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��ࡱ�>��	OQ����LMN�����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������'`	��>qbjbj���
������}&�������TZ�e�e�e�f\vg4Z�h�i(�i"jjj�j.	kk�������$n�h֨p+���q�j�j�q�q+�jj���*|*|*|�q�jj�*|�q�*|*|�����j�iPb�e�O��e|x���і4֤0�É�F�0z�F� �����F�}�T)k��l*|�m��n)k)k)k+�+��{X)k)k)k��q�q�q�qZZZ�X>aDZZZ>aZZZ����Added value of a Mandible Movement Automated Analysis in the screening of Obstructive Sleep Apnea: is there a gender influence?

Dr. Gis�le Maury, gisele.Maury@uclouvain.be, MD1, 
Mr. Laurent Cambron, lcambron@chu.ulg.ac.be2 
Prof. Jacques Jamart, jacques.Jamart@uclouvain.be, MD, MSc3
Prof. Eric Marchand,  HYPERLINK "mailto:eric.Marchand@uclouvain.be" eric.Marchand@uclouvain.be, MD, PhD1
Prof. Fr�d�ric Senny,  HYPERLINK "mailto:f.Senny@helmo.be, Ig, PhD 4" f.Senny@helmo.be, Ig, PhD 4.
Prof. Robert Poirrier, robert.Poirrier@chu.ulg.ac.be, MD, PhD2 

1 Service de pneumologie, CHU Mont Godinne, Universit� catholique de Louvain, YVOIR, Belgium, 5530; 
2Neurology, Faculty of Medicine, B35 ULg (Sart-Tilman), Li�ge, Belgium, 4000;
3Scientific Support Unit, CHU Mont Godinne, Universit� catholique de Louvain, YVOIR, Belgium, 5530;
4 Electronic Unit, Gramm Institute, HELMo, Angleur, Belgium, 4031.

Corresponding author: Dr. Gis�le Maury, gisele.Maury@uclouvain.be, telephone: 003281423351; fax: 003281423352.

Conflict of interest: For each authors, there is no conflict of interests.











Abstract:
We previously demonstrated the added value of mandible movement automated analysis (MMAA) to a portable device for the screening of obstructive sleep apnea (OSA). In the present study, we aimed at investigating its respective value in the two genders. Several sleep monitoring equipments have been developed for the screening of OSA but few have been assessed in the female population.  In a Systematic Review of the literature on portable devices published in 2003, the authors recommend that studies include more diversified populations, among them women.
We prospectively compared in-laboratory polysomnography to simultaneous recording with a portable device with MMAA in 570 patients. The portable device included 3 signals: SpO2, NAF and MMAA. Two respiratory disorder indexes (RDI) were automatically calculated in each gender: RDI (without MMAA) and SMM-RDI (with MMAA). The added value of the MM study was assessed in each group and compared to each other.
The population was studied according to gender (men: 381; age: 50�13; BMI: 30�6 kg/m� and women: 189; age: 49�13; BMI: 27�6 kg/m�). Correlations between apnea hypopnea index (AHI) derived from PSG and SMM-RDI were excellent both in men (r: 0.95; p<0.001) and women (r: 0.94; p<0.001) and better (p<0.001 for both) than AHI with RDI (respectively r: 0.93; p<0.001 and r: 0.91; p<0.001). By adding the MMAA, the sensitivity improved significantly in both genders. The sensitivity of SMM-RDI was lower in women for the detection of both mild to severe (AHI e"5/h) (p=0.005) and moderate to severe (AHI e"15/h) disease (p<0.001). These differences disappeared for severe disease. 
This study demonstrated a significant added value of MMAA to a portable device in the screening of OSA in both genders, although the SMM-RDI had a lower sensitivity in women for the diagnosis of mild and moderate OSA.
Keywords:
Salient mandible movement; sleep apnea syndrome; screening method; portable device; gender.
