Volume 202, Issue 6 , Pages 552.e1-552.e7, June 2010
Prospective trial on obstructive sleep apnea in pregnancy and fetal heart rate monitoring
Article Outline
Objective
Obstructive sleep apnea (OSA) involves episodic nocturnal apneas. Using polysomnography, we examined the predictive capacity of screening questionnaires (Berlin) in pregnancy. Incorporating simultaneous fetal heart rate monitoring (FHM), we examined the association of maternal apnea with FHM abnormalities.
Study Design
We enrolled 100 pregnant women at 26-39 weeks of gestation with OSA screening and baseline data ascertainment who underwent polysomnography and FHM for ≥3 hours. The relationship between maternal characteristics, OSA, and FHM was explored with multivariate analyses that were controlled for potential confounders.
Results
When compared with polysomnography, sensitivity and specificity by Berlin screening was 35% and 63.8%, respectively; the snoring component of the Berlin correlated better with oxygen desaturation <95% (P = .003). Body mass index was a significant confounder (rs = 0.44; P < .0001). No association was observed between FHM abnormalities and OSA parameters.
Conclusion
In pregnancy, the Berlin questionnaire poorly predicts OSA. It is unclear whether fetal compromise during maternal apnea is a mechanism in OSA that is related to pregnancy outcome.
Key words: fetal heart rate monitoring, obstructive sleep apnea, polysomnography, pregnancy
Obstructive sleep apnea (OSA) is characterized by episodes of airflow limitation that cause intermittent hypoxia.1 Studies have observed that in nonpregnant patients, it is an independent risk factor for hypertension, coronary artery disease, and atherosclerosis.2, 3, 4 Although the true prevalence rate in pregnancy is unknown, many physiologic changes contribute to increased risk for OSA.5, 6, 7, 8 To date, few studies have investigated OSA in pregnancy, and most studies have failed to adjust for potential maternal confounders.1, 6, 7, 8
The diagnosis of OSA is established by polysomnography, but time and expense limitations have lead to the development of several validated screening tools, which includes the Berlin questionnaire.9, 10 Although the Berlin questionnaire has been shown to have a positive predictive value as high as 89%, recent analyses suggest the predictive performance of the questionnaire may be quite variable; the sensitivity and specificity range from 57-86% and 43-97%, respectively.10, 11, 12, 13, 14
Among pregnant women, snoring, which is a risk factor for OSA, increases through latter gestation; although not all “snorers” have OSA, it has been associated with adverse pregnancy outcomes, intrauterine growth restriction, and preeclampsia.15, 16, 17, 18 Furthermore, maternal apnea episodes have been associated with fetal heart rate decelerations that may be a contributing factor to documented adverse pregnancy outcomes.19
We hypothesized that the Berlin questionnaire is a valid tool for the screening of OSA in pregnancy, when compared with the gold standard, polysomnography. We therefore sought to investigate the performance of the Berlin questionnaire among pregnant women in a large, prospectively acquired cohort. We also sought to investigate the suggested association between sleep-related maternal apnea events and changes in fetal oxygenation status, as measured by fetal heart rate monitoring (FHM).
Methods
Basic study design
Institutional review board approval was obtained from both Baylor College of Medicine and the Harris County Hospital District. All pregnant women during the 9-month study interval who were admitted to the antepartum service at the Ben Taub General Hospital were approached for participation in the study. Enrollment was halted when we reached an initial cohort of 100 women. Inclusion criteria included singleton pregnancy with unrelated condition for antepartum admission and gestational age ≥26 weeks by best obstetric estimate (with at least 1 confirmatory sonogram). Exclusion criteria consisted of hospital stay <4 hours or immediate delivery within 4 hours, multifetal gestation, known or suspected fetal growth restriction <10%, multiple fetal anomalies or death, known severe cardiopulmonary disease, and known OSA.
Once consented, the patient completed the Berlin questionnaire in their native language. The Berlin is a well-validated screening tool for OSA among nonpregnant subjects that stratifies patients into low-risk and high-risk categories for OSA.1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14 The Berlin questionnaire uses 10 self-administered questions at an 8th-grade equivalent regarding risk factors that focus on characteristics of snoring, sleepiness throughout the day, and presence of hypertension and increased body mass index (BMI).9, 10, 11, 12, 13, 14A questionnaire for a high-risk patient will have 2 of 3 symptom categories positive. In this study, we considered a high-risk Berlin questionnaire score to be evidence of symptom-diagnosed OSA. After completion of the questionnaire, patients were monitored with simultaneous and time-synched FHM and polysomnography, as described further later (Figure).
