Abstract
Beta‐blockers are one of the most commonly prescribed classes of antihypertensive medications during pregnancy. Previous studies reported an association between beta‐blocker exposure and intrauterine growth restriction. Whether some beta‐blocker subtypes may be associated with higher risk is not known. This is a retrospective cohort study of pregnant women exposed to beta‐blockers in the Kaiser Permanente Southern California Region between 2003 and 2014. Logistic regression models were used to evaluate association between exposure to different beta‐blocker agents and risk of low fetal birth weights. In a cohort of 379 238 singleton pregnancies, 4847 (1.3%) were exposed to beta‐blockers. The four most commonly prescribed beta‐blockers were labetalol (n = 3357), atenolol (n = 638), propranolol (n = 489), and metoprolol (n = 324). Mean birth weight and % low birth weight (<2500 g) were 2926 ± 841 g and 24.4% for labetalol, 3058 ± 748 g and 18.0% for atenolol, 3163 ± 702 g and 13.3% for metoprolol, 3286 ± 651 g and 7.6% for propranolol, and 3353 ± 554 g and 5.2% for non‐exposed controls. Exposure to atenolol and labetalol were associated with increased risks of infant born small for gestational age (SGA) (atenolol: adjusted OR 2.4, 95% CI: 1.7‐3.3; labetalol: adjusted OR 2.9, 95% CI: 2.6‐3.2). Risk of SGA associated with metoprolol or propranolol exposure was not significantly different from the non‐exposed group (metoprolol: adjusted OR 1.5, 95% CI: 0.9‐2.3; propranolol: adjusted OR 1.3, 95% CI: 0.9‐1.9). Association between beta‐blocker exposure and SGA does not appear to be a class effect. Variations in pharmacodynamics and confounding by indication may explain these findings.
Keywords: beta‐blocker, intrauterine growth restriction, pregnancy, small for gestational age
1. INTRODUCTION
Beta‐blockers are the most commonly used class of medication for treating cardiac conditions in pregnant women.1 About one‐third of pregnant women with cardiovascular disease use medications for their cardiac condition during pregnancy. Among these women, about two‐thirds are prescribed beta‐blockers.1 Indications for beta‐blocker use in pregnant women include treatment of hypertension, gestational hypertension, preeclampsia, arrhythmia, heart failure, and valvular disease.2
Beta‐blockers can cross the placenta and potentially can cause physiological changes in the fetus.3 Beta‐blocker exposure had been shown to cause bradycardia and hypoglycemia in the neonate.4 Some studies reported an association between beta‐blocker exposure and infants born small for gestational age (SGA).5, 6, 7, 8 Other studies failed to show this association.9, 10 Many of these studies evaluate beta‐blockers as a group and do not differentiate between individual beta blockers.
Beta‐blockers are a heterogeneous class of medications with different pharmacologic and physiologic properties. The beta‐1, beta‐2, and alpha blocking activities differ between beta‐blockers. Beta‐blockers such as bisoprolol and metoprolol have much higher beta‐1 to beta‐2 selectivity compared to labetalol.11 Beta‐blockers such as carvedilol and labetalol have alpha‐1 blocking activities that are associated with vasodilation. Therefore, when studying the effects of beta‐blockers on clinical outcomes, it is important to differentiate members of this pharmacologic class.
The goal of this study is to report the prevalence of beta‐blocker exposure in pregnant women in a large, demographically diverse, integrated healthcare system, and to determine the impact of exposure to individual members of the beta‐blocker class on fetal birth weight.
2. METHODS
2.1. Study population
This is a retrospective cohort study that included all births in the Kaiser Permanente Southern California (KPSC) Region between January 1, 2003 and December 31, 2014. Patients who were not enrollees of the Kaiser Permanente Health Plan or did not have continuous 1‐year enrollment within the year prior to the estimated date of delivery, were excluded from the study to allow adequate follow‐up data. Only singleton pregnancies were included. The research protocol used in this study was reviewed and approved by the Kaiser Permanente Institutional Review Board (IRB).
