LITERATURE REVIEW

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Journal of Sleep Research / Volume 0, Issue 0

REGULAR RESEARCH PAPER  Full Access

Comparison of apnoea–hypopnoea index and oxygen desaturation index when

identifying obstructive sleep apnoea using type‐4 sleep studies

Chamara V. Senaratna , Adrian Lowe , Jennifer L. Perret , Caroline Lodge , Gayan Bowatte , Michael J. Abramson , Bruce R. Thompson , Garun Hamilton , Shyamali C. Dharmage

First published: 18 December 2018 https://doi-org.proxygw.wrlc.org/10.1111/jsr.12804

Serials Solutions

Summary

The concordance of di�erent indices from type‐4 sleep studies in diagnosing and categorising the severity of obstructive sleep apnoea is not known. This is a critical gap as type‐4 sleep studies are used to diagnose obstructive sleep apnoea in some settings. Therefore, we aimed to determine the concordance between �ow‐based apnoea– hypopnoea index (AHI ) and oxygen desaturation index (ODI ) by measuring them concurrently. Using a random sub‐sample of 296 from a population‐based cohort who underwent two‐channel type‐4 sleep studies, we assessed the concordance between AHI and ODI . We compared the prevalence of obstructive sleep apnoea of various severities as identi�ed by the two methods, and determined their concordance using coe�cient Kappa(κ). Participants were aged (mean ± SD) 53 ± 0.9 years (48% male). The body mass index was 28.8 ± 5.2 kg m and neck circumference was 37.4 ± 3.9 cm. The median AHI was 5 (inter‐quartile range 2, 10) and median ODI was 9 (inter‐quartile range 4, 15). The obstructive sleep apnoea prevalence reported using AHI was signi�cantly lower than that reported using ODI at all severity thresholds. Although 90% of those with moderate–severe obstructive sleep apnoea classi�ed using AHI were identi�ed by using ODI , only 46% of those with moderate–severe obstructive sleep apnoea classi�ed using ODI were identi�ed by AHI . The overall concordance between AHI and ODI in diagnosing and classifying the severity of obstructive sleep apnoea was only fair (κ = 0.32), better for males (κ = 0.42 [95% con�dence interval 0.32–0.57]

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versus 0.22 [95% con�dence interval 0.09–0.31]), and lowest for those with a body mass index ≥ 35 (κ = 0.11). In conclusion, ODI and AHI from type‐4 sleep studies are at least moderately discordant. Until further evidence is available, the use of ODI as the measure of choice for type‐4 sleep studies is recommended cautiously.

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1 INTRODUCTION Given the increasing prevalence of obstructive sleep apnoea (OSA) over the last decade (Senaratna et al., 2017), there has been increasing attention on diagnostic methods. The gold‐ standard diagnostic test for OSA is attended, in‐laboratory polysomnography, which is also known as type‐1 sleep study (Qaseem et al., 2014). Due to logistical and �nancial constraints, other types of studies, which use portable sleep study devices and can be performed at home, are also used to diagnose OSA. These are called type‐2, ‐3 or ‐4 sleep studies based on the number and complexity of data channels they use (Collop et al., 2007; Qaseem et al., 2014), and play an important role in the diagnosis and management of OSA.

How the indices that are used to de�ne OSA, namely, apnoea–hypopnoea index (AHI), respiratory disturbance index (RDI) and/or oxygen desaturation index (ODI; Chai‐Coetzer et al., 2014; Dawson et al., 2015), are generated depends on the type of sleep study. Study types 1–3 generate AHI (or RDI) utilising air�ow and oxygen saturation, and are usually scored according to certain “rules” – most commonly those published by the American Academy of Sleep Medicine (AASM) (1999, Iber, Israel, Chesson, & Quan, 2007; Berry et al., 2012). However, AHI generated from a type‐4 sleep study is generally based solely on nasal air�ow and does not take oxygen desaturation into account (AHI ). Some type‐4 sleep studies also commonly generate ODI, either solely or in addition to air�ow‐based AHI. In type‐4 studies these indices are typically based on auto‐analysis using testing equipment's software.