FIGURE.
Simultaneous fetal heart monitoring and polysomnography tracings
Uterine contractions and fetal heart rate were recorded along with maternal nasal airflow, heart rate, and oxygen saturation that were with synched polysomnography and fetal heart monitoring devices. The polysomnography tracing illustrates an apnea (red shading), oxygen desaturation (dark blue), and hypopnea (light blue), none of which were accompanied by fetal heart monitoring abnormalities, as shown in representative tracings.
FHM, fetal heart monitoring; PSG, polysomnography.
Olivarez. Obstructive sleep apnea and fetal heart monitoring. Am J Obstet Gynecol 2010.
Maternal data collection
At time of enrollment, extracted maternal baseline data included maternal age, ethnicity, height, prepregnancy weight, and pregnancy weight. Of note, prepregnancy weight was asked of the patient and then confirmed with the medical record. Maternal data also included gestational age, smoking history, gravidity, parity, abortion history, pregestational diabetes mellitus, history of thyroid disease, chronic hypertension, previously diagnosed sleep disorder, gestational diabetes mellitus, gestational hypertension, preeclampsia, preterm labor, preterm premature rupture of membranes, urinary tract infection, abruption, hydramnios, and intrauterine growth restriction. In all instances, standard clinical definitions based on the American College of Obstetrics and Gynecology practice guide lines were used for diagnosis.
BMI classification
Each woman was stratified into BMI category according to the International Obesity Task Force classification: underweight, <19 kg/m2; normal weight, 19-24.9 kg/m2; overweight, 25-29.9 kg/m2; class I obesity, 30-34.9 kg/m2; class II obesity, 35-39.9 kg/m2; and class III obesity, >40 kg/m2. BMI was calculated using height and weight data (kilograms/square meter) that were collected during initial assessment and during postpartum hospitalization. For analyses of prepregnancy BMI, we defined BMI as 19-24.9 kg/m2 as normal, when compared with those women with a BMI >25 kg/m2. However, the reference “normal” range for analyses of BMI at delivery was 19–29.9 kg/m2 (normal and overweight). This acknowledges that a normal-weight woman who gains up to the recommended (Institute of Medicine) amount during pregnancy will have a body mass index of ≤29.9.
Simultaneous polysomnography and FHM monitoring
The polysomnography device that was used in the study was the ResMed ApneaLink (ResMed Corp, San Diego, CA), which records continuous pulse oxymetry and nasal airflow. Standard external FHM that was in use at Ben Taub General Hospital was used along with the WatchChild system (Hill-Rom, Batesville, IN) to record fetal heart rate.
Once placed on FHM, the polysomnography monitor was prepared and placed on the patient. The polysomnography monitor was then started, and the start time was electronically marked on the fetal heart rate tracing strip and recorded by the WatchChild system. Patients were monitored at night for 3-6 hours (dependent on maternal sleeping behavior), and the study was interrupted only if the patient experienced intense discomfort with the monitors or if the patient spontaneously awoke. Occasionally (<3 occurrences/subject), the patient had to be awakened for repositioning of the devices. At the conclusion of the study, the devices were removed, and the fetal heart rate strip was printed from the WatchChild system. In addition, the polysomnography data were downloaded onto the study computer that generated a comprehensive sleep study report.
FHM data
After the data were collected, the FHM tracings were reviewed independently by 3 maternal-fetal medicine faculty members at Baylor College of Medicine who analyzed the tracings and noted the following information as previously described: severe variables (<70 beats/min; >60 seconds), late decelerations (≥1 deceleration/30 minutes), bradycardia (<110 beats/min), tachycardia (>180 beats/min), poor long-term variability (variable decelerations with poor beat to beat), variable decelerations with tachycardia, recurrent prolonged variable decelerations (>2 decelerations of <70 beats/min for >90 seconds in 15 minutes), sinusoidal pattern (long-term variability frequency, 2-5 cycles/minute), ≥1 variable decelerations in 2 consecutive 30-minute windows, increased variability (>25 beats/min for at least 30 minutes), baseline fetal heart rate <100 beats/min with accelerations, baseline fetal heart rate between 100 and 200 beats/min without accelerations, and suspected fetal arrhythmia.20 Consensus was reached with a fourth independent reviewer when 2 of 3 maternal-fetal medicine physicians noted the same abnormality during the same time interval. There were complete tracings on the 100 participants and no missing data, because the monitors were adjusted to maintain fetal tracing throughout the sleep study interval.