Abbreviations:
AHI: apnea hypopnea index
B&A: Bland and Altman 
BMI: body mass index
CI: confidence interval
COPD: chronic obstructive pulmonary disease
HTA: systemic arterial hypertension
LR +: positive likelihood ratio
LR -: negative likelihood ratio
MMAA: mandible movement automated analysis
MS: multiple sclerosis
NAF: nasal airflow
OSA: obstructive sleep apnea
PSG: polysomnography
RDI: respiratory disorder index
Se: sensitivity
SMM: salient mandible movement
SMM-RDI: respiratory disorder index with MMAA
Sp: specificity
SpO2: pulse oximetry providing oxygen saturation
TDT: total dark time
TST: total sleep time
TEXT
1.Introduction
Obstructive sleep apnea (OSA) syndrome is a frequent disease associated with multiple co-morbidities. Assessing the severity of the disease relies on the calculation of the apnea hypopnea index (AHI) which is the number of apneas and hypopneas per hour of sleep time. OSA is highly prevalent in the general population of western countries but largely more so in men than women. In the Wisconsin Cohort Study, Young et al. reported a prevalence of symptomatic sleep apnea (AHI e" 5/h with excessive daytime sleepiness) of 4 and 2 % for men and women, respectively [1]. In 2001, Bixler et al. published an OSA (AHI e"10/h and daytime symptoms) prevalence of 3.9% in men and 1.2 % in women [2]. Untreated, OSA has important health and socioeconomic consequences but efficient therapies are available and consequently its diagnosis is important. However, a large number of patients with OSA remain undiagnosed. Currently, polysomnography (PSG) is the golden standard test for diagnosing OSA. Several less elaborated sleep monitoring equipments have been developed for the diagnosis of OSA but few have been assessed in the female population.  In a Systematic Review of the literature on portable devices endorsed by the American Academy of Sleep Medicine, American College of Chest Physicians and American Thoracic Society published in 2003 [3] the authors recommend that studies include more diversified populations, among them women. 
OSA was initially considered to be highly prevalent in men, occurring in later life and presenting differently in women. Due to this idea and the lack of understanding of gender differences in clinical presentation, referral bias probably explains the poor representation of women in previous studies on portable devices. Later, gender-differences have been identified including a greater predominance of rapid eye movement- associated sleep abnormalities in women [4 - 6] and a lower mean AHI [5, 7] contributing to under-diagnosis of OSA in women. More and more attention is being paid to these gender specificities. 
Studies on portable monitoring assessments in the diagnosis of OSA in women are rare, mostly performed in mixed populations with a small number of women. These do not allow drawing a clear picture on the value of the portable devices in the female gender. To our knowledge, only one recent study assessed a visual analysis of a portable home monitoring in a selected women population [8]. 
The mandible movement study assessed here has been described in a previous article analyzing a large population including a high percentage of women (33%) [9].The method is based on the better detection of hypopnea thanks to the associated SMM as described by Senny et al.[10]. According to the definition used for hypopnea in this study, SMM is considered as an arousal marker. 
Data are now studied in each gender group to describe, first, the added value of the mandible movement automated analysis (MMMA) to a portable screening device in both genders and, secondly, to assess the gender influence on the accuracy of this method. 
2.Materials and methods
2.a. Setting
The sleep laboratory of the CHU Li�ge. This laboratory is linked to a neurological unit.
2.b. Participants
We prospectively included 629 consecutive subjects referred for diagnostic PSG who had a recording in one of the room of the sleep laboratory of the CHU Li�ge between January 2006 and March 2010, without any selection according to gender. There was no intentional selection for assignment of the patients in this particular room. The only exclusion criterion was ongoing Continuous Positive Airway Pressure (CPAP) treatment.
Methods have been described in details previously [9] 
The experiments were conducted in accordance with the principles of Declaration of Helsinki for Human Experimentation and after approval of Ethics Review Board; the patients were informed of the aim of the study and gave consent.
