Sleep complaints are common in hemodialysis (HD) patients, with a reported prevalence of 50 to 80% [
The reported prevalence of obstructive sleep apnea (OSA) in HD populations is highly variable, ranging from 20 to 80%, owing to differences in the studied populations, diagnostic tools, and OSA definitions [
Despite these large differences, the prevalence of OSA in HD patients appears to be remarkably higher than in the general population, which has been reported to be 5 to 34% [
OSA causes a disruption of sleep, leading to daytime symptoms, such as excessive daytime sleepiness [
The clinical presentation of OSA in HD patients differs from its presentation in patients without end-stage renal disease (ESRD). As such, classical symptoms such as loud snoring seem to be less often present, whereas others (hypertension and fatigue) are common in hemodialysis patients [
The aims of this study were to assess the prevalence of OSA in a European HD population to evaluate the predictive value of classical screening tools and to develop a specific diagnostic algorithm for HD patients.
Between June 2012 and June 2013, all the patients attending 6 hemodialysis centers in the western part of Switzerland were offered to be screened for OSA if they fulfilled the inclusion criteria. Participating centers were one university hospital department, 4 hemodialysis units located in peripheral hospitals, and one private dialysis center.
Inclusion criteria were being on chronic intermittent hemodialysis, age ≥18 years, and agreement to participate in the study. Patients were excluded if they had decompensated congestive heart failure or cognitive impairment/active psychiatric disease limiting their ability to understand the questionnaires.
We conducted a multicenter, cross-sectional population study.
Each participant underwent a nocturnal polygraphy and completed a set of questionnaires.
Anthropometric parameters were measured; patients performed a 24 h urine collection to quantify residual diuresis and underwent a complete laboratory analysis.
The study complied with the Declaration of Helsinki and was approved by the Institutional Ethics Committee (Commission d’éthique de la recherche clinique, Lausanne, Switzerland). All participants provided written informed consent.
Patients completed a set of questionnaires during the dialysis session.
Daytime sleepiness was evaluated by the
The screening scores for OSA were Berlin Questionnaire, STOP-BANG score, and Adjusted Neck Circumference.
The
The
All respiratory events were manually scored by the same experienced pulmonologist (AO) according to the American Academy of Sleep Medicine criteria [
Weight was measured at the end of the hemodialysis session to reflect nutritional status, in light indoor clothing without shoes, using a calibrated Seca scale and height was measured to the nearest centimeter using a wall-mounted stadiometer.
The hemodialysis schedule was not altered by the participation in the present study. All the patients underwent thrice weekly hemodialysis; HD sessions of the participating centers were performed during the morning or afternoon; there were no evening shifts. The hemodialysis efficacy was assessed using urea kinetic modelling and expressed as equilibrated Kt/V (eKt/V), according to the KDOQI recommendations [
Statistical analysis was conducted using Stata 11.0 for Windows (StataCorp LP, College Station, TX, USA).
The performance of the different screening tools to predict the presence of OSA, as diagnosed by PG, was assessed by computing sensitivity, specificity, and positive and negative predictive values.
A study population of 100 subjects was calculated as necessary to detect OSA with 80% sensitivity with 5% alpha error and 10% precision, assuming a 57% prevalence of OSA [
ROC analysis was performed to evaluate the overall performance of the screening tests. Subjects with Cheyne-Stokes respiration were not considered in the evaluation of the OSA screening tools.
In order to develop and validate a new diagnostic algorithm for OSA, the patients were divided in a derivation population (all patients from the hemodialysis unit of the University Hospital of Lausanne, the main study center) and an independent validation population (all patients from the other centers).
A predictive logistic regression model was fitted to the derivation set, including all the variables present in classical screening scores: age, gender, BMI, neck circumference, hypertension, daytime sleepiness, snoring, and unrefreshing sleep (Model 1). A second prediction model (Model 2) was fitted using the most significant factors from Model 1 (with
The factors identified in Model 2 were entered in a classification and regression tree analysis (CART, a chi-square based nonparametric technique) to evaluate the best discriminatory factors and cut-offs in a classification algorithm of the observations.
Based on the results of the CART analysis, we developed a screening algorithm to identify the patients at risk for OSA and we assessed its performance by computing sensitivity, specificity, positive and negative predictive values, and area under ROC curve in the validation population.