As type‐4 sleep studies have shown good diagnostic utility (Qaseem et al., 2014), they are being increasingly used to diagnose OSA, especially in resource‐poor settings (Gantner et al., 2010). Both AHI and ODI measured in type‐4 sleep studies have been shown to correlate well with AHI measured in type‐1 sleep studies (Erman, Stewart, Einhorn, Gordon, & Casal, 2007; Netzer, Eliasson, Netzer, & Kristo, 2001). There is some evidence that ODI better estimates respiratory events (Escourrou et al., 2015) and, furthermore, provides a more robust signal (Gantner et al., 2010). However, whether the diagnosis and severity classi�cation of OSA varies based on the chosen index when type‐4 portable sleep study devices are used has not been previously investigated. This knowledge is of importance, as accurate severity classi�cation of OSA has prognostic and management implications. Given that some type‐4 portable sleep study devices o�er the opportunity to choose between independently‐measured AHI and ODI when making a diagnosis of OSA, we aimed to determine the correlation between AHI and ODI

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4/9/2019 Comparison of apnoea–hypopnoea index and oxygen desaturation index when identifying obstructive sleep apnoea using type‑4 sleep studies - Senaratn…

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when concurrently measured using a type‐4 portable sleep study device and their concordance when used to describe the prevalence and classi�cation of the severity of OSA.

2 MATERIALS AND METHODS We used data from the sixth decade follow‐up of the Tasmanian Longitudinal Health Study (TAHS). The TAHS cohort was originally recruited in 1968 to study chronic respiratory diseases and allergies in 8,583 Tasmanian school children born in 1961 (probands; Matheson et al., 2017). They were followed up in 1974, 1979, 1991, 2002, 2010 and 2012. The last follow‐up was completed in 2016 and data were collected from 3,609 probands who could be traced; 74% of them attended a respiratory (not sleep) laboratory study.

2.1 Sleep studies A random sample of 772 from among respiratory laboratory attendees were invited to undergo type‐4 sleep studies using ApneaLink™ device (ResMed, Bella Vista, Australia). Those who agreed to participate were given instructions on setting up ApneaLink™ at home, switching it on before going to bed, and switching it o� after getting up in the morning. ApneaLink™ recorded the following signals: nasal air‐�ow, snoring, oxygen desaturation, respiratory e�ort and pulse rate. ApneaLink™ devices were returned to the laboratory after each use, and data were downloaded using ApneaLink™ version 9.2.0 proprietary software. These were then auto‐ analysed using this software and user‐de�ned criteria. A random sample of 10% of the records was manually examined to check the accuracy of the auto‐analysis. The criterion for apnoea was a reduction in air�ow by ≥ 80% for at least 10 s, for hypopnoea there was a reduction in air�ow by ≥ 50% for at least 10 s, and for oxygen desaturation event a reduction in oxygen saturation by at least 3% from the baseline.

2.2 De�nitions Those who had both oxygen desaturation information (ODI ) and �ow‐based apnoea– hypopnoea (AHI ) information for at least 4 hr of sleep were included in the analysis. ODI and AHI thresholds of ≥ 5, ≥ 15 and ≥ 30 events per hr were used to categorise participants as having any, moderate–severe and severe OSA, respectively.

Based on the body mass index (BMI), participants were categorised as being normal weight (< 25 kg m ), overweight (≥ 25 and < 30 kg m ), obese class‐I (≥ 30 and < 35 kg m ), obese class‐II (≥ 35 and < 40 kg m ) and obese class‐III (≥ 40 kg m‐ ; World Health Organization, 2000). Obese class‐I was de�ned as obese, and classes‐II and ‐III were collectively de�ned as morbidly obese. The prevalence of OSA of various severities was reported based on the above thresholds using AHI and ODI criteria separately.

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2.3 Analysis Data were analysed using Stata/SE 14.1 software (StataCorp LP, College Station, TX, USA). The correlation between AHI and ODI was determined using Pearson's correlation coe�cient. Bland–Altman plots were used to examine the agreement between AHI and ODI . The prevalence of OSA at di�erent OSA severity thresholds as determined using AHI and ODI was compared using Pearson's χ ‐test. The severity of OSA classi�ed using ODI and AHI was cross‐tabulated, and di�erences in distribution were also examined using the χ ‐test. Cohen's Kappa (κ) coe�cient was used to check the concordance between AHI and ODI in classifying OSA severity. The concordance was considered poor if the Kappa coe�cient was < 0.00, slight if 0.00–0.20, fair if 0.21–0.40, moderate if 0.41– 0.60, substantial if 0.61–0.80, and almost perfect if 0.81–1.00 (Landis & Koch, 1977).