Kaiser Permanente Southern California is a large group‐model health maintenance organization in California which had enrolled more than 3 million members in each of the study years. It includes 14 hospitals. Health plan members have a demographic and socioeconomic profile similar to the overall southern California population.12 Over 20 000 deliveries occur at Kaiser Permanente hospitals every year. Comprehensive medical records are maintained for all patients.
This study utilized computerized electronic health system databases, which include information on member enrollment, inpatient and outpatient diagnoses, pharmacy medication dispensing records, patient vital statistics, and laboratory information. Kaiser Permanente Southern California maintains a perinatal database that includes all births within the Kaiser Foundation hospitals with maternal and infant linkage using unique identifiers. The perinatal database includes maternal characteristics, infant gender, gestational age at birth, birth weight, APGAR scores, delivery methods, and pregnancy‐associated complications.
Pregnant women exposed to beta‐blockers during their pregnancy were identified using pharmacy dispensing records. Patient was considered to be exposed to beta‐blocker during pregnancy if they filled a prescription for a beta‐blocker between their estimated conception date and the date of delivery. The reference group consisted of pregnant women who were not exposed to beta‐blockers at any time during their pregnancy.
Maternal comorbidities were identified by searching the KPSC Research Data Warehouse (which contains diagnoses from all ambulatory visits, emergency room visits, and inpatient admissions) using International Classification of Disease, 9th Revision, Clinical Modification (ICD‐9‐CM) codes. The self‐reported racial / ethnic data were obtained from California birth certificates.
2.2. Study outcomes
Fetal birth weights were obtained from California birth certificates. SGA was defined as having a birth weight below the 10th percentile for the corresponding gestational week. Low birth weight was defined as a birth weight of less than 2500 g (5 pounds 8 ounces). Very low birth weight was defined as birth weight less than 1500 g (3 pounds 5 ounces).
2.3. Statistics
Descriptive statistics for categorical data were reported in absolute numbers and percentages. Continuous variables were analyzed by calculating mean values and standard deviations. The prevalence of beta‐blocker use within each study year was noted for each beta‐blocker prescribed. Differences in categorical data between patient groups were compared by Fisher’s exact or chi‐squared tests. Differences in continuous data between patient groups were compared by Student’s t tests or ANOVA. P‐values <0.05 (2‐sided test) were considered statistically significant. Logistic regression analyses were used to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Multivariable logistic regression models were constructed to adjust for maternal age, gestational age, maternal race and ethnicity, body mass index, and maternal comorbidities (including hypertension, hyperlipidemia, diabetes, heart failure, stroke, arrhythmia, and renal insufficiency). Statistical analysis was performed using STATA IC 13 (StataCorp LP, College Station, TX).
3. RESULTS
3.1. Patient population
The cohort comprised 379 238 pregnancies between January 1, 2003 and December 31, 2014. Among this group, 4847 pregnancies (1.3%) were exposed to beta‐blockers.
Table 1 shows the number of pregnancies exposed to each beta‐blocker medication in each year during the study period. Overall, labetalol was the most commonly prescribed beta‐blocker, with 3357 pregnancies (0.89%) exposed during the study period. The second most commonly prescribed beta‐blocker was atenolol (638 pregnancies, 0.17%). This was followed by propranolol (489 pregnancies, 0.13%), and metoprolol (324 pregnancies, 0.09%). Use of acebutolol, nadolol, pindolol, bisoprolol, and carvedilol were rare.
Table 1.
