Abstract
Introduction
Hyperaldosteronism (HA) is a common cause of secondary hypertension and may contribute to resistant hypertension (RH). The authors sought to determine and characterize HA screening, positivity rates, and mineralocorticoid receptor antagonist (MRA) use among patients with RH.
Methods
A cross-sectional study was performed within Kaiser Permanente Southern California (7/1/2012–6/30/2017). Using contemporary criteria, RH was defined as blood pressure uncontrolled (≥ 130/80) on ≥ 3 medications or requiring ≥ 4 antihypertensive medications. The primary outcome was screening rate for HA defined as any aldosterone and plasma renin activity measurement. Secondary outcomes were HA screen positive rates and MRA use among all patients with RH. Multivariable logistic regression analysis was used to estimate odds ratio (with 95% confidence intervals) for factors associated with HA screening and for patients that screened positive.
Results
Among 102,480 patients identified as RH, 1977 (1.9%) were screened for HA and 727 (36.8%) screened positive for HA. MRA use was 6.5% among all patients with RH (22.5% among screened, 31.2% among screened positive). Black race, potassium < 4, bicarbonate > 29, chronic kidney disease, obstructive sleep apnea, and systolic blood pressure were associated with HA screening, but only Black race (1.55 [1.20–2.01]), potassium (1.82 [1.48–2.24]), bicarbonate levels (1.39 [1.10–1.75]), and diastolic blood pressure (1.15 [1.03–1.29]) were associated with positive screenings.
Conclusion
The authors’ findings demonstrate low screening rates for HA among patients with difficult-to-control hypertension yet a high positivity rate among those screened. Factors associated with screening did not always correlate with screening positive. Screening and targeted use of MRA may lead to improved blood pressure control and outcomes among patients with RH.
Keywords: hyperaldosteronism, resistant hypertension, hypertension, epidemiology, pharmacotherapy
Introduction
Although hypertension (HTN) is the most common condition managed by physicians and the single greatest risk factor for cardiovascular disease, blood pressure control remains a challenge for a large proportion of the population with HTN.1,2 Many patients either have uncontrolled blood pressure or have a large medication burden often termed as resistant hypertension (RH). RH is defined as blood pressure that is not controlled while on 3 or more antihypertensive medications including a diuretic or controlled on 4 or more medications.1 With the 2017 American College of Cardiology/American Health Association (ACC/AHA) guidelines for goal blood pressure, RH rates up to 18% have been described among the HTN population, and these rates are higher among patients with cardiovascular disease or chronic kidney disease (CKD).1,3–5 Not surprisingly, patients with RH have greater risk for adverse outcomes including cardiovascular events.4,6–8 The ACC/AHA guidelines recommend that patients with RH be evaluated for secondary causes of HTN including hyperaldosteronism (HA).1,6 Screening for HA is recommended for anyone with difficult-to-control hypertension including RH.1,9
HA may be an important and overlooked cause of RH, which is associated with worsened clinical outcomes including higher cardiovascular events and kidney disease outcomes.10 In fact, HA is one of the most common causes of secondary HTN, described in up to 12% to 20% of patients with RH and 5% to 10% among all patients with HTN.11 Aldosterone excess can occur in varying degrees across HTN phenotypes.12 Given the high prevalence of HA that may not be associated with clinical manifestations (eg, hypokalemia, alkalosis), there appears to be low rates of screening for HA within the population with RH despite recommendations from multiple societies.9,13,14
Screening for HA usually begins with measuring aldosterone and renin values to calculate an aldosterone-to-renin ratio (ARR). An ARR > 30 with an aldosterone level > 15 has typically been used as the benchmark of a positive screen.9 The Endocrine Society does not specify an ARR cutoff but suggests an ARR range from 20 to 40 that could be used to detect HA, especially given the wide variability in the diagnostic performance of ARR.9,15 Overall, screening for HA among patients with HTN and RH appears low.16 Recent observations from the US Department of Veterans Affairs (VA) and the midwestern United States have described HA screening rates of 1.6% and 2.4%, respectively, among the population with RH.14,17 Furthermore, these studies did not report the positivity rates among those screened for HA. Identifying opportunities such as diagnosing HA may lead to improved care and outcomes for patients with RH.
