Portal de Periódicos da CAPES

Effect of Three Hypopnea Scoring Criteria on OSA Prevalence and Associated Comorbidities in the General Population

2019; American Academy of Sleep Medicine; Volume: 15; Issue: 02 Linguagem: Inglês

10.5664/jcsm.7612

1550-9397

Camila Hirotsu, José Haba‐Rubio, Daniela Andries, Nadia Tobback, Pedro Marques‐Vidal, Péter Vollenweider, Gérard Waeber, Raphaël Heinzer,

Sleep and Wakefulness Research

Free AccessObstructive Sleep Apnea - Women - Metabolic Syndrome - Diabetes - Hypertension - Scientific InvestigationsEffect of Three Hypopnea Scoring Criteria on OSA Prevalence and Associated Comorbidities in the General Population Camila Hirotsu, PhD, Jose Haba-Rubio, MD, Daniela Andries, Nadia Tobback, Pedro Marques-Vidal, MD, PhD, Peter Vollenweider, MD, Gérard Waeber, MD, Raphael Heinzer, MD, MPH Camila Hirotsu, PhD Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland *Contributed equally to this study as co-first authors Search for more papers by this author , Jose Haba-Rubio, MD Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland *Contributed equally to this study as co-first authors Search for more papers by this author , Daniela Andries Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland Search for more papers by this author , Nadia Tobback Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland Search for more papers by this author , Pedro Marques-Vidal, MD, PhD Department of Internal Medicine, University Hospital of Lausanne (CHUV), Lausanne, Switzerland Search for more papers by this author , Peter Vollenweider, MD Department of Internal Medicine, University Hospital of Lausanne (CHUV), Lausanne, Switzerland Search for more papers by this author , Gérard Waeber, MD Department of Internal Medicine, University Hospital of Lausanne (CHUV), Lausanne, Switzerland Search for more papers by this author , Raphael Heinzer, MD, MPH Address correspondence to: Raphael Heinzer, Center for Investigation and Research in Sleep, University Hospital of Lausanne (CHUV), Lausanne University, Switzerland, 1011 Lausanne, Switzerland+41 21 314 67 54+41 21 314 67 52 E-mail Address: [email protected] Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland Pulmonary Department, University Hospital of Lausanne (CHUV), Lausanne, Switzerland Search for more papers by this author Published Online:February 15, 2019https://doi.org/10.5664/jcsm.7612Cited by:18SectionsAbstractPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Apnea-hypopnea index (AHI) is the main polysomnographic measure to diagnose obstructive sleep apnea (OSA). We aimed to evaluate the effect of three standard hypopnea definitions on the prevalence of OSA and its association with cardiometabolic outcomes in the general population.Methods:We analyzed data from the HypnoLaus study (Lausanne, Switzerland), in which 2,162 participants (51% women, 57 ± 19 years) underwent in-home full polysomnography. AHI was calculated using three hypopnea definitions: AASM1999 (≥ 50% decrease in airflow or lower airflow reduction associated with oxygen desaturation ≥ 3% or an arousal), AASM2007 (≥ 30% airflow reduction associated with ≥ 4% oxygen desaturation), and AASM2012(≥ 30% airflow reduction associated with ≥ 3% oxygen desaturation or an arousal). Participants underwent clinical assessment for hypertension, diabetes, and metabolic syndrome.Results:Median AHI of AASM1999, AASM2007 and AASM2012 criteria were 10.9, 4.4, and 10.1 events/h, respectively. OSA prevalence defined as AHI ≥ 5, ≥ 15, and ≥ 30 events/h was 74.5%, 39.3%, and 16.3% using AASM1999; 46.9%, 18.8%, and 6.8% using AASM2007; and 72.2%, 36.6%, and 14.9% using AASM2012. Different AHI thresholds derived from AASM1999, AASM2007, and AASM2012 criteria, respectively, were associated with hypertension (11.5, 4.8, 10.7 events/h), diabetes (15.7, 7.1, 14.4 events/h), and metabolic syndrome (12.8, 5.5, 11.8 events/h).Conclusions:Hypopnea definition has a major effect on AHI and on OSA prevalence in the general population and, hence, important implications for public health policies. There is a twofold difference in the threshold above which an association with diabetes, hypertension, and metabolic syndrome is observed using AASM2007 