Of the 235 screened candidates, 35 were not eligible and 75 declined to participate.
125 patients were included in the study. 104 subjects (66 men and 38 women) completed home PG and were considered for the final analysis. Reasons for dropout were withdrawal of consent (13), technical problems with the PG recording (3), and loss to follow-up before completing PG (3 patients died, 1 was transplanted, and 1 was transferred to another HD unit).
Patients who completed the study were younger (
Demographic, anthropometric, and medical data of the studied population are detailed in Table
Characteristics of the study population.
All | Moderate to severe OSA |
No or mild OSA |
| |
---|---|---|---|---|
|
104 | 58 | 46 | |
|
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|
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Age (y) | 61.7 (14.9) | 65.2 (14.1) | 57.3 (14.8) |
|
Male sex ( |
66 (63.5) | 44 (75.9) | 22 (47.8) |
|
Ethnicity ( |
||||
Caucasian | 84 (80.8) | 49 (84.5) | 35 (76.1) | 0.545 |
Asian | 4 (3.8) | 2 (3.4) | 2 (4.4) | |
African | 15 (15.4) | 7 (12.1) | 9 (19.6) | |
BMI (kg/m2) | 26.1 (4.6) | 26.6 (4.4) | 25.5 (4.8) | 0.217 |
Neck circumference (cm) | 40.4 (4.4) | 42.0 (3.8) | 38.4 (4.3) |
|
Obesity (BMI ≥ 30 kg/m2) ( |
21 (20.2) | 13 (22.4) | 8 (17.4) | 0.526 |
Hypertension ( |
95 (91.4) | 54 (93.1) | 41 (89.1) | 0.474 |
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|
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Time on RRT (y) [median–IQR] | 2.6 [0.9–6.5] | 3.8 [1.2–10.3] | 1.9 [0.9–3.8] |
|
Residual diuresis (mL) | 505 (751) | 386 (563) | 687 (953) | 0.078 |
Interdialytic weight gain (kg) | 0.97 (1.62) | 0.99 (1.66) | 0.95 (1.57) | 0.908 |
HD duration per week (h)# | 11.3 (1.0) | 11.5 (0.8) | 11.2 (1.2) | 0.179 |
Morning HD shift ( |
69 (66.4) | 41 (70.7) | 28 (60.9) | 0.293 |
Hemodiafiltration ( |
25 (24.0) | 12 (20.7) | 13 (28.3) | 0.369 |
HD access ( |
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Fistula | 80 (76.9) | 43 (74.1) | 37 (80.4) | 0.449 |
Catheter | 24 (23.1) | 15 (25.9) | 9 (19.6) | |
eKt/V | 1.51 (0.33) | 1.47 (0.33) | 1.56 (0.34) | 0.203 |
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AHI (number/h) [median–IQR] | 17.5 [7.0–40.0] | 33.0 [22.0–52.0] | 6.5 [4.0–10.0] |
|
ODI (number/h) [median–IQR] | 18.0 [9.0–39.0] | 38.0 [20.0–49.0] | 9.0 [6.8–13.0] |
|
Epworth score [median–IQR] | 5 [3–8] | 5 [2–7] | 5 [3–9] | 0.247 |
Excessive daytime sleepiness ( |
17 (16.4) | 7 (12.1) | 10 (21.7) | 0.185 |
Sleep time (h) [median–IQR] | 7 [6–8] | 7 [6–8] | 7 [6–8] | 0.955 |
Poor sleep quality ( |
31 (29.8) | 18 (31.0) | 13 (28.4) | 0.759 |
Snoring ( |
57 (54.8) | 37 (63.8) | 20 (43.5) |
|
Unrefreshing sleep ( |
35 (33.6) | 19 (32.8) | 16 (34.8) | 0.828 |
Observed apneas ( |
7 (6.7) | 6 (10.3) | 1 (2.2) | 0.099 |
Restless legs syndrome ( |
19 (19.0) | 12 (22.2) | 7 (15.2) | 0.374 |
Use of sleep medications ( |
17 (16.4) | 7 (12.1) | 10 (21.7) | 0.285 |
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Hemoglobin (g/L) | 113.4 (12.3) | 113.0 (12.9) | 114.0 (11.8) | 0.684 |
Phosphates (mmol/L) | 1.49 (0.38) | 1.43 (0.37) | 1.57 (0.37) | 0.072 |
Creatinine (mg/dL) | 7.9 (2.3) | 7.7 (2.4) | 8.2 (2.2) | 0.343 |
BUN (mg/dL) | 54.6 (14.0) | 54.0 (14.6) | 55.2 (13.7) | 0.730 |
Bicarbonates (mmol/L) | 22.4 (2.4) | 22.1 (2.7) | 22.7 (2.0) | 0.277 |
Values are expressed as mean (SD) if not otherwise specified. IQR: interquartile range.