Kaiser Permanente Southern California has a large and diverse population with approximately 900,000 patients with HTN. The authors have previously described and characterized RH among the Kaiser Permanente Southern California population using the 2017 ACC/AHA and prior Joint National Committee HTN definitions for RH. In this study, the authors performed a theoretical observational study superimposing current ACC/AHA definitions for HTN onto the 2012–2017 HTN management landscape to identify RH. Using this “RH cohort” from the Kaiser Permanente Southern California population with HTN, they sought to determine and characterize the rate of HA screening and the positivity rate among those screened. Furthermore, they evaluated the overall rate of mineralocorticoid receptor antagonist (MRA) use among patients with RH. Their objective was to determine whether a potential diagnostic gap exists within an integrated health system with successful blood pressure control rates and a standardized approach toward HTN.18
Methods
A cross-sectional study was performed within Kaiser Permanente Southern California, an integrated health care system in Southern California comprising 15 medical centers and approximately 236 satellite clinics. Members within Kaiser Permanente Southern California have similar coverage benefits, co-pays for visits and medications, and access to care. All health information was collected as part of routine clinical care. This study was approved by the Kaiser Permanente Southern California Institutional Review Board and exempted from informed consent (IRB #12169).
The population with RH within Kaiser Permanente Southern California has been previously described.3,4 In brief, patients with RH were defined as 1) having blood pressure that is uncontrolled (≥ 130/80 mmHg) on ≥ 3 antihypertensive medications regardless of diuretic use or 2) on ≥ 4 antihypertensive medications. The authors did not require diuretic use for definition of RH given the variabilities of antihypertensive medication use in real-world populations. Index date was defined as when the patient was first identified as having RH within the period 7/1/2014–6/30/2015. This index date was used to determine demographic and laboratory characteristics of patients with RH. All serum aldosterone and plasma renin activity (PRA) measurements performed 2 years prior or 2 years after the index date were extracted (7/1/2012–6/30/2017). The authors obtained member demographics, comorbidities, vital signs, laboratory values, and medications use from the Kaiser Permanente Southern California electronic health records including the pharmacy and analytic database (Supplemental Table 1).
The primary outcome evaluated was the rate of screening for HA, which was defined as a patient with plasma aldosterone (ng/dL) and PRA (ng/mL/h) measurement results (Quest Diagnostics Nichols Institute, San Juan Capistrano, CA). An ARR was calculated as a function of aldosterone divided by PRA. The authors evaluated 2 secondary outcomes. One secondary outcome was the rate of patients with RH who screened positive for HA. A positive screen was defined as ARR > 20 regardless of aldosterone value. The authors used an ARR > 20 (vs > 30) threshold given continuous observations demonstrating the higher prevalence of HA than previously assumed.12 Additionally, the Endocrine Society does not specify an ARR cutoff but suggests an ARR range from 20 to 40 that could be used to detect HA, especially given the wide variability in the diagnostic performance of ARR.9,15 ARR > 20 does have higher sensitivity compared to ARR > 30, a typical benchmark of a positive screen.19 The authors did perform sensitivity analyses using an ARR cutoff of > 30. Finally, the other secondary outcome was the rate of MRA use among all patients with RH. They also evaluated MRA use by patients screened for HA and by HA screen positivity status.
Statistical analyses
The authors calculated percentages for both primary and secondary outcomes. Comparisons were made in terms of demographics, vitals, smoking, preexisting comorbidities, laboratory values, and MRA use among patients screened vs not screened and those who screened positive vs those who screened negative using t-tests and χ2 tests. Multivariable logistical regression analysis was used to determine variables associated with the ARR testing vs no testing. Variables adjusted in the logistic regressions were age, sex, race and ethnicity, body mass index (BMI), CKD, heart failure, obstructive sleep apnea (OSA), serum potassium, and serum bicarbonate. In addition, the authors performed multivariable logistic regressions to determine factors associated with screening positive for HA based on ARR > 20 and > 30. P < 0.05 was considered statistically significant.