Number of pregnancies exposed to different beta‐blocker subtypes by year
| 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Acebutolol | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| Atenolol | 79 | 79 | 64 | 85 | 73 | 61 | 38 | 47 | 40 | 36 | 16 | 20 | 638 |
| Bisoprolol | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 3 | 1 | 3 | 11 |
| Carvedilol | 0 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 2 | 2 | 1 | 2 | 13 |
| Labetalol | 92 | 134 | 167 | 227 | 241 | 262 | 292 | 325 | 358 | 421 | 418 | 420 | 3357 |
| Metoprolol | 16 | 34 | 15 | 25 | 24 | 22 | 25 | 28 | 29 | 31 | 31 | 44 | 324 |
| Nadolol | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 5 |
| Pindolol | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 6 |
| Propranolol | 44 | 45 | 37 | 46 | 35 | 35 | 30 | 37 | 49 | 50 | 47 | 34 | 489 |
| None | 29 833 | 28 289 | 28 574 | 30 046 | 31 036 | 31 257 | 30 582 | 30 468 | 31 863 | 33 637 | 33 834 | 34 972 | 374 391 |
3.2. Trends in beta‐blocker use from 2003 to 2014
The proportion of pregnancies exposed to beta‐blockers increased from 7.7 per 1000 pregnancies in 2003 to 14.8 per 1000 pregnancies in 2014. Among the individual beta‐blockers, use of labetalol increased from 3.1 per 1000 pregnancies in 2003 to 11.8 per 1000 pregnancies in 2014. In contrast, use of atenolol decreased from 2.6 per 1000 pregnancies in 2003 to 0.6 per 1000 pregnancies in 2014. Use of other beta blockers such as metoprolol and propranolol remained relatively stable during this time period.
3.3. Maternal characteristics
Table 2 shows the baseline maternal characteristics of the population. Women exposed to beta‐blockers were older and had higher body mass indices (BMI). Diagnoses of hypertension, hyperlipidemia, diabetes, heart failure, and a history of arrhythmia were more common among patients exposed to beta‐blockers.
Table 2.
Baseline maternal characteristics for pregnancies exposed to beta‐blockers
| Non‐exposed (n=374 391) | Any beta‐blockers (n=4847) | P‐valuea | Beta‐blocker | |||||
|---|---|---|---|---|---|---|---|---|
| Atenolol (n = 638) | Labetalol (n = 3357) | Metoprolol (n = 324) | Propranolol (n = 489) | P‐valueb | ||||
| Age (years) | 29.7 ± 5.9 | 33.1 ± 5.7 | <0.001 | 34.1 ± 5.6 | 33.3 ± 5.6 | 32.3 ± 5.6 | 30.7 ± 5.6 | <0.001 |
| Race/Ethnicity | ||||||||
| White | 92 030 (24.6%) | 1302 (26.9%) | <0.001 | 152 (23.8%) | 829 (24.7%) | 121 (37.4%) | 188 (38.4%) | <0.001 |
| Black | 31 910 (8.5%) | 784 (16.2%) | 103 (16.2%) | 573 (17.1%) | 44 (13.6%) | 56 (11.5%) | ||
| Hispanic | 194 660 (52.0%) | 2007 (41.4%) | 286 (44.8%) | 1411 (42.0%) | 112 (34.6%) | 186 (38.0%) | ||
| Asian | 46 266 (12.4%) | 606 (12.5%) | 78 (12.2%) | 442 (13.2%) | 38 (11.7%) | 41 (8.4%) | ||
| Other | 9525 (2.5%) | 148 (3.1%) | 19 (3.0%) | 102 (3.0%) | 9 (2.7%) | 18 (3.7%) | ||
| BMI (kg/m2) | 26.2 ± 6.1 | 31.6 ± 8.0 | <0.001 | 32.0 ± 8.2 | 32.2 ± 8.0 | 28.4 ± 7.3 | 28.1 ± 6.7 | <0.001 |
| HTN | 25 823 (6.9%) | 3698 (75.3%) | <0.001 | 513 (80.4%) | 2906 (86.6%) | 147 (45.4%) | 115 (23.5%) | <0.001 |
| HLD | 36 535 (9.8%) | 1362 (28.1%) | <0.001 | 232 (36.4%) | 979 (29.2%) | 66 (20.4%) | 74 (15.1%) | <0.001 |
| DM | 14 812 (4.0%) | 849 (17.5%) | <0.001 | 130 (20.4%) | 625 (18.6%) | 42 (13.0%) | 45 (9.2%) | <0.001 |
| CHF | 1066 (0.3%) | 144 (3.0%) | <0.001 | 25 (3.9%) | 64 (1.9%) | 29 (9.0%) | 9 (1.8%) | <0.001 |
| Arrhythmia | 16 022 (4.3%) | 989 (19.4%) | <0.001 | 163 (25.6%) | 392 (11.7%) | 197 (60.8%) | 165 (33.8%) | <0.001 |
| CKD | 1866 (0.5%) | 246 (5.1%) | <0.001 | 40 (6.3%) | 175 (5.2%) | 19 (5.9%) | 10 (2.0%) | 0.008 |
| Gravida | ||||||||
| 1 | 110 836 (29.6%) | 1104 (22.8%) | <0.001 | 137 (21.5%) | 783 (23.3%) | 69 (21.3%) | 102 (20.9%) | 0.53 |
| 2 | 105 243 (28.1%) | 1223 (25.2%) | 138 (21.6%) | 845 (25.2%) | 102 (31.5%) | 129 (26.4%) | ||
| >3 | 158 312 (42.3%) | 2520 (52.0%) | 363 (56.9%) | 1729 (51.5%) | 153 (47.2%) | 258 (52.8%) | ||
Values are n (%), or mean ± SD.