2.c. Recordings
We compared attended full PSG with 2 methods combining nasal airflow (NAF) and pulse oximetry (SpO2) recordings, with or without MMAA. In order to avoid divergences of sensors, NAF and SpO2 signals were common for the portable device and the PSG.
2.d. Analysis: 
The analysis was detailed in a previous publication [9]. They are briefly summarized here. The added value of MMAA is analyzed according to the gender, as well as the characteristics of the two groups. The first step was to describe the improvement gained by the MMAA in each gender: this new portable screening device was compared to RDI (without MMAA) and to the golden standard test (PSG). The second step was to compare the results between both genders. 
The number of respiratory events (apneas and hypopneas) per hour was calculated using as denominator the time between lights out and lights on called the total dark time (TDT) for the 2 methods and TST (total NREM stages 1 to 4 and REM sleep) providing an apnea hypopnea index (AHI) for PSG. The primary measures were the RDI for the 2 automated methods that were compared with the visual analysis for PSG.  A first classical RDI was defined as respiratory events provided by the NAF and SpO2 analysis divided by the TDT. A second RDI was called SMM-RDI and was defined as the number of respiratory events recognized by the NAF, SpO2 and MMAA divided by the TDT. Both RDI and SMM-RDI were calculated for both genders. 
In this MMAA, as in the previous publication, the SMM was identified with the same definition.
Automated analysis and mandible movement signal were blinded to the PSG scorer.
The mean SpO2 and the time below 90 % SpO2 during sleep were computed in the PSG analysis. 
2.e. Statistical analysis
Sensitivity (Se) and specificity (Sp), positive and negative likelihood ratios (LR) compared to the reference (PSG) were determined for different AHI thresholds (AHI e"5/h; AHI e"15/h and AHI e"30/h) with exact confidence intervals computed by F distribution. Sensitivity and specificity of the two automated devices were compared by binomial test. 
Paired Student t test was applied for comparison of the difference between the two methods compared to PSG.
Pearson correlation coefficients between the 2 indexes were assessed on the whole studied population and compared by Fisher test with tanh -1 transformation. In each gender group, correlations were compared by Hotelling test published by Cohen in 1989 [11]. Categorical variables were compared by chi-squared test.
The simultaneous influence of various parameters on AHI was studied by multiple linear regression.
Bland & Altman graphs were drawn with MedCalc (Mariakerke, Belgium) on the entire studied population. For the Bland Altman plot, each data point represents the difference between the two measurements (AHI-RDI) plotted against their mean for a single patient ([AHI+RDI]/2). Mean difference and limits of agreement (Bland & Altman) are reported, where the limits of agreement have been defined as � 1.96 SD of the difference. The same was done with SMM-RDI.
All statistical tests were two-tailed. Statistical significance was set at p < 0.05.
Analyses were performed with NCSS version 2007 (Kaysville, Utah) and SPSS 15.0 (SPSS Inc., Chicago Ill.) 
3. Results:
3.1 Whole population
Data were collected in 629 patients consecutively and randomly admitted in one room of the sleep laboratory, and 570 were considered for the analysis. Fifty nine patients were excluded for the analysis as depicted in previous publication [9]. Most frequent main diagnoses were OSA (n=344; 60%), insomnia/ anxiety/ depression (n=75; 13 %) and upper airway resistance syndrome (n=25; 4 %). 
[Table 1]
3.2 Analysis according to gender
The population studied was divided in 2 groups according to gender.The men-to-women ratio was 2:1 (381 men and 189 women). The characteristics of the patients are described in table 1. Age was similar in both genders but both BMI and AHI were higher in men than women. Significantly more men (AHI e" 30/h) (n: 145; 38%) than women (n: 24; 13%) had severe OSA (p<0.001). (p<0.001). The prevalence of asthma and psychiatric disorders was higher in women whilst the prevalence of cardiovascular co-morbidities was higher in men.