Results
RH cohort characteristics
A total of 102,480 individuals were identified as RH during the observation period. The mean age of the study population was 69 years (Table 1). Females accounted for 51.6% of the population. The race and ethnicity composition of the population was 44.4% non-Hispanic White, 24.5% Hispanic, 19.5% Black, 9.8% Asian and Pacific Islander, 0.2% American Indian, and 1.7% Other. Mean blood pressure was 131/70 mmHg. Of the RH cohort, 20.3% had preexisting CKD, while diabetes was present in 42.9%. The mean sodium was 139 mg/dL and mean creatinine was 1.2 mg/dL. Of the population with RH, 6.5% were found to be on an MRA.
Table 1:
Cohort characteristics and labs—population with resistant hypertension, screened for hyperaldosteronism and not screened for hyperaldosteronism
| Characteristics | RH | Screened | Not screened | p value |
|---|---|---|---|---|
| (N = 102,480) | (N = 1977) | (N = 100,503) | ||
| Age at index, mean (SD) | 68.6 (11.65) | 64.9 (12.51) | 68.6 (11.62) | < 0.0001 |
| Female, n (%) | 52,918 (51.6%) | 1035 (52.4%) | 51,883 (51.6%) | 0.521 |
| Race | < 0.0001 | |||
| White, n (%) | 45,528 (44.4%) | 742 (37.5%) | 44,786 (44.6%) | |
| Black, n (%) | 19,933 (19.5%) | 549 (27.8%) | 19,384 (19.3%) | |
| Hispanic, n (%) | 25,072 (24.5%) | 440 (22.3%) | 24,632 (24.5%) | |
| Asian and Pacific Islander, n (%) | 9995 (9.8%) | 210 (10.6%) | 9785 (9.7%) | |
| American Indian and Alaska Native, n (%) | 174 (0.2%) | 2 (0.1%) | 172 (0.2%) | |
| Other/Unknown, n (%) | 1778 (1.7%) | 34 (1.7%) | 1744 (1.7%) | |
| SBP, mean (SD) | 130.7 (13.92) | 134.3 (17.11) | 130.6 (13.84) | < 0.0001 |
| DBP, mean (SD) | 70.4 (11.44) | 73.4 (13.28) | 70.4 (11.39) | < 0.0001 |
| BMI ≥ 30, n (%) | 51,658 (52.1%) | 1041 (53.6%) | 50,617 (52.1%) | 0.176 |
| Smoking status | 0.369 | |||
| Current smoker, n (%) | 2883 (2.8%) | 58 (2.9%) | 2825 (2.8%) | |
| Former smoker, n (%) | 36,578 (35.7%) | 669 (33.8%) | 35,909 (35.7%) | |
| Missing, n (%) | 5923 (5.8%) | 121 (6.1%) | 5802 (5.8%) | |
| Never, n (%) | 57,096 (55.7%) | 1129 (57.1%) | 55,967 (55.7%) | |
| Comorbidities | ||||
| Ischemic heart disease, n (%) | 1239 (1.2%) | 37 (1.9%) | 1202 (1.2%) | 0.007 |
| CHF, n (%) | 6260 (6.1%) | 123 (6.2%) | 6137 (6.1%) | 0.832 |
| Cerebrovascular disease, n (%) | 697 (0.7%) | 28 (1.4%) | 669 (0.7%) | < 0.0001 |
| Dementia, n (%) | 588 (0.6%) | 8 (0.4%) | 580 (0.6%) | 0.315 |
| Chronic kidney disease, n (%) | 19,394 (20.3%) | 497 (26.2%) | 18,897 (20.2%) | < 0.0001 |
| Peripheral artery disease, n (%) | 2282 (2.2%) | 54 (2.7%) | 2228 (2.2%) | 0.125 |
| Sleep apnea, n (%) | 7748 (7.6%) | 231 (11.7%) | 7748 (7.6%) | < 0.0001 |
| Diabetes mellitus, n (%) | 43,969 (42.9%) | 856 (43.3%) | 43,113 (42.9%) | 0.722 |
| Laboratory | ||||
| Aldosterone, mean (SD) | 12.8 (16.94) | 12.8 (15.29) | 12.9 (20.37) | 0.993 |
| Renin, mean (SD) | 3.1 (7.86) | 3.2 (8.15) | 3.0 (6.57) | 0.881 |
| Sodium, mean (SD) | 138.6 (2.98) | 138.6 (3.26) | 138.6 (2.97) | 0.129 |
| Bicarbonate, mean (SD) | 27.4 (2.83) | 27.4 (3.08) | 27.4 (2.83) | 0.421 |