OSA: obstructive sleep apnea; BMI: body mass index; RRT: renal replacement therapy; HD: hemodialysis; eKt/V: hemodialysis efficacy, assessed using urea kinetic modelling; AHI: apnea/hypopnea index; ODI: oxygen desaturation index; BUN: blood urea nitrogen.
#All patients on thrice weekly HD with a synthetic HD membrane (polysulfone or polyethersulfone).
Only 14% (15 patients) had a normal polygraphy (AHI < 5/h); 30% had mild OSA (AHI 5–15/h), 25% had moderate OSA (AHI 15–30/h), and 31% had severe OSA (AHI ≥ 30/h). 11 out of 58 patients (19%) with moderate to severe OSA (AHI ≥ 15/h) had been previously diagnosed and only 6 (10%) were already treated.
Four out of 104 patients (4% of the population) had a central sleep apnea with a Cheyne-Stokes respiratory pattern.
Sleep related symptoms were present in a minority of the study population and did not significantly differ between patients with and without OSA (Table
Among the characteristics of kidney disease and hemodialysis, only time on renal replacement therapy (RRT) differentiated the 2 populations, with OSA patients being on RRT for a longer time.
The performances of the 3 existing screening scores are reported in Table
Performance of the screening instruments for OSA.
Test | Sensitivity | Specificity | PPV | NPV | Accuracy |
---|---|---|---|---|---|
Berlin’s Questionnaire | 51.9% [42.1–61.6] | 54.4% [44.6–64.1] | 57.1% [47.4–66.8] | 49.0% [39.2–58.8] | 53.0% |
STOP-BANG | 85.2% [78.2–92.2] | 54.4% [44.6–64.1] | 68.7% [59.6–77.8] | 75.8% [67.4–84.2] | 71.0% |
Adjusted Neck Circumference | 29.6% [20.7–38.6] | 91.3% [85.8–96.8] | 80.0 [72.2–87.8] | 52.5% [42.7–62.3] | 58.0% |
ANT algorithm |
90.5% [81.7–99.2] | 63.6% [49.3–78.0] | 70.4% [56.7–84.0] | 87.5% [77.6–97.4] | 76.7% |
Values are expressed as mean [95% CI].
PPV: positive predictive value; NPV: negative predictive value.
Accuracy: percentage of correctly classified subjects.
Berlin’s Questionnaire (BQ) was positive in 49 patients, classifying them at high risk of having OSA. Only 28 (57%) out of these patients had an AHI ≥ 15/h, corresponding to a positive predictive value (PPV) of 57%.
STOP-BANG score had a higher sensitivity, assigning 67 patients to the group having high risk of OSA. 71% of the patients were correctly classified.
Twenty patients had an Adjusted Neck Circumference (ANC) > 48 cm. This score had the highest specificity of the 3 tests but demonstrated a very poor sensitivity.
When the usual cut-off value of the tests was not considered and all tests were compared using a ROC analysis, the STOP-BANG score and the ANC showed a similar performance, with a ROC area of 0.652 [SE 0.085] and 0.655 [SE 0.083], respectively, whereas Berlin’s Questionnaire was less accurate with a ROC area of 0.538 [SE 0.093].
The characteristics of the derivation and validation populations are summarized in Supplementary Table S1 in Supplementary Material available online at
In the multivariate model based on the factors of classical screening scores (Model 1), only age, neck circumference, and hypertension were significantly associated with OSA, while BMI and sleep related symptoms showed a nonsignificant association (Table
Multivariate logistic models for prediction of obstructive sleep apnea.