3.2.1. Added value of the MMAA in each gender
Correlations between SMM-RDI and AHI were excellent both in men (r: 0.95) and women (r: 0.94) and better than those between RDI and AHI: respectively r: 0.93 (p<0.001 for comparison between correlation coefficient of each methods) and r: 0.91 (p<0.001 for comparison between correlation coefficient of each methods).
[Figure 1]
[Figure 2] 
A Bland & Altman plot corroborated the analysis. Whilst RDI underestimated AHI, this was improved by adding MMAA. In women, mean underestimation with RDI was 8.0�9.1/h but only 3.4�7.4/h with SMM-RDI (p< 0.001). Limits of agreement were [-9.8; 26.0] for AHI-RDI and [-11.1; 18.0] for AHI - SMM-RDI in women. In men, mean underestimation with RDI was 11.0�9.1/h but 6.0�8.2/h with SMM-RDI (p<0.001). Limits of agreement were [- 6.9; 28.9] for AHI-RDI and [-10.2; 22.0] for AHI - SMM-RDI in men. 
[Figure 3 A, B] 
[Figure 4 A, B]
Respectively 337 (89%), 243 (64%) and 145 (38%) men had an AHI e"5/h, e"15/h and e"30/h. Respectively 123 (65%), 64 (34%) and 24 (13%) women had an AHI e"5/h, e"15/h and e"30/h.  Significantly more men had moderate or severe OSA (p<0.001). Accuracy characteristics of the method with or without MMAA for the different AHI cut-offs are described in table 2. We found a statistically significant improvement in sensitivity with the addition of MMAA (SMM-RDI) in both male and female at the three different AHI thresholds.
The values of Sp for moderate to severe and severe AHI were excellent for the automated methods with or without MMAA, both above 90% in both genders. For severe disease (AHIe"30/h), the addition of MMAA to simple RDI significantly improved Se (respectively 42 % for RDI; 71 % for SMM-RDI in women (p<0.001) and 54 % for RDI; 68 % for SMM-RDI in men (p<0.05). This occurred without a significant loss of Sp (table 2). 
Positive and negative likelihood ratios were similar for the 2 methods in both genders. Used in the screening of moderate to severe (AHIe"15/h) and severe (AHIe"30/h) disease, a positive test will clearly increase the likelihood of a disease and a negative test will markedly decrease the likelihood of disease. 
 [Table 2]
3.2.2. Gender specificities and comparison between men and women 
Snoring and apnea were significantly more frequently related by men (respectively 41% versus 25%; p<0.001 and 26% versus 6%; p<0.001). Men had more cardiovascular co-morbidities (14% versus 6%; p<0.005) but not hypertension (36 % men and 31% women; p: 0.091). Lack of sleep (reported difficulties initiating or maintaining sleep) was more frequently related by women (18% versus 5%; p<0.001) but this did not translate in more frequent use of benzodiazepines (18% in women versus 14 % in men; p: NS). Epworth sleepiness Score was not statistically different in men (10�5) and women (11�10; p: NS) for all the groups.  There was no correlation between AHI and ESS for the subsequent severity cut-offs in any gender group. There were significant gender differences in non-vascular co-morbidities such as asthma and psychiatric disorder (mainly depression). In both men and women, the correlation between AHI and BMI was statistically significant (p<0.001). Multiple regression showed that AHI was independently influenced by BMI (p<0.001), age (p<0.001) and gender (p<0.001).
Mean SpO2 was significantly smaller in men (94.0 � 5.2%) compared with women (95.2 � 6.7 %; p <0.001) and the time spent below 90% SpO2 during sleep was significantly higher in men (36.8 � 76.2 min) than women (14.8 � 56.3 min; p<0.001).
Men had a significantly higher mean RDI (17.7�19.6/h), and SMM-RDI (22.8�19.4/h) compared with women RDI (7.5�13.0/h) and SMM-RDI (12.1�14.4/h; p<0.001 for both). TST and TDT were not statistically different between genders, respectively 416�94 and 549�88 minutes in men, and 426�107 and 562�94 minutes in women.