| Potassium, mean (SD) | 4.1 (0.47) | 4.0 (0.54) | 4.1 (0.47) | < 0.0001 |
| Creatinine, mean (SD) | 1.2 (0.93) | 1.3 (1.08) | 1.2 (0.93) | < 0.0001 |
| MRA usage, n (%) | 6631 (6.5%) | 445 (22.5%) | 6186 (6.2%) | < 0.0001 |
BMI, body mass index; CHF, congestive heart failure; DBP, diastolic blood pressure; MRA, mineralocorticoid receptor antagonist; SBP, systolic blood pressure; SD, standard deviation.
Screening for HA
Of the 102,480 patients with RH, 1977 (1.9%) were screened for HA (Figure 1). Compared to patients not screened for HA, those screened for HA were more likely to be younger (65 years vs 69 years), more likely to be Black (27.8% vs 19.3%) or Asian (10.6% vs 9.7%), and more likely to have a higher blood pressure (134/73 vs 131/70 mmHg; Table 1). Patients screened for HA were also found to have more preexisting CKD (26.2% vs 20.2%), cerebrovascular disease (1.4% vs 0.7%), and OSA (11.7% vs 7.6%) compared to those not screened for HA (Table 1). Preexisting diabetes and heart failure were similar between both those screened and not screened for HA. Patients screened for HA were found to have a lower baseline potassium (4.0 mEq/dL vs 4.1 mEq/dL) and higher creatinine (1.3 mg/dL vs 1.2 mg/dL) compared to those not screened. Patients with evidence of screening for HA were also more likely to be on an MRA compared to those not screened for HA (22.5% vs 6.2%).
Figure 1:
In the period July 1, 2014, through June 30, 2015, 825,068 adults were identified with hypertension. Those without 12 months’ continuous membership prior to and after index date, secondary hypertension, and without a blood pressure measurement were excluded. A total of 102,480 individuals were found to have resistant hypertension (systolic blood pressure < 130/80 while on 3 or more medications or being on 3 medications regardless of blood pressure). Among this patient cohort, laboratory measurements were assessed between 2012 and 2017, which demonstrated that 1977 (1.9%) resistant hypertension patients were screened for hyperaldosteronism and of whom 727 (36.8%) screened positive (ARR > 20). ARR = aldosterone-to-renin ratio.
Screened Positive for HA
Of the 1977 patients with RH screened for HA, 36.8% (727) screened positive for HA (defined as ARR > 20; Figure 1). Those who screened positive for HA were more likely to be younger (64 years vs 66 years) and Black (33.4% vs 24.5%) compared to those who did not screen positive (Table 2). They also were found to have a higher baseline diastolic blood pressure (76 mmHg vs 72 mmHg) compared to those who did not screen positive. Those who screened positive had a higher aldosterone (17.9 ng/dL vs 9.7 ng/dL), lower renin (0.3 pg/mL vs 4.9 pg/mL), lower potassium (3.9 mEq/dL vs 4.1 mEq/dL), and lower creatinine (1.2 mg/dL vs 1.4 mg/dL) compared to those who did not screen positive. The patients that screened positive for HA had less preexisting CKD (19.6% vs 30.0%) and were more likely to be on an MRA compared to those who did not screen positive (31.2% vs 17.4%).