Polysomnography data
Two pulmonologists independently interpreted the polysomnography data and verified the sleep study report. Polysomnography data were validated by the number of apnea events and hypopnea events per hour (Apnea-Hypopnea Index [AHI]) and used an a priori determination of the AHI of ≥5 as diagnostic of OSA. Similarly, apnea-hypopnea episodes (AHE; defined as total number of apneas and hypopneas throughout the monitored period) and oxygen desaturation events (ODE; defined as total number of oxygen desaturation events throughout the monitored period), time when oxygen saturation was <90%, time when oxygen saturation was <85%, nadir of oxygen desaturation, oxygen desaturation index, percentage of flow-limited breaths with snoring, and the percentage of flow-limited breaths without snoring were also verified.
Associating FHM and polysomnography data
The FHM and polysomnography data sheets were then reviewed; the precise recorded time of apnea event, presence of an associated fetal heart rate tracing abnormality, degree of oxygen desaturation, and nadir of oxygen desaturation were noted. At the onset of each subject's recorded monitoring, time-syncing for each FHM and polysomnography tracing occurred and was validated to be within <10 seconds of precision.
Statistical analysis
Women with and without OSA as diagnosed by accepted polysomnography were compared. For calculation of power, the primary objective was to test the null hypothesis pertaining to the use and validation of the Berlin Questionnaire in pregnant women. We assumed a prevalence of 40% by questionnaire.6, 17, 18, 19, 21, 22, 23, 24 With an alpha of .05, we anticipated that to detect a difference in the questionnaire vs polysomnography at <20% would require a minimum of 80 subjects. Given the concomitant fetal heart rate tracing as a pilot study, we arbitrarily (but a priori) increased the total number of enrollees to 100. Nominal data were analyzed with χ2 or Fisher's exact test with Yates correction for continuity, Student t test for continuous data, and Wilcoxon rank sums for ordinal or nonnormal data. The relationship between standard Berlin measures and each of its components (eg, snoring questions, sleepiness questions) and the gold-standard polysomnography test (along with various components measured by polysomnography, including AHI, AHE, and ODE) were analyzed by linear and logistic regression analyses. R2 and corresponding adjusted odds ratios with 95% confidence intervals (CIs) and probability values were determined with polysomnography as the outcome variable and Berlin as the predictor of interest. The relationship between OSA and measures was examined overall and at various strata of BMI per aforementioned Institute of Medicine Guidelines (≤25, 25-29, ≥30 kg/m2); statistical hypotheses were tested with the use of 2-tailed 95% CIs. Specifically, our analysis was adjusted for the potential confounders of maternal age, parity, and race and then stratified by maternal BMI to examine for consistency in finding, which was performed by adding this variable to each model on the second step. Data from logistic regression modeling are reported as adjusted odds ratios with 95% CIs and probability values for estimation of statistical significance. All statistical analysis was performed with SAS System statistical software (SAS Institute Inc, Cary, NC).
Results
Study population
A total of 176 patients met the criteria over a 9-month period and were approached for participation in the study; 76 patients declined to participate, and 100 patients consented, administered the questionnaire, and underwent at least 3 hours of nocturnal polysomnography with FHM. All 100 patients had complete FHM tracings. Most of our patients were Hispanic and were admitted for unrelated conditions, preterm labor, gestational diabetes mellitus, preeclampsia, preterm premature rupture of membranes, chronic hypertension, trauma, gestational hypertension, pregestational diabetes mellitus, and urinary tract infection (in order of frequency), none of which was significant enough to warrant immediate delivery (Table 1). Using the “gold standard” polysomnography diagnostic criteria, 20 patients (20%) were diagnosed with OSA; by Berlin questionnaire measures, a significantly higher number (36 patients; 36%) would have been diagnosed with OSA. As described in Table 1, subjects were similar by virtue of maternal characteristics and indication for antepartum admission (Table 1).