compared to AASM1999 or AASM2012 criteria.Citation:Hirotsu C, Haba-Rubio J, Andries D, Tobback N, Marques-Vidal P, Vollenweider P, Waeber G, Heinzer R. Effect of three hypopnea scoring criteria on OSA prevalence and associated comorbidities in the general population. J Clin Sleep Med. 2019;15(2):183–194.BRIEF SUMMARYCurrent Knowledge/Study Rationale: The American Academy of Sleep Medicine's rules for scoring hypopneas have changed thrice over the past 20 years, but their clinical effects on the prevalence of obstructive sleep apnea and their association with hypertension, diabetes, and metabolic syndrome in the general population are unknown.Study Impact: Our findings indicate that the method used for scoring hypopneas significantly influences the prevalence of obstructive sleep apnea and its association with cardiometabolic outcomes. We could provide predictive equations to translate the differences in apnea-hypopnea indexes within three recommended criteria of the American Academy of Sleep Medicine in a general population-based sample. Further, this study highlights the need for standardization of the scoring method to allow compatibility among epidemiological studies.INTRODUCTIONObstructive sleep apnea (OSA) is characterized by recurrent partial (hypopneas) or total (apneas) episodes of upper airway obstruction during sleep,1 and has been widely recognized as an important and treatable risk factor for cardiovascular and metabolic conditions.2,3 Polysomnography (PSG) is considered the gold standard for identifying individuals with OSA. Among the several parameters measured by PSG, the apneahypopnea index (AHI), which comprises the average number of apneas and hypopneas per hour of sleep, is the main metric for diagnosing OSA, as well as for assessing the disease severity and responsiveness to the treatment. A recent systematic review showed that, in general populations, OSA prevalence as AHI ≥ 5 events/h varies considerably among studies (9% to 38%).4 Higher estimates were also observed over time in the most recent epidemiological studies.5 Among the possible explanations for these findings, ethnical composition, equipment-related issues, and changes in respiratory events definition might have an important role.Indeed, several definitions of hypopnea have been used in research and the clinical setting, leading to large interlaboratory variations in AHI depending on the definition used.6–10 These differences relate to the degree of airflow reduction, the amplitude of oxygen desaturation, and the association with electroencephalographic arousal required to define a hypopnea. Because OSA diagnosis and treatment decision are largely based on AHI, the definition of hypopnea may have a direct effect on OSA prevalence estimates and the patients' management. Thus, the aim of our study was to evaluate the effect of three hypopnea definitions on the prevalence and severity of OSA in a middle-aged general population sample. We compared three standard definitions for identifying hypopneas: the 1999 American Academy of Sleep Medicine (AASM) criteria (also known as Chicago criteria),11 and the recommended definitions from The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (AASM Scoring Manual) in 200712 and 2012.13 Next, we sought to establish the AHI thresholds that could predict the presence of hypertension, diabetes, and metabolic syndrome as well as to compare the association between OSA severity and these cardiometabolic outcomes using each hypopnea definition.METHODSPopulation SampleThis is a cross-sectional study that analyzed data from HypnoLaus, a population-based sleep cohort study (Lausanne, Switzerland) performed between September 1, 2009 and June 30, 2013. HypnoLaus participants were recruited among individuals from the CoLaus/PsyCoLaus cohort.14 CoLaus/PsyCo-Laus is a population-based cohort of 6,734 participants (52.5% women) aged 35 to 75 years, identified from a random sample of all age-eligible adults living in the city of Lausanne, Switzerland (117,161 habitants). The CoLaus/PsyCoLaus study was conducted to assess the prevalence of cardiovascular risk factors and to identify new determinants of these risk factors and their association with mental disorders.15 