Table 2:
Cohort characteristics—screened positive vs negative for hyperaldosteronism
| Characteristics | Screened | Positive Screen | Negative Screen | p value |
|---|---|---|---|---|
| (N = 1977) | (N = 727) | (N = 1250) | ||
| Age at index, mean (SD) | 64.9 (12.51) | 63.6 (11.45) | 65.6 (13.04) | < 0.0001 |
| Female, n (%) | 1035 (52.4%) | 357 (49.1%) | 678 (54.2%) | 0.028 |
| Race | < 0.0001 | |||
| White, n (%) | 742 (37.5%) | 236 (32.5%) | 506 (40.5%) | |
| Black, n (%) | 549 (27.8%) | 243 (33.4%) | 306 (24.5%) | |
| Hispanic, n (%) | 440 (22.3%) | 160 (22.0%) | 280 (22.4%) | |
| Asian and Pacific Islander, n (%) | 210 (10.6%) | 70 (9.6%) | 140 (11.2%) | |
| American Indian and Alaska Native, n (%) | 2 (0.1%) | 1 (0.1%) | 1 (0.1%) | |
| Other/Unknown, n (%) | 34 (1.7%) | 17 (2.3%) | 17 (1.4%) | |
| SBP, mean (SD) | 134.3 (17.11) | 135.2 (16.85) | 133.9 (17.25) | 0.198 |
| DBP, mean (SD) | 73.4 (13.28) | 75.6 (12.73) | 72.0 (13.43) | < 0.0001 |
| BMI ≥ 30, n (%) | 1041 (53.6%) | 396 (55.5%) | 645 (52.5%) | 0.211 |
| Smoking status | < 0.0001 | |||
| Current smoker, n (%) | 58 (2.9%) | 22 (3.0%) | 36 (2.9%) | |
| Former smoker, n (%) | 669 (33.8%) | 211 (29.0%) | 458 (36.6%) | |
| Missing, n (%) | 121 (6.1%) | 37 (5.1%) | 84 (6.7%) | |
| Never, n (%) | 1129 (57.1%) | 457 (62.9%) | 672 (53.8%) | |
| Comorbidities | ||||
| Ischemic heart disease, n (%) | 37 (1.9%) | 11 (1.5%) | 26 (2.1%) | 0.370 |
| CHF, n (%) | 123 (6.2%) | 33 (4.5%) | 90 (7.2%) | 0.018 |
| Cerebrovascular disease, n (%) | 28 (1.4%) | 10 (1.4%) | 18 (1.4%) | 0.907 |
| Dementia, n (%) | 8 (0.4%) | 1 (0.1%) | 7 (0.6%) | 0.154 |
| Chronic kidney disease, n (%) | 497 (26.2%) | 135 (19.6%) | 362 (30.0%) | < 0.0001 |
| Peripheral artery disease, n (%) | 54 (2.7%) | 10 (1.4%) | 44 (3.5%) | 0.005 |
| Sleep apnea, n (%) | 231 (11.7%) | 92 (12.7%) | 139 (11.1%) | 0.306 |
| Diabetes mellitus, n (%) | 856 (43.3%) | 283 (38.9%) | 573 (45.8%) | 0.003 |
| Laboratory | ||||
| Aldosterone, mean (SD) | 12.8 (15.29) | 17.9 (20.06) | 9.7 (10.28) | < 0.0001 |
| Renin, mean (SD) | 3.2 (8.15) | 0.3 (0.32) | 4.9 (9.94) | < 0.0001 |
| Sodium, mean (SD) | 138.6 (3.26) | 139.0 (3.14) | 138.4 (3.31) | < 0.0001 |
| Bicarbonate, mean (SD) | 27.4 (3.08) | 27.9 (2.92) | 27.1 (3.12) | < 0.0001 |
| Potassium, mean (SD) | 4.0 (0.54) | 3.9 (0.52) | 4.1 (0.55) | < 0.0001 |
| Creatinine, mean (SD) | 1.3 (1.08) | 1.2 (0.88) | 1.4 (1.18) | 0.001 |
| MRA usage, n (%) | 445 (22.5%) | 227 (31.2%) | 218 (17.4%) | < 0.0001 |
BMI, body mass index; CHF, congestive heart failure; DBP, diastolic blood pressure; MRA, mineralocorticoid receptor antagonist; SBP, systolic blood pressure; SD, standard deviation.
