with the SDB group obtaining less TST and REM and greater WASO and Arousals/h. The group × night interaction was significant for N3 with the SDB group obtaining 42 min less during BL. There was a significant main effect of day for RRT, lapses and subjective fatigue and a significant day × group interaction for RRT. Overall, the SDB group slept less, experienced more disturbed sleep and had poorer response time performance, which was exacerbated by the second night of sleep restriction. This could present a safety concern, particularly during longer campaigns and is worthy of further investigation. In addition, we would recommend promotion of awareness of SDB, its symptoms and potential impact among volunteers and relevant agencies.
    Preview · Article · Jan 2016 · International Journal of Environmental Research and Public Health
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    • "In the developed implementation, a pulse oximeter is used to measure oxygen level and provide continuous data transmission of a 4 byte data packet sent every second. In the developed system, we use the oxygen desaturation index (ODI) which is defined as the average number of events per hour [26]. An event is detected if the oxygen level is below the average by 4%, and lasts at least 10 s. "
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    ABSTRACT: Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body's movement. Finally, we examine our system's ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare.
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