This study was approved by the Human Research Ethics Committee of the University of Melbourne (approval number 040375). Participants provided written informed consent.

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3 RESULTS Out of the 772 who were invited to undergo sleep studies, 137 declined and another 211 who agreed could not complete the sleep studies due to various reasons (Supporting Information Figure S1). Out of the remaining 424 (54.9%), 296 had both air�ow and oxygen saturation recordings for at least 4 hr. Their basic characteristics are shown in Table 1. They were aged 53 years, overweight, and had a high prevalence of OSA de�ned using ODI and AHI .

Table 1. Basic characteristics of the sample (n = 296)

Characteristic Mean ± SD or N (%) or median (range; inter‐quartile range)

Age (years) 52.9 ± 0.9

Gender (male) 143 (48.3)

BMI (kg m ) 28.8 ± 5.2

Neck circumference (cm) 37.4 ± 3.9

Apnoeas and hypopnoeas per hr 5 (0, 97; 2, 10)

Oxygen desaturation events per hr 9 (1, 92; 4, 15)

AHI  ≥ 5 161 (54.4)

AHI  ≥ 15 41 (13.8)

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Characteristic Mean ± SD or N (%) or median (range; inter‐quartile range)

ODI  ≥ 5 221 (71.7)

ODI  ≥ 15 81 (21.4)

AHI , �ow‐based apnoea–hypopnea index (using 50% drop in nasal pressure); BMI, body mass

index; ODI , oxygen desaturation index (using 3% drop in oxygen saturation).

There was a strong correlation between AHI and ODI in the overall sample (Pearson's r = .85; 95% con�dence interval [CI] 0.82, 0.88; p < .001). A moderation e�ect by gender is seen (p for moderation e�ect < .001), where males had a signi�cantly stronger correlation (r = .90; 95% CI 0.86, 0.93; p < .001) than females (r = .65; 95% CI 0.55, 0.73; p < .001). Similar gender di�erences were also seen across all BMI categories, although statistical signi�cance was seen only in overweight and obese class‐I categories (Table 2).

Table 2. Correlation between ODI and AHI at di�erent BMI thresholds and categories

BMI category/threshold (kg m ) Pearson's r (95% CI); p (n)

Total Male Female

Normal weight (< 25) .65 (0.50, 0.76); < .001

(77)

.69 (0.43, 0.84); < .001

(29)

.60 (0.38, 0.75); < .001

(48)

Overweight (≥ 25 and < 30) .85 (0.79, 0.90); < .001

(103)

.87 (0.79, 0.92); < .001

(66)

.67 (0.44, 0.82); < .001

(37)

Obese class‐I (≥ 30 and < 35; obese) .91 (0.87, 0.94);

< .001(80)

.95 (0.91, 0.98); < .001

(37)

.62 (0.40, 0.78); < .001

(43)

Obese classes‐II & ‐III (≥ 35; morbidly

obese)

.78 (0.60, 0.88); < .001

(36)

.80 (0.39, 0.95); .003

(11)

.62 (0.30, 0.82); < .001

(25)

≥ 25 kg m .86 (0.82, 0.89); < .001

(219)

.90 (0.86, 0.93); < .001

(114)

.63 (0.50, 0.73); < .001

(105)

≥ 30 kg m .86 (0.81, 0.90);

< .001(116)

.92 (0.86, 0.95); < .001

(48)

.61 (0.44, 0.74); < .001

(68)

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BMI, body mass index; CI, con�dence interval.

Obese classes II and II were combined due to small numbers in these categories.

In the total sample, the correlation was weakest in those who had normal BMI (r = .65; Table 2), and this signi�cantly increased gradually through overweight to obese (r = .91), then decreased in morbidly obese (r = .78). These di�erences were statistically signi�cant between BMI categories from normal weight to obese. However, the correlation in morbidly obese was not statistically di�erent from those who were normal weight or overweight (Table 2). This trend was also seen in males. In females, however, such a trend was not present, and the correlation was not statistically di�erent between those in normal weight, overweight, obese and morbidly obese categories. Intra‐class correlation coe�cients (ICCs) were almost identical to these (Table S1).