TABLE 1. Patients characteristic by presence of obstructive sleep apnea by the gold standard polysomnography test
| Characteristic | Overall (n = 100) | OSA + (n = 20) | OSA – (n = 80) | P value |
|---|---|---|---|---|
| Maternal age, ya | 26.6 ±7.1 | 28.7±6.8 | 28.6±7.2 | .85 |
| Body mass index, kg/m2a | 27.5±7.2 | 28.6±8.6 | 27.2±6.9 | .73 |
| Gestational age, wka | 32.3±3.5 | 32.5±2.9 | 32.2±3.6 | .75 |
| Gravidity, na | 3.3±2.1 | 3.5±2.9 | 3.2±1.8 | .91 |
| Parity, na | 1.72±1.5 | 1.6±1.4 | 1.8±1.5 | .63 |
| Smoker, n (%) | 4(4) | 1(5) | 3(3.8) | .99 |
| Amniotic fluid indexa | 13.6±4.6 | 12.9±4.6 | 13.8±4.6 | .55 |
| Pregestational diabetes mellitus, n (%) | 7(7) | 1(5) | 6(7.5) | .99 |
| Chronic hypertension, n (%) | 9(9) | 2(10) | 7(8.8) | .99 |
| Gestational diabetes mellitus, n (%) | 21(21) | 3(15) | 18(22.5) | .55 |
| Gestational hypertension, n (%) | 7(7) | 1(5) | 6(7.5) | .99 |
| Mild preeclampsia, n (%) | 9(9) | 1(5) | 8(10) | .68 |
| Severe preeclampsia, n (%) | 4(4) | 0 | 4(5) | .58 |
| Preterm labor, n (%) | 24(24) | 8(40) | 16(20) | .06 |
| Preterm premature rupture of membranes, n (%) | 11(11) | 3(15) | 8(10) | .68 |
| Urinary tract infection, n (%) | 4(4) | 0 | 4(5) | .58 |
| Trauma, n (%) | 9(9) | 4(20) | 5(6) | .076 |
| Polyhydramnios, n (%) | 2(2) | 0 | 2(2.5) | .99 |
aData are given as mean ± SD. |
Analysis of Berlin questionnaire and correlation with gold standard polysomnography
OSA classification by polysomnography and Berlin questionnaire were unrelated in our prospectively acquired cohort of pregnant women (P = .92; Table 2). Of the 20 patients with positive findings for OSA on polysomnography, only 7 patients were positive by Berlin questionnaire measures (sensitivity, 35%). Among 80 women with a polysomnography negative test, 51 women were negative by Berlin (specificity, 63.8%).
TABLE 2. Comparison of the Berlin questionnaire and the gold standard polysomnography test for obstructive sleep apnea
| Test | Polysomnography | |||||||
|---|---|---|---|---|---|---|---|---|
| 1(r2) | P value | 2(r2) | P value | 3(r2) | P value | 4(r2) | P value | |
| Berlin 1 | 0.0001 | .92 | 0.003 | .59 | 0.012 | .28 | 0.056 | .02 |
| Berlin 1 + body mass index | 0.008 | .68 | 0.02 | .32 | 0.042 | .12 | 0.19 | <.0001 |
| Berlin 2 | 0.011 | .28 | 0.026 | .11 | 0.03 | .08 | 0.09 | .003 |
| Berlin 2 + body mass index | 0.013 | .52 | 0.035 | .17 | 0.05 | .07 | 0.21 | <.0001 |
| Berlin 3 | 0.007 | .4 | 0.005 | .48 | 0.002 | .63 | 0.0008 | .78 |
| Berlin 3 + body mass index | 0.013 | .52 | 0.027 | .27 | 0.04 | .11 | 0.19 | <.0001 |
To explore the efficacy and validity of Berlin measures and components of polysomnography among pregnant women, we analyzed the correlation between the Berlin questionnaire in its standard form (Berlin 1; Table 2) and the gold standard polysomnography test (polysomnography 1; Table 2), which uses AHI ≥5 for a diagnosis of OSA. We subsequently divided the Berlin questionnaire into its individual components and focused further on those questions that pertained specifically to snoring and daytime sleepiness and compared these with the standard polysomnography test and its components of AHI, AHE, and ODE. As described in Table 2, the standard Berlin questionnaire correlated poorly with the gold standard polysomnography test for OSA in pregnancy, with or without BMI. However, both the standard Berlin questionnaire (Berlin 1) and the snoring questions of the Berlin questionnaire (Berlin 2) were predictors of ODE components of OSA (r2 = 0.056; P = .02 for standard Berlin; r2 = 0.09; P = .003 for “snoring”; Table 2); such relations were not seen for AHI or AHE components. The utility of the Berlin questionnaire (both standard and snoring questionnaire) in the prediction of ODE was improved substantially with the inclusion of BMI (Table 2). Specifically, across all strata, the addition of information on BMI significantly increased the ability of the Berlin questionnaire in the prediction of polysomnography with significance reached in the ODE-positive cohort (P < .0001; Table 2). “Sleepiness” measures did not correlate with polysomnography, with or without BMI (Table 2).