The correlation coefficient of the relationship between AHI and RDI (respectively 0.91 and 0.93) and between AHI and SMM-RDI (respectively 0.94 and 0.95) did not significantly differ between women and men (table 3).
[Table 3]
Se of both automated methods were significantly lower in the women than men for the detection of both mild to severe (AHI e"5/h) (p<0.001 for RDI and p 0.005 for SMM-RDI) and moderate to severe (AHI e"15/h) OSA (p<0.05 for RDI and p<0.001 for SMM-RDI). Moreover, Sp of SMM-RDI for the detection of moderate to severe disease (AHI e"15/h) was significantly higher in female (p: 0.005) whilst remaining above 90 % in both genders. 
However, the Se (table 4) and Sp (table 5) of both automated methods did not significantly differ between genders for the detection of severe OSA (AHI > 30/h).
 [Table 4] [Table 5]
Discussion:
In this prospective study, we compared attended polysomnography to 2 automated methods, with or without MMAA provided by a distance meter and added to airflow and oxygen saturation analysis for the detection of respiratory events in 381 men and 189 women. A comparison between genders was performed. In both genders, correlations between RDI and AHI as well as Se were improved significantly by adding the MMAA (SMM-RDI).
In patients with severe disease (AHI e"30/h) SMM-RDI Se and Sp were similar in both genders. The comparison of the two automated methods between genders showed that the Se of both RDI and SMM-RDI was significantly smaller in women for mild to severe (AHI e"5/h) and moderate to severe (AHI e"15/h) diseases. The analysis of the mean SpO2 during sleep showed a significantly lower value in men (p<0.001). This was most probably explained by a significantly higher BMI in men. Accordingly, the higher Se observed of both automated methods in men for the diagnosis of both mild to severe and moderate to severe disease could be explained by a better detection of hypopnea associated with oxygen desaturation in this gender. Indeed, due to the curvilinear shape of the oxyhemoglobin dissociation curve, a hypopnea of similar duration is more likely to induce a significant desaturation if the initial SpO2 is lower. Moreover, as functional residual capacity is inversely correlated to BMI, a similar decrease in ventilation will be associated with a higher drop in PaO2 in more obese subjects. This could explain why the difference of Se between genders was observed with the RDI as well as the SMM-RDI. In men and women patients with severe disease (AHI e"30/h) SMM-RDI sensitivities are not significantly different because apneas and hypopneas are more frequent and probably longer, leading to desaturation or typical SMM. Moreover, for the SMM-RDI, another reason could be evoked for the gender differences in Se: the smaller amplitude of the mandible oscillations linked to respiratory effort in women. In our study, this movement which is thought to be a surrogate for cortical arousal is used in hypopnea recognition not defined by the presence of desaturation. The MMAA deals with absolute values and not values expressed as a percentage of the maximal amplitude of the voluntary mandible movement used for calibration. A SMM was considered as a relevant arousal for hypopnea definition if its maximal value was greater or equal to 1 (which corresponds to a movement of an absolute amplitude around 1mm) and twice greater than the average of the movement values during the related event. As described by Lewis et al., there are sex differences in mandibular opening distances which appear to be due to the combined effects of mandibular length and mandibular rotation [13]. The amplitude of the oscillatory movements of the mandible and the distance linked to the elevation of the mandible could differ in men and women. Accordingly, an adapted definition for SMM might be needed in women. More, a higher resolution of the signal to detect mandible oscillation could be necessary in women with mild and moderate disease. For patients suffering from severe disease (AHI e"30/h), the movements could have more amplitude, thus reducing the differences between genders in predictive parameters. 