BMI, body mass index; HTN, hypertension; HLD, hyperlipidemia; DM, diabetes; CHF, congestive heart failure; CKD, chronic kidney disease.
Non‐exposed group vs patients exposed to any beta‐blockers. Student’s t tests for continuous variables, Fisher’s exact for categorical variables
Atenolol vs. labetalol vs metoprolol vs propranolol. ANOVA for continuous variables, chi‐squared for categorical variables.
Among the four most commonly prescribed beta‐blockers, the comorbidities present differed. A higher proportion of women exposed to atenolol or labetalol had a diagnosis of hypertension, whereas women exposed to metoprolol or propranolol were more likely to have a diagnosis of arrhythmia. A diagnosis of congestive heart failure was more commonly seen in patients exposed to metoprolol.
3.4. Obstetric findings
Obstetric findings are presented in Table 3. Beta‐blocker use was associated with higher rate of preeclampsia and eclampsia: 0.6% of the non‐exposed pregnancies were complicated by preeclampsia, whereas 1.9% of the beta‐blocker exposed pregnancies were complicated by preeclampsia. The rate of eclampsia was 0.1% in the non‐exposed group, and 0.5% in the beta‐blocker group. There was no significant difference in the rates of preeclampsia and eclampsia in women prescribed the four different kinds of beta‐blockers.
Table 3.
Obstetric findings
| Non‐exposed (n = 374 391) | Any beta‐blockers (n = 4847) | P‐valuea | Beta‐blocker | |||||
|---|---|---|---|---|---|---|---|---|
| Atenolol (n = 638) | Labetalol (n = 3357) | Metoprolol (n = 324) | Propranolol (n = 489) | P‐valueb | ||||
| Preeclampsia | 2330 (0.6%) | 94 (1.9%) | <0.001 | 13 (2.0%) | 74 (2.2%) | 4 (1.2%) | 3 (0.6%) | 0.09 |
| Eclampsia | 287 (0.1%) | 24 (0.5%) | <0.001 | 2 (0.3%) | 19 (0.5%) | 0 (0%) | 2 (0.4%) | 0.48 |
| Gestational age at delivery | 38.9 ± 1.9 | 37.4 ± 3.0 | <0.001 | 38.0 ± 2.7 | 37.1 ± 3.1 | 38.0 ± 2.7 | 38.5 ± 2.4 | <0.001 |
| Delivery method: | ||||||||
| Vaginal‐spont | 261 819 (70%) | 2301 (47.4%) | <0.001 | 309 (48.4%) | 1482 (44.2%) | 176 (54.3%) | 318 (65.1%) | <0.001 |
| Vaginal‐assisted | 8233 (2.2%) | 91 (1.9%) | 20 (3.2%) | 50 (1.5%) | 9 (2.8%) | 8 (1.6%) | ||
| Cesarean | 104 203 (27.8%) | 2455 (50.6%) | 309 (48.4%) | 1825 (54.3%) | 139 (42.9%) | 163 (33.3%) | ||
Values are n (%), or mean ± SD. Vaginal‐spont, spontaneous vaginal delivery; Vaginal‐assisted, assisted vaginal delivery.