Bland–Altman plots for the total sample and for each gender showed a higher number of respiratory events in general when ODI was used compared with when AHI was used (Figures 1 and 2). The bias (mean di�erence between ODI and AHI ) for the total sample was 3.5. The limits of agreement were wide (lower and upper limits of agreement −9.9, 16.8). There was no di�erence between males (3.2; limits −10.2, 17.0) and females (3.8; limits −9.2, 16.7). The bias increased with BMI, being 0.8 (−6.9, 8.5) for those with normal weight, 2.5 (−7.8, 12.8) for overweight, 4.4 (−9.3, 18.1) for obese class‐I, 5.8 (−4.8, 16.5) for obese class‐II, and 18.2 (−6.5, 42.8) for obese class‐III (Figures S2 and S3). At AHI and ODI thresholds of ≥ 5, ≥ 15 and ≥ 30, the use of AHI underestimated the OSA prevalence, respectively, by 27%, 50% and 48% compared with the use of ODI (Table 3). Di�erences between classi�cation by AHI and ODI at all of these thresholds were statistically signi�cant (p < .001). Similarly, the use of ODI identi�ed signi�cantly more participants as having mild OSA, moderate OSA and severe OSA (p < .001 for all) than when AHI was used. This e�ect was proportionately more pronounced in the classi�cation of moderate and severe OSA, where the use of ODI identi�ed twice as many participants to have moderate OSA and severe OSA than use of AHI .

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Figure 1

Open in �gure viewer PowerPoint

Bland–Altman plot for the distribution of apnoea–hypopnoea index (AHI ) and oxygen

desaturation index (ODI ) in the total sample �ow50%

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Figure 2

Open in �gure viewer PowerPoint

Bland–Altman plot for the distribution of apnoea–hypopnoea index (AHI ) and oxygen

desaturation index (ODI ) in (a) males and (b) females

Table 3. Prevalence of OSA at di�erent AHI /ODI thresholds

AHI /ODI

threshold

Prevalence % (95% CI); N % Di�erence in prevalence when AHI was used

compared with when ODI used Using

AHI

Using

ODI

No OSA (≥ 5) 45.6 (40.0,

51.4); 135

25.3 (20.7,

30.6); 75

↑80.2

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AHI /ODI

threshold

Prevalence % (95% CI); N % Di�erence in prevalence when AHI was used

compared with when ODI used Using

AHI

Using

ODI

Any OSA (≥ 5) 54.4 (48.6,

60.0); 161

74.7 (69.4,

79.3) ; 221

↓27.2

Mild OSA (≥ 5 to <

15)

40.5 (35.1,

46.3); 120

47.3 (41.6,

53.0) ; 140

↓14.4

Moderate OSA (≥

15 to < 30)

9.5 (6.6,

13.4); 28

18.9 (14.8,

23.8) ; 56

↓49.7

Moderate–severe

OSA (≥ 15)

13.8 (10.3,

18.3); 41

27.4 (22.6,

32.8) ; 81

↓49.6

Severe OSA (≥ 30) 4.4 (2.6,

7.4); 13

8.4 (5.6,

12.2) ; 25

↓47.6

AHI , �ow‐based apnoea–hypopnea index (using 50% drop in nasal pressure); CI, con�dence

interval; ODI , oxygen desaturation index (using 3% drop in oxygen saturation); OSA, obstructive sleep

apnoea.

p < .001 for chi‐squared test for comparison between prevalence determined using AHI and

ODI for the given AHI /ODI threshold (within the same row).

The concordance between ODI and AHI in classifying OSA severity was only fair (Landis & Koch, 1977; 54.4% similarly classi�ed; κ = 0.32, 95% CI 0.24–0.40). The highest discordance in severity classi�cation was seen when 57% of those who were classi�ed as moderate OSA by ODI were identi�ed as mild OSA by AHI (Table 4). Furthermore, 28% of those who were classi�ed as severe OSA by ODI were also identi�ed as mild OSA by AHI . In contrast, all those who were classi�ed as having severe OSA by AHI were similarly classi�ed by ODI , and nearly 86% of those who were classi�ed as moderate OSA by AHI were classi�ed as moderate or severe OSA by ODI . These di�erences were statistically signi�cant (p for Fisher's exact test < .001).