For the HypnoLaus nested study, participants of the CoLaus/PsyCoLaus study were invited to answer sleep questionnaires regarding their sleep habits and potential sleep disorders, and the first consecutive 3,043 were contacted to have a full sleep study at home. Of these, 71% (n = 2,168) accepted the invitation and underwent PSG, among which 3% (n = 60) had technical problems. Of these, 6 participants declined to undergo a second PSG, and 54 participants agreed.16 Therefore, 2,162 PSG recordings composed the HypnoLaus cohort and were included in this study. The Institutional Review Board in Lausanne approved the study, and all participants gave their written informed consent.Clinical Data CollectionParticipants from HypnoLaus study were invited to attend the outpatient clinic at the University Hospital of Lausanne (Centre Hospitalier Universitaire Vaudois [CHUV], Lausanne, Switzerland). After an overnight fasting they were also invited for questionnaires completion, clinical assessment, and blood samples collection. Body weight and height were measured using a calibrated scale and a vertical stadiometer, respectively (Seca, Hamburg, Germany). Body mass index (BMI) was calculated as body mass in kg divided by the square of the participant's height in meters. Waist circumference (at the level of the umbilicus) was measured to within 0.5 cm with plastic tape. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were evaluated in triplicate on the left arm at 5-minute intervals with the participant seated and resting for at least 10 minutes using a calibrated automated oscillometric sphygmo-manometer (Omron HEM-907, Matsusaka, Japan).17 Overnight fasting blood samples were taken of each participant. Glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides were quantified by colorimetric assays as previously described.14 These assays were performed on fresh blood samples by the CHUV Clinical Laboratory (Lausanne, Switzerland).PolysomnographyIn-home overnight full PSG was performed, using a digital portable sleep-wake recording system (EMBLA Titanium, Embla Systems, Inc, Broomfield, Colorado, United States). A trained technician hooked up the participant in the CIRS facility (Center for Investigation and Research in Sleep, CHUV, Lausanne, Switzerland). The electrodes and recorder were installed at the laboratory and recordings were done in the normal home environment. PSG measurements included: electroencephalograms (EEG) from frontal, central, and occipital areas (F3-M2, C3-M2, O1-M2, F4-M1, C4-M1, O2-M1) according to the international 10/20 electrode configuration system, right and left electrooculograms, mental-submental electromyogram (EMG), right and left leg EMG, thoracic and abdominal breathing movements by respiratory inductance plethysmography, respiratory airflow by a nasal-cannula connected to a pressure transducer, oxygen saturation (SpO2) by pulse oximetry, heart rate by electrocardiogram (ECG), and body position.Scoring of PSGPSG test results were scored using Somnologica software (Embla Systems, Inc) by two experienced scorers (DA, NT), with an interagreement concordance greater than 90%. Sleep, arousal, and movements during sleep were scored based on the 2007 AASM Scoring Manual.12 Concerning respiratory events, an apnea was defined by a complete or almost complete (> 90%) cessation of airflow (measured by nasal pressure) lasting 10 seconds or longer. Hypopneas were initially scored based on the Chicago criteria (AASM1999), being defined by criterion 1 or 2, plus criterion 3: A clear decrease (> 50%) of airflow amplitude from the baseline. Baseline was defined as the mean amplitude of stable breathing and oxygenation in the 2 minutes preceding onset of the event (in individuals who have a stable breathing pattern during sleep) or the mean amplitude of the three largest breaths in the two minutes preceding onset of the event (in individuals without a stable breathing pattern).A clear reduction of airflow amplitude, not reaching the aforementioned criterion but associated with either an oxygen desaturation of ≥ 3% or an arousal. From an operational standpoint, a discernible reduction in airflow was considered a > 30% reduction in the airflow signal.The