Analysis of BMI and OSA
Given our findings as reported in Table 2, we sought to further characterize the independent influence of BMI on OSA and its various components (Table 3). In this analysis, we present crude and adjusted point estimates, with control for potential confounders. In multivariate analysis, BMI was associated independently with specific components of OSA, including AHI (adjusted odds ratio [aOR], 4.64; P = .03), AHE (aOR, 6.03; P = .02), and ODE (aOR, 17.49; P < .0001) after being controlled for potential confounders. Control for confounders was based on those factors in our data set that were univariate predictors of OSA as diagnosed by the Berlin questionnaire and of obesity (preexisting diabetes mellitus, gestational hypertension, gestational diabetes mellitus, gestational age, and trauma).
TABLE 3. Multivariate analysis of body mass index and risk for obstructive sleep apnea from the gold standard polysomnography test
| Variable | Polysomnography | |||||||
|---|---|---|---|---|---|---|---|---|
| 1(odds ratio) | P value | 2(F valuea) | P value | 3(F valuea) | P value | 4(F valuea) | P value | |
| Crude body mass index | 1.03 | .44 | 2.15 | .14 | 4.30 | .04 | 22.44 | <.0001 |
| Adjusted body mass indexb | 1.06 | .17 | 4.64 | .03 | 6.03 | .02 | 17.49 | <.0001 |
aFrom logistic or generalized linear regression models; |
bAdjustment is made for pregestational diabetes mellitus, gestational hypertension, gestational diabetes mellitus, preterm labor, gestational age, and history of trauma. |
Analysis of FHM and apnea in OSA subjects
As shown in Table 4, among the 20 subjects with OSA by polysomnography criteria, the only FHM abnormalities that occurred in our study participants were variable decelerations that were of appropriate gestational age. Moreover, none of the apnea episodes were associated with any fetal tracing abnormality (Figure; Table 4).
TABLE 4. Polysomnography data and associated fetal heart monitoring data in patients with obstructive sleep apnea
| Subject | Body mass index | Estimated gestational age, wk | Apnea events and hypopnea events per hour, n | Total apneas and hypopneas throughout the monitored period, n | O2 nadir | Fetal heart monitoring abnormality | Associated with apnea |
|---|---|---|---|---|---|---|---|
| OV004 | 18 | 33 | 8 | 2 | 93 | Variable deceleration | No |
| RC014 | 20.5 | 29 | 10 | 0 | 98 | Variable deceleration | No |
| RV018 | 26.6 | 34 | 7 | 10 | 92 | None | No |
| LM022 | 23.4 | 35 | 5 | 1 | 95 | Variable deceleration | No |
| RS025 | 22.2 | 33 | 17 | 21 | 89 | Variable deceleration | No |
| RS027 | 25.9 | 29 | 11 | 21 | 89 | Variable deceleration | No |
| VY040 | 30.1 | 32 | 19 | 4 | 94 | Variable deceleration | No |
| ML055 | 57 | 35 | 18 | 83 | 84 | None | No |
| HL057 | 26.5 | 30 | 8 | 7 | 89 | None | No |
| AL060 | 31.9 | 26 | 28 | 26 | 91 | Variable deceleration | No |
| FJ065 | 26.5 | 31 | 5 | 6 | 86 | None | No |
| GA069 | 24.3 | 34 | 8 | 2 | 92 | Variable deceleration | No |
| CN072 | 24 | 34 | 10 | 0 | 92 | None | No |
| PE073 | 43.3 | 27 | 16 | 15 | 91 | None | No |
| CA075 | 31.5 | 35 | 6 | 7 | 79 | None | No |
| SR078 | 25.6 | 34 | 6 | 3 | 92 | None | No |
| GM080 | 27.4 | 33 | 28 | 9 | 91 | None | No |
| HA082 | 25.4 | 36 | 15 | 4 | 90 | Variable deceleration | No |
| CE091 | 26.6 | 33 | 6 | 10 | 91 | None | No |
| Mean | 28.3 | 32.3 | 12.2 | 12.2 | 90.4 | — | — |
Comment
In this prospective study, we thoroughly characterized OSA among 100 pregnant women with the use of Berlin measures alongside polysomnography and FHM. Our study showed that 20% of our pregnant cohort was diagnosed with OSA by polysomnography. The Berlin questionnaire is a simple tool that has been used widely to screen for sleep disordered breathing in the nonpregnant population.10, 11, 12 However, our findings support the previous observations of other investigators in smaller or nonprospectively acquired cohorts that the Berlin questionnaire, in its standard form, is not a reliable predictor of OSA, with a sensitivity approximating 35% and a specificity approximating 64%.13, 14