Table 4. Agreement between OSA severities determined by AHI and ODI

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Using AHI Using ODI

No OSA (n =

75)

Mild OSA (n =

140)

Moderate OSA (n =

56)

Severe OSA (n =

25)

N (%) N (%) N (%) N (%)

Using AHI Using ODI

No OSA (n =

75)

Mild OSA (n =

140)

Moderate OSA (n =

56)

Severe OSA (n =

25)

N (%) N (%) N (%) N (%)

No OSA (n = 135) 61 (20.6) 69 (23.3) 4 (1.4) 1 (0.3)

Mild OSA (n = 120) 14 (4.7) 67 (22.6) 32 (10.8) 7 (2.4)

Moderate OSA

(n = 28)

0 (0.0) 4 (1.4) 20 (6.8) 4 (1.4)

Severe OSA (n = 13) 0 (0.0) 0 (0.0) 0 (0.0) 13 (4.4)

Bold values indicate OSA severity that have been similarly classi�ed using AHI and ODI .

OSA, obstructive sleep apnoea; AHI , �ow‐based apnoea–hypopnea index (using 50% drop in nasal

pressure); ODI , oxygen desaturation index (using 3% drop in oxygen saturation); No OSA (AHI/ODI <

5); Mild OSA (AHI/ODI ≥ 5 to < 15); Moderate OSA (AHI/ODI ≥ 15 to < 30); Severe OSA (AHI/ODI ≥

30).

Percentages are out of a total of 296; Fisher's exact test < 0.001.

The concordance between ODI and AHI in classifying OSA severity was a�ected by gender and BMI. The concordance in males was moderate (59.4% similarly classi�ed; κ = 0.42, 95% CI 0.32–0.57), but only fair in females (49.7% similarly classi�ed; κ = 0.22, 95% CI 0.09–0.31). Similarly, the concordance was fair in those who had normal weight (63.6% similarly classi�ed; κ = 0.31, 95% CI 0.13–0.53), who were overweight (59.2% similarly classi�ed; κ = 0.38, 95% CI 0.23–0.52) and who were in obese class‐I (48.8% similarly classi�ed; κ = 0.24, 95% CI 0.09–0.38), but was only slight for those who were morbidly obese (33.3% similarly classi�ed; κ = 0.11, 95% CI −0.06 to 0.30).

The ICC between auto‐analyses and manual analyses of the random sub‐sample of 10% (n = 30) was 0.9 (95% CI 0.9–1.0) for both AHI and ODI. The kappa coe�cients for agreement for classi�cation of OSA severity when autoscoring and manual scoring were used were found to be 0.7 ± 0.1 (absolute agreement 80%) for AHI and 0.8 ± 0.1 (agreement 87%) for ODI.

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4 DISCUSSION

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We found that AHI (based on air�ow only) signi�cantly underestimated respiratory events and OSA prevalence at all thresholds compared with the use of concurrently measured ODI from the same type‐4 sleep study device. The concordance of AHI and ODI in classifying the severity of OSA was only fair and worsened with increasing BMI, but was better in males than in females. Although 90% of those with moderate or severe OSA classi�ed using AHI were also identi�ed by using ODI , only 46% of those who had moderate or severe OSA classi�ed using ODI were identi�ed by using AHI . These levels of disagreement were not clearly re�ected by the corresponding correlations, which were high.

Both AHI and ODI from type‐4 portable sleep study devices have been shown to have acceptable diagnostic utility when compared with type‐1 sleep studies (Erman et al., 2007; Netzer et al., 2001). When portable devices have been used in sleep clinic populations and analysed manually, the AHIs from portable devices have shown to have good correlation and concordance with those from polysomnography (Ayappa, Norman, Suryadevara, & Rapoport, 2004). The important di�erences in our study were that it was conducted in the general population and results from the testing were auto‐analysed. In a previous validation using auto‐analysis, the AHI from the same type‐4 portable sleep study device as used in our study has been shown to under‐report the respiratory events, but only in those with severe OSA, where the AHI was ≥ 30 events per hr (Erman et al., 2007). Type‐4 portable sleep study devices providing a �ow‐based AHI (AHI ) do not utilise ancillary measures to score hypopnoeas (arousals or oxygen desaturation), and therefore may either under‐ or over‐score hypopnoeas, depending on the threshold of �ow‐reduction that is used. When AHI is used with ApneaLink™, a conservative �ow‐reduction of 50% (Erman et al., 2007) is often required to prevent overscoring that is likely if smaller reductions are allowed without either arousal or oxygen desaturation. Although there is some potential for under‐reporting respiratory events by ODI from oximetry (missing respiratory events that have an associated arousal rather than oxygen desaturation; Netzer et al., 2001), there is some evidence that ODI performs better and is more similar to AHI from type‐1 sleep studies compared with AHI from portable sleep study devices (Escourrou et al., 2015). This suggests that ODI from type‐4 portable sleep study devices, rather than AHI , is a better approximation of the actual respiratory events. Furthermore, it has been shown that use of air�ow channels in addition to oximetry does not signi�cantly improve standalone oximetry agreement with type‐1 sleep studies (Dawson et al., 2015).