Non‐exposed group vs patients exposed to any beta‐blockers. Student’s t‐ tests for continuous variables, Fisher’s exact for categorical variables.
Atenolol vs labetalol vs metoprolol vs propranolol. ANOVA for continuous variables, Chi‐squared for categorical variables.
Gestational age at delivery was lower in the beta‐blocker exposed group (37.4 ± 3.0 weeks in the beta‐blocker group vs 38.9 ± 1.9 in the non‐exposed group). Cesarean delivery was more common in the beta‐blocker exposed group compared to the non‐exposed group. Among the individual beta‐blockers, atenolol and labetalol were associated with a higher rate of cesarean delivery compared to metoprolol and propranolol.
3.5. Fetal outcomes
Exposure to beta‐blockers was associated with infants born with low birth weight (Table 4). Mean birth weight and % low birth weight (<2500 g) were 2996 ± 811 g and 21.5% for the beta‐blocker exposed group, which is significantly lower than the non‐exposed group (mean birth weight 3353 ± 554 g and % low birth weight 5.2%). Among the different kinds of beta‐blockers, exposure to labetalol was associated with the lowest mean birth weight (2926 ± 841 g) and highest rate of low birth weight (24.4%). This was followed by atenolol (mean birth weight 3058 ± 748 g, % low birth weight 18.0%). Exposure to metoprolol and propranolol were associated with higher mean birth weight and lower percentages of low birth weight compared to atenolol and labetalol. The rate of very low birth weight (<1500 g) was also highest in the group exposed to labetalol (7.4%).
Table 4.
Fetal outcome
| Non‐exposed (n = 374 391) | Any Beta‐Blockers (n = 4847) | P‐valuea | Beta‐blocker | |||||
|---|---|---|---|---|---|---|---|---|
| Atenolol (n = 638) | Labetalol (n = 3357) | Metoprolol (n = 324) | Propranolol (n = 489) | P‐valueb | ||||
| Infant Gender Male | 192 013 (51.3%) | 2569 (53.0%) | 0.02 | 322 (50.5%) | 1784 (53.1%) | 172 (53.1%) | 268 (54.8%) | 0.51 |
| Birth weight (g) | 3353 ± 554 | 2996 ± 811 | <0.001 | 3058 ± 748 | 2926 ± 841 | 3163 ± 702 | 3286 ± 651 | <0.001 |
| Low birth weight ( <2500g) | 19 393 (5.2%) | 1025 (21.2%) | <0.001 | 115 (18.0%) | 820 (24.4%) | 43 (13.3%) | 37 (7.6%) | <0.001 |
| Very Low birth weight (<1500 g) | 3441 (0.9%) | 291 (6.0%) | <0.001 | 24 (3.8%) | 248 (7.4%) | 10 (3.1%) | 8 (1.6%) | <0.001 |
| APGAR score at 1 min | 8.3 ± 1.1 | 7.9 ± 1.5 | <0.001 | 8.0 ± 1.4 | 7.9 ± 1.5 | 8.1 ± 1.3 | 8.2 ± 1.2 | <0.001 |
| Low APGAR score (<7) | ||||||||
| At 1 min | 16 755 (4.5%) | 487 (10.0%) | <0.001 | 52 (8.2%) | 378 (11.3%) | 19 (5.9%) | 34 (7.0%) | <0.001 |
| At 5 min | 3024 (0.8%) | 113 (2.3%) | <0.001 | 14 (2.2%) | 83 (2.5%) | 8 (2.5%) | 7 (1.4%) | 0.55 |
| At 10 min | 1430 (0.4%) | 46 (0.9%) | <0.001 | 5 (0.8%) | 31 (0.9%) | 3 (0.9%) | 6 (1.2%) | 0.89 |
Values are n (%), or mean ± SD. Vaginal‐spont, spontaneous vaginal delivery.