As we believe that the mandible movement occurs passively, secondary to respiratory efforts [14], the REM sleep atonia may induce a reduction of respiratory effort [15] and mandible movement.  Since REM AHI is recognized as being higher in women than men [4, 5, 6] this might also explain a reduced sensitivity of the SMM-RDI for diagnosing OSA in women. 
The mean underestimation SMM-RDI � AHI was 3.4�7.4 in women and 6.0�8.2 in men. This difference was in proportion to the difference in AHI between the 2 genders (15.5�19.0/h in women and 28.6�24.0/h in men). There was also a significant difference in BMI between genders. The link between OSA and obesity has been frequently related in prospective cohort study and is unquestionable [12]. A multiple regression analysis showed that BMI, age and gender independently influenced AHI. In our cohort, the correlations between the three indexes were not significantly different between genders. 
Recent studies showed other gender differences in complaints, co-morbidities and polysomnographic features in OSA [2; 4 - 6, 18]. In the present study, the complaint of �lack of sleep� was more frequent in women without more frequent use of benzodiazepines than in men or significant differences in TST between genders measured on the PSG. The TST observed in the present population is in the expected range for age and gender [16, 17]. 
There could be a link between related psychiatric disorder reported by 27% women and 11% male (p<0.001) (mainly depression) and the complaint of lack of sleep in women (18% of her and 5 % in men). Insomnia due to mental disorder is more prevalent in women than in men. The main final diagnosis was insomnia/anxiety/depression in 75 patients (13%) among them 45 women (8%). But Shepertycky et al. found that women had a significantly higher incidence of depression (OR=4.6) and complaints of insomnia in case of sleep disordered breathing [18]. We didn�t use questionnaires to assess functional status and mood disturbance at baseline. This has been related in the medical history and based on history taking: it is difficult to distinguish, in this study, how these gender characteristics differences in complaints are linked to the identified dyssomnia.
The prevalence of hypertension is similar in both groups and not higher in men despite higher OSA severity index and prevalence. The reason is probably that hypertension is recognized as an indication for polysomnographic exploration and in this way, a selection bias. 
The present study has several limitations. The important difference between TDT and TST is probably associated with the fact that our population is composed of patients with complaints of bad sleep and submitted to �first night effect�.  The denominator used for RDI and SMM-RDI is the total dark time.  Different denominators explain variability in respiratory events index.  This is an important point, explaining higher AHI than RDI or SMM-RDI.   Despite the specific complaint of women, there is no gender significant difference in TST and TDT which does not contribute to the gender differences of indexes issued from automated analysis. 
Despite these anatomic and sleep related differences between genders, correlation of the SMM-RDI with AHI and the added value of MMAA to a simple portable device were excellent in both men and women.
Conclusions:
In the field of apneas and hypopneas screening devices, few data regarding women are currently available.
In our cohort including a large proportion of women, SMM-RDI correlations with PSG were excellent in both genders. The addition of MMAA to NAF and oxygen saturation analysis significantly improved the accuracy for the detection of respiratory events in both men and women. This added value occurred in both genders.
To improve the sensitivity of SMM-RDI in women, a specific SMM definition may be required.


















4. Acknowledgement:
Gis�le Maury is currently in a doctoral fellowship supported by the Fondation Mont Godinne. 

5. References: 
1. Young T, Palta M, Dempsey J, Skatrud J, Weber S et al. (1993) The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med  328: 1230-5.
2. Bixler EO, Vgontzas AN, Lin HM, Ten Have T, Rein J et al.(2001) Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med 163 (3Pt1):608-613.
3. Flemons WW, Littner MR, Rowley JA, Gay P, McDowell Anderson W et al. (2003) Home diagnosis of Sleep Apnea: a systematic review of the literature. Chest  124: 1543-1579.
4. Ye L, Pien G W, Ratcliffe S, Weaver TE (2009) Gender differences in obstructive sleep apnea and treatment response to continuous positive airway pressure. J Clin Sleep Med 5:512-518. 