event lasts 10 seconds or longer. An AHI for each individual was calculated, consisting of the number of apneas and hypopneas per hour of sleep. Each recording was reviewed for validation of the respiratory scoring by a single investigator (JHR).Because both recommended 2007 and 2012 AASM criteria represent a subset of Chicago criteria's events, we derived the other two AHI definitions from the first scoring. For the recommended 2007 AASM Scoring Manual criteria (AASM2007), we removed hypopneas that did not fulfill the stricter AASM hypopnea definition, that is, hypopneas that were not associated with ≥ 4% oxygen desaturation. For the recommended 2012 AASM Scoring Manual criteria (AASM2012), we removed hypopneas that were not associated with either an arousal or a ≥ 3% oxygen desaturation (Table S1).OSA severity was classified according to standard criteria as mild (AHI 5 to < 15 events/h), moderate (AHI 15 to < 30 events/h), and severe (AHI ≥ 30 events/h). AHI < 5 events/h was defined as no-OSA.OutcomesHypertension was defined as a SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, and/or use of antihypertensive medication. Diabetes was considered positive when fasting plasma glucose levels were ≥ 7.0 mmol/L or there was use of antidiabetic medication.18 Metabolic syndrome was defined according to the Joint Interim Statement,19 as the presence of at least three risk factors among: high blood pressure (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg or use of antihypertensive medication); visceral obesity (waist circumference ≥ 88 cm in women or ≥ 102 cm in men); high triglycerides (≥ 1.7 mmol/L, or use of fibrates or nicotinic acid); low HDL levels (< 1.30 mmol/L in women or < 1.03 mmol/L in men, or use of fibrates or nicotinic acid); and high fasting plasma glucose (≥ 5.6 mmol/L or use of anti-diabetic medication).Statistical AnalysisWe used AHI as the primary variable in this study for analysis of the differences between hypopnea scoring criteria. AHI is displayed according to the median and interquartile range (IQR) values. The differences between the three different AHIs obtained through AASM1999, AASM2007, and AASM2012 criteria were assessed using the Friedman test, with pairwise comparisons performed by Wilcoxon signed-rank test. Bar graphs were constructed to represent the prevalence of OSA using each AASM criteria at AHI thresholds of ≥ 5, ≥ 15, and ≥ 30 events/h. Equivalent thresholds for the three AHIs were estimated using the receiver operating characteristic curve (ROC) with each AHI as the gold standard, giving equal weight to maximize both sensitivity and specificity. Bland-Altman plots representing the mean difference between each pair of AHIs according to each scoring criteria were built to illustrate the agreement between AHIs. Conversion equations between each pair of AHI were established with linear or quadratic regressions, with the latter being employed when appropriate. AHI thresholds significantly associated with the presence of hypertension, diabetes, and metabolic syndrome were estimated by ROC analysis using each hypopnea criteria. Multivariable logistic regression was used for testing the association between OSA severity (mild, moderate, or severe OSA versus no-OSA) and the presence of hypertension, diabetes, and metabolic syndrome using each scoring criteria.RESULTSPopulation Sample and OSA PrevalenceThe patient characteristics and PSG results are shown in Table 1. Participants were 57.2 ± 19.2 (median ± IQR) years of age with a BMI of 25.7 ± 5.4 kg/m2. The sample included 51% of women. The average total sleep time was 404.0 ± 89.0 minutes and the sleep efficiency was 87.6 ± 13 %.Table 1 Sample characteristics.Table 1 Sample characteristics.The prevalence of OSA in the HypnoLaus cohort based on different AASM scoring criteria is presented in Figure 1. When using AHI thresholds of ≥ 5, ≥ 15, and ≥ 30 events/h, respectively, AASM2007 would provide 34.9%, 48.6%, and 54.2% lower OSA diagnosis rate compared to AASM2012 as well as 37.0%, 52.1%, and 58.1% lower OSA diagnosis rate compared to AASM1999 criteria. Compared to AASM1999 criteria, AASM2012 would provide 3.2%, 6.8%, and 8.5% lower OSA diagnosis rate when using the same