Non‐exposed group vs patients exposed to any beta‐blockers. Student’s t‐ tests for continuous variables, Fisher’s exact for categorical variables.
Atenolol vs labetalol vs metoprolol vs propranolol. ANOVA for continuous variables, Chi‐squared for categorical variables. Neonatal death, defined as death within 28 d of birth. Low birth weight, birth weight <2500 g. Very low birth weight, birth weight <1500 g.
APGAR scores at 1 minute were lower in the beta‐blocker exposed group compared to the non‐exposed group. The percentages of infants with APGAR scores lower than seven at 1, 5, and 10 minute of birth were higher in the beta‐blocker exposed group compared to the non‐exposed group.
Because mean gestational age at delivery was slightly lower in the beta‐blocker groups, we evaluated infant birth weights based on whether they were small for their gestational age at birth (SGA) (Table 5). Exposure to any beta‐blocker was associated with being SGA (adjusted odds ratio 2.6, 95% CI: 2.4‐2.9). However, the odds of being SGA differed among the different beta‐blocker medications. Exposure to atenolol and labetalol was associated with increased odds of being born SGA (atenolol: adjusted OR 2.4, 95% CI: 1.7‐3.3; labetalol: adjusted OR 2.9, 95% CI: 2.6‐3.2). There was no significant association between metoprolol and propranolol exposure and the odds of being born SGA.
Table 5.
Association of small for gestational age with beta‐blocker exposure
| Beta‐blocker | SGA <10% | Univariable analysis | Multivariable analysis | ||
|---|---|---|---|---|---|
|
Unadjusted Odds ratio (95% CI) |
P value |
Adjusted Odds ratioa (95% CI) |
P‐value | ||
| Non‐exposed | 32 488 (8.7%) | 1.0 | 1.0 | ||
| Any Beta‐blockers | 796 (16.4%) | 2.1 (1.9‐2.2) | <0.001 | 2.6 (2.4‐2.9) | <0.001 |
| Atenolol | 112 (17.6%) | 2.2 (1.8‐2.7) | <0.001 | 2.4 (1.7‐3.3) | <0.001 |
| Labetalol | 590 (17.6%) | 2.2 (2.0‐2.5) | <0.001 | 2.9 (2.6‐3.2) | <0.001 |
| Metoprolol | 35 (10.8%) | 1.3 (0.9‐1.8) | 0.2 | 1.5 (0.9‐2.3) | 0.07 |
| Propranolol | 50 (10.3%) | 1.2 (0.9‐1.6) | 0.25 | 1.3 (0.9‐1.9) | 0.19 |
Small for gestational age defined as <10th percentile.
Adjusted for maternal age, gestational age, white race, body mass index, hypertension, hyperlipidemia, diabetes, heart failure, stroke, arrhythmia, and renal insufficiency.
4. Discussion
The prevalence of cardiovascular disease in pregnant women is increasing, with up to 4% of pregnancies complicated by maternal cardiovascular conditions.2 The most common cardiac issues seen in pregnant women are hypertension, gestational hypertension, preeclampsia, heart failure, congenital heart disease, arrhythmia, and valvular heart disease.2, 13, 14 All of these conditions can be treated with beta‐blockers. It is therefore perhaps not surprising that beta‐blockers are the most commonly used class of medication for treating cardiac conditions in pregnant women.1 In this study of a cohort of 379 238 pregnancies, 4847 pregnancies (1.3%) were exposed to beta‐blockers. Over the 12‐year period from 2003 to 2014, use of beta‐blockers nearly doubled, increasing from 7.7 per 1000 pregnancies in 2003 to 14.8 per 1000 pregnancies in 2014. Given such common usage, it is important to evaluate the safety of individual drugs within this pharmacologic class in pregnant women.