5. O�Connor C, Thornley KS, Hanly PJ (2000) Gender differences in the polysomnographic features of obstructive sleep apnea. Am J Respir Crit Care Med 161:1465-1472.
6. Vagiakis E, Kapsimalis F, Lagogianni I, Perraki H, Minaritzoglou A et al.(2006) Gender differences on polysomnographic findings in Greek subjects with obstructive sleep apnea syndrome. Sleep Medicine 7:424-430.
7. Ware JC, McBrayer RH, Scott JA (2000) Influence of sex and age on duration and frequency of sleep apnea events. Sleep 23:165-170.
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18. Shepertycky MR, Banno K, Kryger MH (2005) Differences between men and women in the clinical presentation of patients diagnosed with obstructive sleep apnea syndrome. Sleep 28:309-314. 


















Table 1
WomenMenN189381IAH PSG � 15.5�19.0/h28.6�24.0/h<0.001 IAH e" 5 h-1  #
123 (65)337 (89) IAH e" 15 h-1  #
64 (34)243 (64)<0.001 IAH e" 30 h-1  #
24 (13)145 (38)<0.001RDI �7.5�13.017.7�19.6<0.001SMM-RDI � 12.1�14.422.8�19.4<0.001Age � 49�1350�13NSBMI (kg/m�) � 27�630�6<0.001Complaints  #
Snoring
sleepiness
apnea (witness)
tiredness
lack of sleep
not refreshing sleep
frequent awakenings
gasping
48(25)
61(32)
12(6)
40(21)
35(18)
9(5)
7(4)
1(0.5)
156(41)
114(30)
99(26)
60(16)
18(5)
15(4)
5(1)
8(2)
<0.001
NS
<0.001
NS
<0.001
NS
NS (0.063)
NSEpworth Sleepiness Scale � 11�1010�5NSKnown smoking habits  #
(Current/Former /Non smoker)179
49(27)/22(12)/108(60)360
85(24)/81(22)/194(54)NS
NSCurrent BzD use #35(18)55(14)NSHTA #55(31)137(36)NS (0.091)Chronic respiratory disease #
Asthma
 COPD
 Others*21(12)
14
4
337(10)
11
20
6NS
0.014
NS (0.077)
NSCardiovascular disease #11(6)53(14)<0.005Related psychiatric disorder #52(27)42(11)<0.001Neurological disease #
 Ischemic event/ MS/ Epilepsy/others**45(24)
4(9)/4(9)/2(4)/35(78)68(18)
17(25)/33(48)/7(10)/11(16)NSNose & Throat disease #
Uvulo-palatal surgery38(21)
3(2)73(19)
12(3)NS
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Table 2: 
WOMENAHI e" 5/hAHI e" 15/hAHI e" 30/hRDISMM-RDIpRDISMM-RDIpRDISMM-RDIpSe (% - CI 95%)51
[42-60]88
[82-93]<0.00141
[29-54]59
[46-71]<0.00142
[22-63]71
[49-87]P<0.05Sp (% - CI 95%)98
[92-100]75
[63-84]<0.001100
[97-100]99
[96-100]<0.00199
[97-100]99
[96-100]NSLR + (% - CI 95%)34.32
[4.87-242]3.46
[2.28-5.25]74.81
[10.5-533]69.17
[9.26-516.53]58.79
[14.48-238.72]LR - (% - CI 95%)0.50
[0.41-0.59]0.16
[0.10-0.27]0.59
[0.48-0.73]0.41
[0.3-0.55]0.59
[0.42-0.82]0.30
[0.16-0.55]
MENAHI e" 5/hAHI e" 15/hAHI e" 30/hRDISMM-RDIpRDISMM-RDIpRDISMM-RDIpSe (% - CI 95%)76
[71-81]95
[92-97]<0.00158
[52-64]79
[74-84]<0.00154
[45-62]68
[59-75]<0.001Sp (% - CI 95%)93
[81-98]67
[51-81]<0.00198
[94-100]92
[86-96]<0.01100
[98-100]100
[98-100]NSLR + (% - CI 95%)10.93
[3.66-32.61]2.93
[1.90-4.50]26.5
[8.61-81.56]9.89
[5.60-17.50]158.83