AHI thresholds.Figure 1: Prevalence of obstructive sleep apnea.Prevalence of apnea-hypopnea index (AHI) thresholds ≥ 5, ≥ 15 and ≥ 30 events/h according to three different recommended American Academy of Sleep Medicine (AASM) criteria (1999, 2007 and 2012) in the whole (A), male (B), and female (C) sample of HypnoLaus cohort (n = 2,162).Download FigureAHI AgreementTable 2 shows median ± IQR of AHI in the HypnoLaus population according to the three scoring criteria. All AHIs were significantly different from each other (P < .001) by Friedman test and post hoc comparisons. The median AHI of AASM2007 and AASM2012 criteria were approximately 40% and 93% of the median AHI of AASM1999. In turn, the median AHI of AASM2007 was approximately 44% of the median AHI of AASM2012. Bland-Altman plots (Figure 2) show the agreement between each pair of AHI according to the different scoring criteria. The variation in the mean AHI difference between each pair of scoring criteria was greater according to the AHI magnitude, except for the comparison between AASM1999 and AASM2012 criteria, which was more stable. The Bland-Altman plots demonstrate a mean increase of 6.4 events/h when comparing AHI definitions between AASM2007 and AASM2012; a mean reduction of 0.9 events/h when comparing AASM2012and AASM1999; and a mean reduction of 7.3 events/h when comparing AASM2007 and AASM1999 criteria.Table 2 Apnea-hypopnea index changes according to hypopnea definitions in the HypnoLaus cohort.Table 2 Apnea-hypopnea index changes according to hypopnea definitions in the HypnoLaus cohort.Figure 2: AHI agreement among different hypopnea criteria.Bland-Altman plots demonstrating the level of agreement for apneahypopnea index (AHI) between American Academy Sleep Medicine (AASM) criteria from 2012 versus 2007, 2012 versus 1999, and 2007 versus 1999, respectively in the HypnoLaus cohort (n = 2,162). The dashed line represents the mean difference between the two measured AHIs and the dotted lines represent the mean difference ± 1.96 standard deviation (SD). The x-axis presents the mean of the two AHIs and the y-axis presents the difference (Δ) between the two measurements.Download FigureEquivalent AHIs and Prediction EquationsTable 3 shows the equivalence between the scoring criteria for each AHI threshold. For instance, when using AASM2012 according to AHI thresholds of ≥ 5, ≥ 15 and ≥ 30 events/h, to achieve a similar OSA prevalence AASM2007 would have to shift its thresholds down to about 2.0, 6.6, and 14.9 events/h, respectively. In male and female subsamples, the equivalent AHI threshold conversion factors were similar to those of the whole sample.Table 3 Equivalent apnea-hypopnea index thresholds for different hypopnea definitions in the HypnoLaus cohort.Table 3 Equivalent apnea-hypopnea index thresholds for different hypopnea definitions in the HypnoLaus cohort.Prediction equations to determine the relationship between each pair of AHI are represented in Figure 3. All equations showed a R2 ≥ 0.90 as high collinearity was present between criteria (R = 0.996 for correlation between AASM1999 and AASM2012 derived AHI; R = 0.959 for correlation between AASM2007and AASM2012 derived AHI; and R = 0.951 for correlation between AASM2007 and AASM1999 derived AHI). In the prediction equations between AASM2007 and AASM2012 as well as AASM2007 and AASM1999, a quadratic regression better fitted the relationship, but with a small increase in R2 of 0.02 compared to the linear regression.Figure 3: AHI equivalence equations among different hypopnea criteria.Least squares regression showing the apnea-hypopnea indexes (AHIs) equations of equivalence according to the different recommended American Academy of Sleep Medicine (AASM) criteria in the HypnoLaus cohort (n = 2,162).Download FigureEffect of Scoring Criteria on the Association Between OSA and OutcomesThe AHI thresholds significantly associated with hypertension, diabetes, and metabolic syndrome according to the three scoring criteria are represented in Table 4. For all outcomes assessed, the area under the curves (AUCs) as well as the sensitivity and specificity were similar among the three scoring criteria. Overall, higher AHI thresholds emerged from