The potential risk with beta‐blockers of fetal growth restriction is of particular concern. Fetal growth restriction is associated with an increased risk of cardiovascular and metabolic disease later in life, and adversely impacts stature and intellectual development in these children.15, 16, 17 Early reports raised the concern of an association of atenolol exposure with intrauterine growth restriction.8, 18, 19 However, later studies involving other beta‐blockers such as labetalol and metoprolol yielded conflicting results, with some studies showing a high rate of fetal growth restriction,5 and others showing no difference or improved fetal outcomes.20, 21, 22 Meta‐analyses involving more than 30 studies showed an inconsistent association with infants born SGA.6, 23
Several mechanisms might contribute to the potential negative effects of beta‐blockers on fetal growth. Animal studies showed that fetal bradycardia caused by beta‐blocker exposure negatively affects fetal cardiac output and adversely affected fetal development.24 The negative inotropic and chronotropic effects of beta‐blockers can lead to a reduction in maternal cardiac output, which in turn affect fetal growth.25, 26 Beta‐blockers also lower blood pressure, and the treatment‐induced decline in maternal blood pressure can lead to impaired placental perfusion that may adversely affect fetal growth.27 Certain beta‐blocker effects may be receptor‐specific. For example, myometrial relaxation is mediated by beta‐2 receptors, and non‐selective beta‐blockers may adversely affect or even counteract this process. On the other hand, alpha‐blocking activity can lead to vasodilation and could improve uteroplacental blood flow. As such, the differential receptor selectivity of various agents in the beta‐blocker class may impart diverse effects on fetal development.
In our study population, a higher proportion of infants exposed to beta‐blockers had low birth weight (<2500 g) and very low birth weight (<1500 g). Further, certain groups of exposed infants had a higher rate of low APGAR scores compared to the unexposed population. The lower infant birth weight may be partly related to the earlier gestational age at delivery. Therefore, we also evaluated the proportion of infants born SGA in the beta‐blocker group. We found an overall association between beta‐blocker exposure and being born SGA (less than 10th percentile). When evaluating each beta‐blocker agents individually, we found that exposure to atenolol or labetalol was associated with increased odds of birth weight SGA. In contrast, there was no significant association between metoprolol and propranolol exposure with birth weight SGA. This finding suggests that the effect of beta‐blockers on low birth weight may not be a class effect, but rather, an effect associated with specific medications within the class.
While this differential effect could be due to pharmacological differences between beta‐blockers, it is also possible that the differences in underlying maternal cardiac conditions and confounding by indication played a role. The association between beta‐blocker exposure and fetal congenital cardiac anomalies is felt to be related to confounding.28 In our population, atenolol and labetalol were more commonly used in patients with maternal hypertension. Maternal hypertension itself is associated with many risk factors for intrauterine growth restriction.29 While we adjusted for known maternal comorbidities including age, diabetes, kidney disease, heart failure and others, as in all observational studies, it is possible that there are unknown confounders that were not adjusted. Ultimately, a randomized trial to directly compare different beta‐blockers would be useful in validating the findings observed in this population.
One limitation of our study is that beta‐blocker exposure was based on pharmacy dispensing information. Because of the way this study was designed, it was not possible to ascertain if the mothers actually took the medication. On the other hand, because pharmacy dispensing information was used, we were able to avoid any recall bias that might be associated with studies utilizing surveys to determine medication exposure. Another limitation is that of confounding by indication. Many of the conditions for which beta blockers are indicated, such as preeclampsia, arrhythmia, and heart failure, are potentially themselves associated with poor fetal outcomes. While we attempted to adjust for all known confounders, it is impossible in any observational study to adjust for confounders that are not known, or to assume causality based on association.
Despite the limitations, because pregnant women are often excluded from randomized controlled trials, observational studies are still very important in providing information on the effectiveness and safety of medications in pregnancy. Since the outcomes of interest are rare, studies using large population‐based datasets are invaluable.
Given the increasing prevalence of cardiovascular disease in pregnant women, more studies that address the safety of cardiovascular medications and their risks of adverse fetal outcomes are important and should be performed.
CONFLICTS OF INTEREST
The authors report no conflicts of interest to disclose.
Duan L, Ng A, Chen W, Spencer HT, Lee M‐S. Beta‐blocker subtypes and risk of low birth weight in newborns. J Clin Hypertens. 2018;20:1603–1609. 10.1111/jch.13397
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