In contrast, we observed that the “snoring” component of the Berlin questionnaire (Berlin 2; TABLE 2, TABLE 3) did correlate well with at least 1 polysomnography parameter-oxygen desaturation event (ODE; P = .003; Table 2). Although not diagnostic of OSA, this single measure has been shown to be a relatively reliable indicator of sleep-disordered breathing.5, 15 In contrast, “sleepiness” measures (Berlin 3) failed to correlate with either standard composite polysomnography or its components (Table 2). We speculate that the common collective experience of “fatigue” among women in latter pregnancy may be explained by general discomfort from frequent nocturia, discomfort at night from physical size of the fetus, and increased restless leg syndrome rather than OSA.21, 22 Our results suggest that a modified form of the Berlin questionnaire that would focus on the “snoring” measures might more accurately predict the risk for OSA among pregnant women than the standard questionnaire.
We further extend our analysis to demonstrate that BMI independently associates with polysomnography components and improves the prediction of the Berlin measures (TABLE 2, TABLE 3). Indeed, BMI persisted as an independent risk factor for OSA, even after being controlled for other potential confounders that included pregestational diabetes mellitus, hypertension, and preterm labor (OR, 1.12; 95% CI, 1.0–1.22; P = .007). Specifically, the odds ratio for OSA is increased 12% with every unit increase in BMI. These collective findings may be interpreted in 1 of 2 ways. First, BMI is an independent contributor to OSA or its associated measures. Second, BMI is the true predictor of AHI and ODE among pregnant women in their latter trimesters. Although our study does not truly delineate between these 2 alternate explanations, it does beg investigators to question the role that BMI contributes to adverse pregnancy outcomes that were attributed previously to OSA.6, 7, 8, 16, 17, 18, 23, 24, 25
Through simultaneous polysomnography and FHM, we extended our analysis to demonstrate that, in women with diagnosed OSA by polysomnography, apnea episodes are not accompanied by any significant changes in the fetal heart rate. The only FHM abnormalities that occurred among all subjects were variable decelerations, and these were among early gestations (<32 weeks). Conflicting data exist about maternal apneic episodes and effects on the fetus. A case report of severe OSA during pregnancy revealed normal fetal heart rate reactivity, even during maternal apneic episodes with associated severe desaturations.23 However, in a recent study by Sahin et al,19 3 of the 4 women had fetal heart decelerations that accompanied maternal desaturation. Our larger sample size of 20 women from a prospectively acquired cohort failed to demonstrate even a single fetal heart decelerations with apneic episodes. Our data suggest that potential fetal hypoxia during apneic episodes may not occur; however, it ultimately remains unclear because our measure of fetal hypoxia is a poor surrogate. It is still unclear whether fetal hypoxia during maternal apnea occurs and, moreover, whether it is a primary contributor to the reported adverse pregnancy outcomes that are associated with OSA. Furthermore, our study questions whether other mechanisms or potential maternal confounders such as obesity may be responsible for the adverse pregnancy outcomes that other investigators have reported as being associated with OSA.24
Because we were able to enroll only a portion of the patients who were approached for participation in the study, we recognize that our study suffers from selection bias. Few patients with uncomplicated pregnancies were included because we enrolled only patients in the antepartum unit. Additionally, we realize that the hospital environment may have resulted in more frequent arousals. However, we were able to obtain reliable polysomnographic data, and our cohort did not differ by virtue of any recognized parameter (Table 1). Moreover, despite these limitations, we were able to perform the largest study to date to analyze OSA in pregnancy that was measured by polysomnography and FHM.