The high correlation between AHI and ODI that we observed and its variation by gender and BMI are, to our knowledge, the �rst such evidence using type‐4 portable sleep study devices. The overall correlation we saw is similar to what has been reported for type‐3 portable sleep study devices (r = .9) and type‐1 sleep studies (r = .80; Ernst et al., 2016). However, despite the increase in correlation with increasing BMI, the average di�erence

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between ODI and AHI (ODI − AHI ) increased with increasing BMI, from 0.8 in those with normal weight to 18.2 in those in obese class‐III. In addition, the proportion of people who were similarly classi�ed by ODI and AHI decreased from 64% in those with normal weight to 33% in those who were morbidly obese.

An increase in BMI has been shown to independently and signi�cantly predict both more severe OSA and a greater oxygen desaturation in blood during sleep‐disordered breathing, especially in the supine position (Peppard, Ward, & Morrell, 2009). It has been suggested that the likely primary mechanism by which obesity accentuates oxygen desaturation is by the e�ect of excess weight on reducing static lung volumes, especially functional residual capacity (Ling, James, & Hillman, 2012). This previous evidence therefore suggests that relatively small reductions in ventilation that fail to record a �ow‐based apnoea or hypopnoea event may lead to oxygen desaturation in obese individuals that is su�cient to record an oxygen desaturation event, compared with no oxygen desaturation in those with normal weight.

Clinically, the importance of establishing the most valid classi�cation for OSA status and severity as derived by type‐4 portable sleep study devices outweighs the information gained from correlating indices on a continuous scale (Berry, 2011). Our �nding that ODI identi�ed over 90% of those classi�ed as having moderate–severe OSA by AHI and that AHI failed to identify more than half of those classi�ed as moderate–severe OSA by ODI has signi�cant clinical implications. Given the direct and indirect health and economic costs of OSA (Leger, Bayon, Laaban, & Philip, 2012; Wittmann & Rodenstein, 2004), including nearly a twofold increase in medical expenditure associated with undiagnosed OSA (Wittmann & Rodenstein, 2004) and billions of dollars of additional healthcare costs resulting from the consequences of undiagnosed OSA (Knauert, Naik, Gillespie, & Kryger, 2015), missing those who may bene�t from treatment for OSA (Adult Obstructive Sleep Apnea Task Force of the American Academy of Sleep Medicine, 2009) also has important public health implications.

Furthermore, as oxygen desaturation has been linked with adverse consequences associated with OSA (Bradley & Floras, 2009; Drager, Jun, & Polotsky, 2010; Ryan, Taylor, & Mcnicholas, 2005), detecting those with nocturnal hypoxaemia may be arguably more important than detecting those with apnoeas and hypopnoeas without signi�cant oxygen desaturation. Nevertheless, physicians’ con�dence in managing patients with OSA based solely on ODI has been shown to be lower than when full polysomnographic data are available, despite the high diagnostic utility of ODI (area under the receiver operating characteristic curve being 0.94 [95% CI 0.90–0.98]) and non‐inferior clinical improvement in patients treated based solely on ODI versus full polysomnography data (Chai‐Coetzer et al., 2017). This lack of con�dence among the physicians may arise from inadequate prior experience in management models solely using ODI data (cf. full polysomnography) than from any inferiority of ODI‐based management. Some previous evidence suggests that if the practitioners are adequately trained

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in managing patients based on ODI alone, clinically important outcomes of the patients managed solely on ODI would not be inferior to those of the patients managed based on full polysomnography data (Antic et al., 2009; Chai‐Coetzer et al., 2013).