[22.39-1126.52]LR - (% - CI 95%)0.26
[0.21-0.31]0.07
[0.04-0.12]0.43
[0.37-0.50]0.22
[0.17-0.29]0.46
[0.39-0.55]0.33
[0.26-0.41]

- Table 2: Sensitivity, Specificity and likelihood ratios derived from the 2 methods and compared to simultaneous polysomnography in women and men. There is a statistically significant improvement in sensitivity with the addition of MMAA (SMM-RDI) in both male and female at the three different AHI thresholds. The values of Sp for moderate to severe and severe AHI were excellent for the automated methods with or without MMAA, both above 90% in both genders. AHI: apnea hypopnea index (provided by the PSG). RDI: respiratory disturbance index (nasal airflow and SpO2). SMM-RDI: respiratory disturbance index based on nasal airflow, SpO2 and MMAA. Se: sensitivity; Sp: specificity; LR +: positive likelihood ratio; LR -: negative likelihood ratio. Exact confidence intervals are based on F distribution. NS: Not Significant.
Table 3 
WOMEN (190)MEN (380)Comparison between the 2 groupsAHI (h-1)15.5 � 1928.6�24.0<0.001TST426�107416�94NSTDT562�94549�88NSRDI (h-1)7.5�13.017.7�19.6<0.001Dif AHI-RDI8.0 � 9.111.0 � 9.1<0.001B&A Limits of agreement[-9.8; 25.9][-6.9; 28.9]Pearson correlation r0.910.93NSSMM-RDI (h-1)12.1�14.422.8�19.4<0.001Dif AHI- SMM-RDI3.4 � 7.46.0 � 8.2<0.001B&A Limits of agreement[-11.1; 18][-10.2�; 22]Pearson correlation r0.940.95NS
Table 3: Correlations, Bland & Altman difference and limits of agreement in each gender which are compared to each other. AHI: apnea hypopnea index (provided by the PSG).TST: total sleep time. TDT: total dark time. RDI: respiratory disturbance index (nasal airflow and SpO2). Dif AHI-RDI: the difference between AHI and RDI. B&A: Bland & Altman. SMM-RDI: respiratory disturbance index based on nasal airflow, SpO2 and MMAA. (+,-����ID<77gd5z�d�gd5z�gd���kd�$$If�l��\����'�#
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la�yt�b�>�EFH����T�T�T�TUUUUUVVV(V*V4V8VRVTV^VbVvVxV�V�V�V�V�V�V�V�V�V�V�V�V�VWW�W�W�W�W�W�W������������������������������������������������h��CJH*aJh��h��CJaJh��5�CJaJh��B*CJaJmH	phsH	h��CJaJmH	sH	U"h5z�B*CJH*aJmH	phsH	h5z�B*CJaJmH	phsH	h5z�B*CJaJmHphsHh5z�CJaJmH	sH	4Dif AHI- SMM-RDI: the difference between AHI and SMM-RDI.  NS: not significant. All the data are Mean � SD except limits of agreement and r pearson correlation with AHI.
Table 4 Comparisons of AHI-RDI and AHI-SMM-RDI Sensitivities between women and men.
SensitivityWOMENMENPRDIe"5 h-15176<0.001e"15 h-14158<0.05e"30 h-14254NSSMM-RDIe"5 h-188950.005e"15 h-15979<0.001e"30 h-17168NS
Table 5 Comparisons of AHI-RDI and AHI-SMM-RDI Specificities between women and men. 
SpecificityWOMENMENPRDIe"5 h-�TUUUUUUUUU����S�����kds>$$If�l���\��R������
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