AASM1999, followed by AASM2012. AHI thresholds derived from AASM2007 criteria were approximately half of the respective AHI thresholds derived from both AASM1999 and AASM2012.Table 4 Equivalent apnea-hypopnea index thresholds associated with cardiometabolic outcomes according to different hypopnea criteria in the HypnoLaus cohort.Table 4 Equivalent apnea-hypopnea index thresholds associated with cardiometabolic outcomes according to different hypopnea criteria in the HypnoLaus cohort.The association between OSA severity and the presence of cardiometabolic outcomes adjusted for age, sex, and BMI is represented in Figure 4. For hypertension, we observed an independent association with severe OSA versus no-OSA using both AASM2012 (odds ratio [OR] = 1.46, 1.01–2.10) and AASM1999 (OR = 1.55, 1.08–2.22), but not AASM 2007 (OR = 1.30, 0.83–2.05). For diabetes, there was significant associations with all OSA groups versus no-OSA in both AASM2012 (mild OSA: OR = 2.13, 1.16–3.91; moderate OSA: OR = 2.31, 1.23–4.32; severe OSA: OR = 2.44, 1.28–4.68) and AASM1999 (mild OSA: OR = 1.92, 1.02–3.62; moderate OSA: OR = 2.34, 1.23–4.43; severe OSA: OR = 2.35, 1.21–4.55). Regarding AASM2007, diabetes was significantly associated with moderate (OR = 1.61, 1.02–2.56) and severe OSA (OR = 1.81, 1.07–3.05), but not with mild OSA (OR = 1.46, 0.99–2.17) when compared to no-OSA.Figure 4: Association between AHIs and cardiometabolic outcomes.Odds ratio and 95% confidence intervals of the association between apnea-hypopnea index (AHI) categories (AHI 5 to < 15, AHI 15 to < 30, and AHI ≥ 30 events/h compared to AHI < 5 events/h) derived from three different recommended AASM criteria (1999, 2007, and 2012) and the presence of hypertension (n = 2,147), diabetes (n = 2,147) and metabolic syndrome (n = 2,149) in the HypnoLaus cohort. Data analyzed with multivariable logistic regression adjusted for age, body mass index, and sex.Download FigureFor metabolic syndrome, we found significant associations with all OSA groups versus no-OSA using the three scoring criteria as follows: AASM2012 (mild OSA: OR = 1.58, 1.12–2.22; moderate OSA: OR = 1.68, 1.16–2.43; severe OSA: OR = 2.38, 1.58–3.60); AASM2007 (mild OSA: OR = 1.48, 1.13–1.93; moderate OSA: OR = 1.66, 1.17–2.35; severe OSA: OR = 2.26, 1.42–3.59); AASM1999 (mild OSA: OR = 1.63, 1.14–2.33; moderate OSA: OR = 1.68, 1.15–2.46; severe OSA: OR = 2.41, 1.59–3.64).There was no association between the different AHI thresh olds and self-reported sleepiness (Epworth Sleepiness Scale) regardless the hypopnea scoring criteria used (Figure S1 and Table S2).DISCUSSIONTo the best of our knowledge, this is the first study that explored the extent to which different hypopnea definitions influence OSA recognition and its association with cardiometabolic outcomes in a general population sample. Using three standard hypopnea definitions with airflow assessed by nasal cannula pressure transducer, we could provide conversion equations between AHIs and equivalent thresholds among the scoring criteria. Our results show up to twofold difference in AHI level above which an association was found with cardiometabolic outcomes between AASM2007 and AASM1999/AASM2012. Last, an independent association between severe OSA and hypertension was only found using AASM1999 or AASM2012 criteria after adjusting for confounders.To date, all studies that aimed to evaluate the association between OSA and metabolic syndrome,20–24 diabetes,25–31 or hypertension32–40 have used older AASM criteria (AASM1999/ AASM2007) or non-AASM criteria for hypopnea scoring, hampering a reliable comparison among them. In addition to the differences in scoring criteria, important heterogeneity in study design, sample size, outcome definition, and demographic factors may have played a role in the inconsistent results found, as shown in Table 5, Table 6, and Table 7.Table 5 Population-based studies on the association between obstructive sleep apnea and metabolic syndrome.Table 5 Population-based studies on the association between obstructive sleep apnea and metabolic syndrome.Table 6 Population-based studies on the association between obstructive sleep apnea and diabetes.Table 6 Population-based studies on the association