Our study is the �rst to report on how the use of AHI and ODI from the same type‐4 devices a�ect OSA prevalence and severity classi�cation. Our sample represents the general population, and the �ndings are unlikely to be materially in�uenced by speci�c health conditions that are present in high‐risk populations. However, our sample size was relatively small and therefore our �ndings should be interpreted with caution. Only 296 out of 424 participants who underwent sleep studies (70%) had valid data for both AHI and ODI. Although this is relatively higher than the rate of data loss that has commonly been reported (Kapoor & Greenough, 2015), data losses of up to 33% have been previously reported (Lux, Boehlecke, & Lohr, 2004). Although the prevalence of OSA when ODI ≥ 5 events per hr was used seems high (72%), similar estimates have been previously reported in population‐based studies (Senaratna et al., 2017; Tan et al., 2016) that used AASM 2012 scoring criteria (Berry et al., 2012), which uses the same desaturation levels. However, as the severity of OSA and the symptom‐ pro�le in the general population, from where our sample was drawn, are likely to be less severe than in patients referred to sleep centres, it is possible that the indices assessed here may be di�erent from clinical populations. In addition, ODI may not be an accurate measure of OSA in those with severe chronic obstructive pulmonary disease (Lacasse et al., 2011; Lewis, Fergusson, Eaton, Zeng, & Kolbe, 2009) or in physically trained subjects with high lung capacity who could have long apnoea/hypopnoea events without desaturation events. Our �ndings are also limited by the fact that the performances of both AHI and ODI were not compared with full polysomnography, the gold‐standard. The analyses of sleep records in our study were done using automated analysis and not manually. Using auto‐analysis is important in our study as this is how these type‐4 devices are generally used in real‐world practice. Our results are, therefore, more relevant to clinical practice than if manual scoring was used. Nevertheless, we performed a sensitivity analysis by selecting a random sub‐sample of 10% of studies for manual scoring. There was good reliability and agreement between auto‐ and manual analysis, similar to what has been reported in previous studies (Nigro, Dibur, Aimaretti, González, & Rhodius, 2011; Nigro, Malnis, Dibur, & Rhodius, 2012).

When comparing AHI and ODI from type‐4 devices, it is important for clinicians to know whether one metric gives the same or di�erent results compared with the other. Clinicians might assume that either metric can be used interchangeably, whereas we have shown that this is not the case. Although ODI becomes a subset of AHI when full polysomnography is used (where oxygen desaturation is considered when scoring hypopnoeas; Ayappa, Rapaport, Norman, & Rapoport, 2005; Redline et al., 2000), this is not the case when type‐4 devices are used. In the latter they are scored independent of each other from di�erent

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data, hence the importance of our study in guiding decision‐making in clinical practice. Furthermore, we have not used the new SCOPER classi�cation system (Collop et al., 2011) to identify the performance metrics of sleep study devices (as the type‐1–4 terminology is still in common use). If the SCOPER system is used, the two indices ODI and AHI fall into two di�erent categories of measurement indicating that they do not measure the same underlying conditions.

Although we have shown a clear di�erence in the performance of AHI and ODI , it is unclear which of these (or other similar metrics) optimally predict adverse outcomes from OSA. It may also be that the optimal diagnostic metric varies according to the type of outcome measured, for example, cardiovascular versus neurocognitive. Future research on prospective cohorts should help to answer this question, and may also have the opportunity to compare ODI and AHI using di�erent criteria (3% or 4% oxygen desaturation) and di�erent thresholds when de�ning apnoea and hypopnoea.

In summary, for this middle‐aged general population sample, we found that the concordance between concurrently measured ODI and AHI from type‐4 portable sleep study devices in diagnosis and severity classi�cation of OSA was unsatisfactory despite the high correlation between the two indices on a continuous scale. While we currently favour ODI as the measure of choice for type‐4 sleep studies, further adequately powered research to address variations within gender and BMI subgroups is required as these are likely to modify ODI during obstructive events.

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ACKNOWLEDGEMENTS The authors acknowledge the National Health and Medical Research Council for funding, and the Australian government for graduate‐training and scholarships. Contributions by Professors Haydn Walters, Peter Frith, Alan James, Paul Thomas, Graham Giles, David Jones, Richard Wood‐ Baker, John Hopper, Melanie Matheson and Lyle Gurrin are appreciated. The authors thank ResMed for providing some of the ApneaLink™ ‐devices.

CONFLICT OF INTEREST G. H. has received equipment from third parties to support research unrelated to the current project. M. A. has received grants from P�zer and Boehringer‐Ingelheim, and assistance with conference attendance from Sano�, the submitted work. A. L. has received grants from the National Health and Medical Research Council, during the conduct of the study. ResMed loaned some of the ApneaLinik™ devices used for this study.

AUTHOR CONTRIBUTIONS

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