There are no specific guidelines for screening measures for OSA among pregnant women. Our data support the use of a modified Berlin questionnaire with the use of snoring symptoms, but it must be further validated in a larger cohort. Moreover, thorough consideration to the independent contribution of obesity must be undertaken. Further research is needed to optimize the use of OSA screening questionnaires in pregnancy and to investigate the true mechanism by which OSA may lead to adverse pregnancy outcome.
References
- . Epidemiology, risk factors, and consequences of obstructive sleep apnea and short sleep duration. Prog Cardiovasc Dis. 2009;51:285–293
- . Obstructive sleep apnea syndrome as a risk factor for hypertension. BMJ. 2000;320:479–482
- . Obstructive sleep apneoa and its cardiovascular consequences. Lancet. 2009;373:82–93
- . Additive effects of obstructive sleep apnea and hypertension on early markers of carotid atherosclerosis. Hypertension. 2009;53:64–69
- The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;32:1230–1235
- . Sleep disorders in pregnancy. Curr Opin Pulm Med. 2003;9:477–483
- . A 26-year-old pregnant woman with fatigue and excessive daytime sleepiness. Chest. 2008;134:637–639
- Effect of normal and preeclamptic pregnancies on the oxyhemoglobin dissociation curve. Anesthesia. 1986;65:426–427
- Validation of the Berlin questionnaire and American Society of Anesthesiologists checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology. 2008;108:822–830
- . Validation of the modified Berlin questionnaire to identify patients at risk for the obstructive sleep apnoea syndrome. Indian J Med Res. 2006;3:281–290
- Prevalence of symptoms and risk of sleep apnea in primary care. Chest. 2003;124:,1406-14
- . Using the Berlin questionnaire to identify patients at risk for sleep apnea syndrome. Ann Intern Med. 1999;131:485–491
- . Is the Berlin questionnaire an appropriate diagnostic tool for sleep medicine in pneumological rehabilitation?. Pneumologie. 2006;60:737–742
- . The Berlin questionnaire for sleep apnea in a sleep clinic population: relationship to polysomnographic measurement of respiratory disturbance. Sleep Breath. 2008;12:39–45
- . Rapid risk stratification for obstructive sleep apnea, based on snoring severity and body mass index. Otolaryngol Head Neck Surg. 2009;5:615–618
- . Snoring, pregnancy-induced hypertension, and growth retardation of the fetus. Chest. 2000;117:137–141
- . Self-reported snoring in pregnancy: association with fetal outcome. Chest. 1996;85:843–848
- Pre-eclamptic toxemia is associated with sleep disorder breathing and endothelial dysfunction. Eur Respir J. 2006;27:328–333
- Obstructive sleep apnea in pregnancy and fetal outcome. Int J Gynaecol Obstet. 2008;100:141–146
- Fetal pulse oximetry and cesarean delivery. N Engl J Med. 2006;355:2195–2202
- . Negotiating sleep: a qualitative study of new mothers. Perinat Neonatal Nurs. 2007;21:114–122
- . Effects of pregnancy on mother's sleep. Sleep Med. 2002;1:37–42
- . Obstructive sleep apnea during pregnancy: therapy and implications for fetal health. Am Rev Respir Dis. 1991;144:461–463
- . Pre-eclampsia and nasal CPAP: part1, early intervention with nasal CPAP in pregnant women with risk-factors for pre-eclampsia: preliminary findings. Sleep Med. 2007;9:9–14
- Obstructive sleep apnoea and its association with gestational hypertension. Eur Respir J. 2009;33:559–565
Authorship and contribution to the article is limited to the 8 authors indicated. There was no outside funding or technical assistance with the production of this article.
Cite this article as: Olivarez SA, Maheshwari B, McCarthy M, et al. Prospective trial on obstructive sleep apnea in pregnancy and fetal heart rate monitoring. Am J Obstet Gynecol 2010;202:552.e1-7.
PII: S0002-9378(09)02251-0
doi:10.1016/j.ajog.2009.12.008
© 2010 Mosby, Inc. All rights reserved.
Volume 202, Issue 6 , Pages 552.e1-552.e7, June 2010