between obstructive sleep apnea and diabetes.Table 7 Population-based studies on the association between obstructive sleep apnea and hypertension.Table 7 Population-based studies on the association between obstructive sleep apnea and hypertension.Previous studies have examined the agreement between different approaches to determine AHI, showing significant differences in OSA frequency and mean AHI scores using different hypopnea scoring definitions.41,42 These studies were conducted in small clinical populations using a thermistor to assess airflow. It is also important to highlight that these results may not be relevant any longer considering the technical differences with current clinical practice, as most sleep centers use the nasal-cannula pressure transducer to evaluate hypopneas.As in our study, Ruehland et al. compared AHIs derived from AASM1999 criteria, AASM2007 recommended criteria, and AASM2007 alternative criteria (≥ 50% airflow reduction associated with 3% desaturation or an arousal).10 They explored the effect of hypopnea definition on the prevalence of OSA in a clinical population using nasal pressure for airflow assessment and demonstrated that using these standard hypopnea definitions leads to marked differences in AHI. For example, the median AHI obtained using the AASM2007 recommended criteria for scoring hypopneas was approximately 30% of the median AHI obtained using the AASM1999 criteria. In our study, this correspondence was a little higher, 40% of the AASM1999 criteria and could be explained by the differences in the source of sample used (clinical versus general population), the sample size (328 versus 2,162), and the more liberal interpretation of “discernible” difference in flow used by the authors.10Few studies have evaluated the effect of different hypopnea scoring criteria in population-based research samples. Redline et al. analyzed data from the Sleep Heart Health Study.43 They examined the effect of using 11 different criteria for scoring hypopneas on the prevalence of disease and found that the median values of respiratory disturbance index (RDI) varied by approximately tenfold for definitions that used the most liberal criteria for event identification (using amplitude changes without any requirement for associated desaturation or arousal) to the most conservative definition (requiring an associated > 5% desaturation with amplitude changes).Our findings showing that the AASM2012 recommended criteria results in higher AHI compared to the AASM2007 recommended criteria and doubles approximately the number of patients in whom OSA is diagnosed are in agreement with previous studies.8,9 BaHammam et al.9 showed that the AHI derived from AASM2007 recommended was 62% lower than the AASM2012 recommended criteria. Considering the AHI thresholds of ≥ 5, ≥ 15, and ≥ 30 events/h, they found that the AASM2007 recommended criteria underestimated by 45%, 52%, and 32%, respectively, the patients with OSA identified by the AASM2012 recommended criteria. In our study, we found a lower underdiagnosis rate for mild (34.9%) and moderate OSA (48.6%), but higher for severe OSA (54.2%). This could be because they used a clinical sample suspected for OSA, in which the proportion of OSA severity was overestimated compared to a general population sample.In a retrospective study performed in 112 consecutive patients of an Australian clinical sleep laboratory in a tertiary hospital, Duce et al.8 investigated the equivalent AHI thresholds regarding the AASM1999, AASM2012 recommended, and both the AASM2007 recommended and alternative criteria. To achieve the same OSA prevalence as the AASM2012 recommended criteria at AHI thresholds of ≥ 5, ≥ 15, and ≥ 30 events/h, they showed that the AASM2007 recommended criteria would have to change their thresholds to 2.6, 7.2, and 14.1 events/h, respectively and the AASM1999 criteria to 7.3, 17.4, and 31.7 events/h, respectively. Overall, these findings are in agreement with our results from a general population sample. They also established equations to convert the different AHIs to one another, which were close to ours.In terms of clinical relevance, our study additionally compared the association of OSA, based on diffe