Factors | Model 1 | Model 2 | ROC area | Hosmer-Lemeshow | ||
---|---|---|---|---|---|---|
OR | 95% CI | OR | 95% CI | |||
Gender (f) | 0.72 |
0.12–4.20 | ||||
Age (y) | 1.10 | 1.02–1.19 | 1.12 | 1.02–1.22 | ||
Neck circumference (cm) | 1.65 | 1.16–2.36 | 1.63 | 1.13–2.34 | ||
Hypertension | 147 | 2–11613 | 379 | 2–87016 | ||
BMI (kg/m2) | 0.87 |
0.68–1.10 | ||||
Daytime sleepiness | 0.58 |
0.08–4.32 | ||||
Snoring | 3.91 | 0.56–27.2 | 6.57 | 0.71–60.71 | ||
Unrefreshing sleep | 0.26 | 0.04–1.78 | 0.10 | 0.01–1.11 | 0.900 | 0.010 |
eKt/V | 0.08 | 0.00–14.09 | 0.927 | 0.259 | ||
Time on RRT | 1.06 | 0.92–1.21 | 0.927 | 0.387 |
Model 1: prediction model with classical risk factors (
Model 2: specific prediction model: significant factors of Model 1 (
OR: Odds Ratio and 95% CI in the final cumulative model.
ROC area: area under the ROC curve of the model when the factor is added.
Hosmer-Lemeshow test: goodness of fit of the model when the factor is added.
The classification and regression trees (CART) analysis identified age > 70 years, neck circumference > 40 cm, and time on RRT > 5 years as the best discriminatory factors. These factors were used to create a specific screening algorithm: the ANT algorithm (Figure
Proposed diagnostic algorithm (ANT algorithm). OSA: obstructive sleep apnea; Neck circum.: neck circumference; RRT: renal replacement therapy. Further investigation by objective sleep recording may be indicated in the low-risk group according to the clinical context (i.e., as a part of a presurgical assessment).
The application of the ANT algorithm to the validation population led to the categorization of 27 patients (63%) as being at risk for OSA, with higher sensitivity and negative predictive values than the classical screening tools (Table
On ROC analysis, the ANT algorithm performed significantly better than the 3 classical screening tools, showing a ROC area of 0.831 [SE 0.066] when applied to the validation population (Figure
ROC curves of the different screening strategies (validation population). AUC: area under ROC curve [SE].
This is the largest cohort study in Europe using an objective method (polygraphy) instead of questionnaires to assess the prevalence of OSA in ESRD patients undergoing hemodialysis. The prevalence of moderate to severe OSA in this population was 56%, and 31% of the subjects had severe OSA (with a clear indication for treatment), but only a small proportion of all OSA patients (19%) had been previously diagnosed and even less (10%) were treated.
The usual screening tools for OSA, Berlin’s Questionnaire, STOP-BANG score, and Adjusted Neck Circumference, performed poorly in this population and thus appear to be ineffective in daily clinical practice. In this study, we developed and validated a diagnostic algorithm specifically dedicated to hemodialysis patients with a high sensitivity and predictive value to help clinicians screen their patients for OSA.
The largest epidemiologic study in this field reported an OSA prevalence of 23.6% using Berlin’s Questionnaire [
OSA is recognized as an important cardiovascular risk factor in the general population [
For the daily clinical practice, it is important to have a simple tool allowing the patients to be quickly screened, in order to refer those at risk of OSA for further testing. Different OSA screening tools, in the form of questionnaires or scores, have been developed and validated in the general population for this purpose. According to our results, these screening tools are less accurate in the hemodialysis population with poor sensitivity and specificity. For example, Berlin’s Questionnaire showed 86% sensitivity and 77% specificity for identifying subjects with AHI > 5/h in the general population [
The poor performance of these screening tools in ESRD patients can be explained by the fact that they are mostly based on the typical clinical characteristics associated with OSA, such as excessive daytime sleepiness, unrefreshing sleep, obesity, and hypertension. In our population, the prevalence of these symptoms was not different between subjects with and without OSA, probably because other factors related to renal failure contribute to their development. Our observations support the fact that clinical parameters specific to the hemodialysis population should be considered when screening for OSA in this population. Among the HD related parameters, time on renal replacement therapy emerged as the one with the strongest association with OSA. This effect was independent of age and represents a new finding, suggesting that factors appearing late in the course of the kidney disease could play a role in the increased prevalence of OSA in HD patients. One possible involved mechanism could be chronic fluid overload with overnight fluid displacement to the neck soft tissues increasing upper airway collapse, a phenomenon that has been documented by our research group in hemodialysis patients with OSA [
To address the limitations of classical screening tools, we propose a simple screening algorithm, based on readily available variables: age, neck circumference, and time on renal replacement therapy. In our validation population, the proposed screening algorithm performed considerably better than usual screening tools, even when the tests were compared by ROC analysis, considering different cut-offs. This simple algorithm could be easily used by clinicians to screen their patients for OSA and select those who need further testing.