Regression analyses for HA screening
After adjustment for age, sex, race and ethnicity, BMI, baseline labs, blood pressure, and preexisting conditions, Black race (odds ratio [OR] 1.35; 95% confidence interval [CI] 1.19–1.52), potassium < 4 mg/dL (OR 1.55; 95% CI 1.41–1.71), estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 (OR 1.80; 95% CI 1.60–2.01), preexisting OSA (OR 1.55; 95% CI 1.33–1.79), and every 10 mmHg increase in systolic blood pressure (OR 1.17; 95% CI 1.13–1.21) were all found to have increased likelihood for HA screening. BMI ≥ 30 (OR 0.84; 95% CI 0.75–0.92) and older age (every 5 year increase; OR 0.87; 95% CI 0.85–0.89) were less likely to be screened for HA (Figure 2 and Supplemental Table 2).
Figure 2:
After adjustment for sex, age, race, BMI, baseline labs, blood pressure, and preexisting conditions, Black race, potassium < 4 mg/dL, eGFR < 60 mL/min/1.73 m2, preexisting OSA, and every 10 mmHg increase in systolic blood pressure were all found to have increased likelihood for HA screening. BMI = body mass index; CI = confidence interval; CL = confidence limits; CKD = chronic kidney disease; OSA = obstructive sleep apnea.
Risk Factors for Screening Positive for HA
In multivariable logistic regression analyses adjusting for age, sex, race and ethnicity, BMI, baseline labs, OSA, and blood pressure, OR (95% CI) for screening positive for HA were 1.55 (1.20–2.01), 1.82 (1.48–2.24), 1.39 (1.10–1.75), and 1.15 (1.03–1.29) for Black race; potassium < 4 mEq/dL; bicarbonate ≥ 30 mmol/L; and every 10 mmHg increase in diastolic blood pressure, respectively (Figure 3 and Supplemental Table 3). Age, sex, BMI, OSA, and systolic blood pressure were not associated with increased likelihood for screening positive for HA. CKD with eGFR < 60 mL/min/1.73 m2 was found to be less likely to screen positive for HA (OR 0.70; 95% CI 0.55–0.90) compared to those with eGFR ≥ 60 mL/min/1.73 m2.
Figure 3:
After adjusting for age, sex, race, BMI, baseline labs, OSA, and blood pressure, Black race, potassium < 4 mg/dL, bicarbonate ≥ 30 mmol/L, and every 10 mmHg increase in diastolic blood pressure were associated with increased likelihood for screening positive for HA. BMI = body mass index; CI = confidence interval; CKD = chronic kidney disease; CL = confidence limits; OSA = obstructive sleep apnea.
Sensitivity analyses using ARR > 30
Sensitivity analyses were performed to define HA as ARR > 30 instead of ARR > 20. Of the 1977 patients screened for HA, 569 (28.7%) were found to have an ARR > 30 (Supplemental Table 4). Those who screened positive for HA by ARR > 30 were more likely to be younger (63 years vs 66 years) and with higher baseline diastolic blood pressure (76 mmHg vs 72 mmHg) compared to those who did not screen positive. Similar to findings for ARR > 20, those who screened positive for HA with ARR > 30 were also found to have a higher aldosterone (19.3 ng/dL vs 10.0 ng/dL), lower renin (0.2 pg/mL vs 4.4 pg/mL), lower potassium (3.9 mEq/dL vs 4.0 mEq/dL), and lower creatinine (1.2 mg/dL vs 1.3 mg/dL) compared to those who did not screen positive. The patients who screened positive for HA had less preexisting CKD (17.4% vs 28.2%) and were more likely to be on an MRA compared to those who did not screen positive (34.3% vs 17.8%).
Discussion
Among a large diverse population with HTN and difficult-to-control blood pressure, which would meet criteria for RH using contemporary guidelines, the authors observed low HA screening rates (1.9%). Given the higher rates of HA than previously assumed and its association with RH, the authors feel that their findings suggest an opportunity to improve diagnostic practices within an integrated health system. Although their findings were consistent with previous observations from other health systems across the United States including the VA health system, they were additionally able to report the results of HA screening. Overall, they found a high positive screen rate of 36.8% (ARR > 20) and 28.7% (ARR > 30), suggesting there are missed opportunities for diagnosis and targeted management of hypertension. The authors used an ARR > 20 cutoff and thus had greater positive results compared to using an ARR > 30 cutoff but studied both rates. The ARR > 20 cutoff has higher19 sensitivity compared to a typical benchmark of positive screen, but given the high rates of HA among patients with RH, health systems and practitioners might consider the lower ARR cutoff for screening.
Patients with RH are 47% more likely to suffer combined outcomes of death, cardiovascular disease, stroke, or CKD.4 RH places additional burdens on patients and health care systems. Furthermore, RH that is secondary to HA is associated with worsened clinical outcomes including higher cardiovascular events and kidney disease outcomes compared to RH alone.10,20 Excess aldosterone exposure can lead to renal fibrosis, renal vascular disease, and podocyte injury leading to a rapid decline in eGFR.21,22 Patients with HA have been shown to have higher rates of cardiovascular events compared with patients with essential HTN including atrial fibrillation, stroke, structural heart disease, and myocardial infarction.10,20,21
In addition to RH, HA screening is also recommended for patients with sustained blood pressure ≥ 150/100 mmHg, spontaneous or diuretic-induced hypokalemia, adrenal incidentaloma, OSA, family history of early HTN or early cerebrovascular disease, and first-degree relatives of patients with HA. The low rate of testing and diagnoses of patients with HA is consistent with the literature showing a high rate of missed diagnosis of other conditions. In general, a failure to screen for HA when there are clear recommendations can be viewed as a diagnostic error. Diagnostic errors are estimated to affect 12 million Americans per year in the ambulatory setting.23
When looking at the characteristics of patients who were screened and those who screened positive, the authors observed that there may be differences in clinical cues between HA screened patients and patients who actually screened positive. They found that traditional clinical cues—low serum potassium, elevated serum bicarbonate levels, and elevated systolic blood pressure—were associated with screening. They observed that high diastolic blood pressure was associated with positive screen results, which may be consistent with volume expansion from high aldosterone levels.24 Interestingly, OSA was not associated with a positive screen result.
There are various reasons for low HA screening in RH. Practitioners may be dissuaded by the fact that concurrent medications may affect the results and interpretation of the ARR. In addition, intraindividual variability of the ARR exists.12,25 Not only is screening difficult, but confirmatory testing, imaging, lateralization of a potential adenoma, and treatment options are all complex issues for both clinicians and patients. Instead, practitioners may opt for empiric treatment with MRAs, which is suggested by the higher rate of MRA use over HA screening in this study. The authors could not account for practitioner reasoning behind screening practice patterns where many may have felt that their treatment may not have been altered by screening.