There are also limitations in our study that need to be considered. First, although this is the largest study on a hemodialysis population using nocturnal recordings, 31% of the subjects declined to have a sleep recording, which may have induced a selection bias. However, since patients who refused the recording were older than those who accepted and OSA tends to increase with age, this may have led to an underestimation of OSA prevalence. This refusal rate also highlights the fact that sleep recordings, although noninvasive, represent an important burden for hemodialysis patients and the fact that a simple clinical algorithm like the one we propose would be easier to use as a first line screening tool. Second, even though we used independent groups of patients for the creation and the validation of the ANT algorithm, this screening strategy will need to be validated in another population. However, since our population has similar demographic and clinical features (28% of patients with age > 70 years and 32% with a time on RRT > 5 years) to the European hemodialysis population [
Finally, we used ambulatory polygraphy instead of the gold standard in-laboratory polysomnography. Even though polygraphy is a recognized diagnostic tool for OSA, the absence of an objective sleep duration measure by electroencephalogram implies an AHI calculation based on the recording time rather than sleep duration which is shorter. As a consequence, severity and prevalence of OSA could have been underestimated. On the opposite side, since all recordings were performed the night preceding hemodialysis, OSA severity could have been overestimated considering the negative effect of fluid overload on OSA. In effect, we recently showed that OSA is more severe on nights preceding a hemodialysis session in overhydrated patients and is reduced by the correction of fluid overload with hemodialysis [
In conclusion, our study confirms the high prevalence of OSA and highlights the low diagnosis and treatment rate of this important cardiovascular risk factor in the hemodialysis population.
Considering the poor performance of classical OSA screening tools, we propose a simple screening algorithm specific to the hemodialysis population to identify patients at risk for OSA who need further testing. This diagnostic approach warrants further prospective validation on a larger population prior to introducing the algorithm into clinical practice.
Valentina Forni Ogna and Adam Ogna are joint first authorship. Michel Burnier and Raphaël Heinzer are joint last authorship. Part of the results was presented in the form of poster at the American Thoracic Society 2014 International Conference in San Diego (CA); May 16–21, 2014.
All authors declare that they have no competing interests regarding the publication of this paper.
Valentina Forni Ogna, Adam Ogna, Menno Pruijm, Georges Halabi, Olivier Phan, José Haba-Rubio, Michel Burnier, and Raphaël Heinzer designed the experiment; Valentina Forni Ogna, Adam Ogna, Alexandra Mihalache, Georges Halabi, Olivier Phan, Menno Pruijm, Roberto Bullani, Daniel Teta, Thierry Gauthier, Anne Cherpillod, Claudine Mathieu, Isabelle Bassi, Emilie Zuercher, and Francoise Cornette conducted the research; Valentina Forni Ogna, Adam Ogna, Alexandra Mihalache, Francoise Cornette, José Haba-Rubio, and Raphaël Heinzer analyzed the data and performed the statistical analyses; Valentina Forni Ogna, Adam Ogna, Menno Pruijm, José Haba-Rubio, Michel Burnier, and Raphaël Heinzer wrote the paper; Adam Ogna, Valentina Forni Ogna, and Raphaël Heinzer have primary responsibility for the final content. All authors had full access to all of the data (including statistical reports and tables) in the study, revised the paper for important intellectual content, and approved the final version of the paper.
This study was supported by research grants of the Swiss Kidney Foundation (Schweizerische Nierenstiftung) and the Pulmonary League of Canton Vaud (Ligue Pulmonaire Vaudoise). The Centre for Investigation and Research in Sleep (CIRS) and the Nephrology Department of Lausanne University Hospital provided further logistic support. The study sponsors had no role in the collection, analysis, and interpretation of data or in the writing of the report and in the decision to submit the paper for publication.