Even though treatment has been shown to reduce adverse outcomes in patients with HA, the authors’ data demonstrate that the overall treatment with MRAs was low regardless of HA screening or positivity. A prior VA population study described that HA screening among the population with RH was also very low along with a low rate of MRA use.17 There is possibly a practitioner discomfort or fear of use with MRAs. In the Kaiser Permanente algorithm of treatment of HTN, MRAs are listed as the fourth line agent assuming eGFR ≥ 60 mL/min/1.73 m2 and potassium < 4.5 mEq/dL (data not shown).
There are potential limitations that may confound the interpretation of these findings. This retrospective study included the period where blood pressure goals were < 140/90 mmHg preceding the 2017 AHA/ACC guidelines. Thus, the identification of patients with RH can be viewed as theoretical for that period because practitioners were managing to a goal blood pressure of < 140/90. Nevertheless, patients on 3 or more medicines can be viewed as patients with more difficult-to-treat hypertension. The authors only evaluated a 4-year window and could not comprehensively search all of each patient’s history for HA screening. Laboratory results were obtained from Kaiser Permanente Southern California only and may have missed patients who had labs measured elsewhere. The authors did not have information on medication adherence, thus the population with RH may have included patients who were nonadherent and not truly resistant. The authors also did not require a stricter definition of RH where 1 of the medications had to be a diuretic. Human adipocytes secrete potent mineralocorticoids-releasing factors, suggesting a direct link between obesity and hypertension,26 and in such individuals rather than screening for HA, practitioners may have empirically used MRA therapy. PRA and aldosterone variations can occur and also are affected by medications that contribute to the variability and diagnostic performance of the ARR. Other methods for hyperaldosteronism screening, (eg, using PRA < 1 ng/mL/h, aldosterone > 15 ng/dL), were not evaluated. Indication bias for screening may reflect heterogeneity in practice patterns by different practitioners.
Despite these limitations, strengths of this study included a large, diverse population with RH that was followed within an integrated health system of a real-world clinical environment. The authors were able to capture routine practice patterns and obtain a comprehensive capture of medications using both the comprehensive electronic health records and internal pharmacy records.
Conclusion
The authors’ findings from a large diverse population of patients with HTN in a real-world environment demonstrate low screening patterns for HA among patients with difficult-to-control hypertension. These patients by current definition have RH. Among patients screened for HA, there was a high positivity rate. Black race, low potassium, and high bicarbonate levels were associated with both screening and screen positivity. Diastolic blood pressure was associated with screening positivity but not systolic blood pressure. These findings suggest a higher prevalence of HA among the population with HTN than previously described and especially among those with difficult-to-control HTN. Proactive screening for HA among difficult-to-treat patients with HTN may be associated with more targeted management and subsequently improved outcomes.
Supplementary Material
tpp_23.096-suppl-01.pdf (131.1KB, pdf)
Acknowledgments
The authors would like to thank Rong Wei and Hui Zhou for their contribution in creating the Kaiser Permanente Southern California population with resistant hypertension. The authors would also like to acknowledge and thank the members of Kaiser Permanente Southern California who were the source of these findings and the foundation for why the authors perform research.
Footnotes
Author Contributions: John J Sim, MD, Jaejin An, PhD, and Victor Kim, MD: research idea and study design; John J Sim, MD, and Jiaxiao Shi, PhD: data acquisition; John J Sim, MD, Victor Kim, MD, Jiaxiao Shi, PhD, Simran Bhandari, MD, Jeffrey W Brettler, MD, and Michael H Kanter, MD: analysis or interpretation of data; John J Sim, MD: study supervision; Jiaxiao Shi, PhD: statistical analysis. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate.
Conflict of Interest: None declared
Funding: This study was supported by Kaiser Permanente Southern California internal research funds and the Kaiser Permanente Southern California Clinician Investigator Award (John J Sim). The funder/supporter had no role in study design, data collection, analysis, reporting, or the decision to submit for publication.
Data-Sharing Statement: Data are available upon request. Readers may contact the corresponding author to request underlying data.
References
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Supplementary Materials
tpp_23.096-suppl-01.pdf (131.1KB, pdf)