Literature DB >> 24870936

Racial differences in the prevalence of severe aortic stenosis.

Devin K Patel1, Kelly D Green2, Marat Fudim1, Frank E Harrell3, Thomas J Wang2, Mark A Robbins2.   

Abstract

BACKGROUND: In an era of expanded treatment options for severe aortic stenosis, it is important to understand risk factors for the condition. It has been suggested that severe aortic stenosis is less common in African Americans, but there are limited data from large studies. METHODS AND
RESULTS: The Synthetic Derivative at Vanderbilt University Medical Center, a database of over 2.1 million de-identified patient records, was used to identify individuals who had undergone echocardiography. The association of race with severe aortic stenosis was examined using multivariable logistic regression analyses adjusting for conventional risk factors. Of the 272 429 eligible patients (mean age 45 years, 44% male) with echocardiography, 14% were African American and 82% were Caucasian. Severe aortic stenosis was identified in 106 (0.29%) African-American patients and 2030 (0.91%) Caucasian patients (crude OR 0.32, 95% CI [0.26, 0.38]). This difference persisted in multivariable-adjusted analyses (OR 0.41 [0.33, 0.50], P<0.0001). African-American individuals were also less likely to have severe aortic stenosis due to degenerative calcific disease (adjusted OR 0.47 [0.36, 0.61]) or congenitally bicuspid valve (crude OR 0.13 [0.02, 0.80], adjusted OR dependent on age). Referral bias against those with severe valvular disease was assessed by comparing the prevalence of severe mitral regurgitation in Caucasians and African Americans and no difference was found.
CONCLUSIONS: These findings suggest that African Americans are at significantly lower risk of developing severe aortic stenosis than Caucasians.
© 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Entities:  

Keywords:  aortic valve stenosis; database; epidemiology; race and ethnicity; risk factor

Mesh:

Year:  2014        PMID: 24870936      PMCID: PMC4309086          DOI: 10.1161/JAHA.114.000879

Source DB:  PubMed          Journal:  J Am Heart Assoc        ISSN: 2047-9980            Impact factor:   5.501


Introduction

Valvular heart disease is responsible for more than 22 000 deaths each year in the United States.[1] In particular, aortic stenosis is a progressive condition known to carry a high short‐term mortality once patients become symptomatic. Risk factors have been described previously and include male sex, smoking, hypertension, elevated low‐density lipoprotein cholesterol (LDL), coronary atherosclerosis, congenital bicuspid valve, and advanced age.[2] Until recently, surgical replacement of the aortic valve was the only treatment proven to alter prognosis. This left a large number of elderly patients with multiple comorbidities without a viable treatment option due to excessive surgical risk; however, the recent introduction of transcatheter aortic valve replacement (TAVR) has offered an effective alternative for these patients.[3-4] As the population of treatment candidates expands, attention will be directed appropriately to assure equity in delivery of therapy. In several surgical series of aortic valve stenosis, it has been noted that African Americans were underrepresented.[5-7] It is unknown, however, whether this underrepresentation reflects racial differences in the prevalence of severe aortic valve stenosis by race or difference in intervention rate of those diagnosed with severe aortic valve stenosis. Therefore, we compared the prevalence of severe aortic stenosis in African Americans and Caucasians at a large, tertiary care medical center.

Methods

Study Population

The Synthetic Derivative is a database of 2.1 million de‐identified records from all patients in the electronic medical record at Vanderbilt University Medical Center, a large tertiary care center in Nashville, Tennessee. All documented patient encounters at the medical center are stripped of identifying information and uploaded via a one‐way hash to the Synthetic Derivative where they can be searched based on structured (laboratory values, ID9/CPT codes, demographics, etc.) and unstructured (text strings in narrative documents and reports) data.[8] As patient data does not contain any identifying information, use of Synthetic Derivative is classified as non‐human research by Vanderbilt University's Institutional Review Board and approval was given for this study. All records in the Synthetic Derivative were queried for documentation of an echocardiogram and race. Patients were then screened and individually reviewed for diagnosis of severe aortic stenosis, age at diagnosis, aortic valve area at diagnosis, and underlying etiology of disease. Criteria for documentation of an echocardiogram and confirmation of severe aortic stenosis are detailed in Table 1. Records that fulfilled search criteria for echocardiogram had a greater than 75% rate of having true documentation of an echocardiogram and approximately 90% of the 3500 patients with mention of severe aortic stenosis in their chart were identified using this search criteria. Race was classified using observer‐reported determination, which has been shown to have >92% concordance with self‐reported race and ancestry based on genetic biomarkers.[9-10]
Table 1.

Criteria for Inclusion in Study Populations

Study Population
EchocardiogramDefined from keyword search in all clinical documentsAll of the following criteria:● Presence of the string “echo” in any clinical document*● Race equals any of the following: African American, Caucasian
Severe aortic stenosisDefined from keyword search in all clinical documentsAll of the following criteria:● Presence of the string “echo” in any clinical document*● Presence of either of the following strings in any clinical document*: “severe aortic stenosis”, “critical aortic stenosis”● Race equals any of the following: African American, Caucasian● Single‐reviewer manual review for either of the following criteria: aortic valve area ≤1 cm2, operative intervention on aortic valve due to stenosis
Severe mitral regurgitationDefined from keyword search in all clinical documentsAll of the following criteria:● Presence of the string “echo” in any clinical document*● Presence of the string “severe mitral regurgitation”in any clinical document*● Race equals any of the following: African American, Caucasian

Search criteria for identification of patients in the study population is shown. A combination of keyword search, demographics qualifiers, and manual review was used.

Any clinical document includes clinical notes, procedure reports, radiology reports, problem lists, clinical communications, discharge summaries, patient letters, pathology reports or rehabilitation reports.

Criteria for Inclusion in Study Populations Search criteria for identification of patients in the study population is shown. A combination of keyword search, demographics qualifiers, and manual review was used. Any clinical document includes clinical notes, procedure reports, radiology reports, problem lists, clinical communications, discharge summaries, patient letters, pathology reports or rehabilitation reports. To address the possibility that Caucasians with severe valvular disease were more likely to be referred for echocardiogram compared with African Americans with severe valvular disease, another valvular condition, severe mitral regurgitation, was assessed. Criteria for diagnosis of severe mitral regurgitation are also shown in Table 1.

Assessment and Imputation of Traditional Risk Factors

Multiple traditional risk factors for aortic stenosis including sex, race, age, body mass index (BMI), LDL, creatinine, diabetes, hypertension, coronary artery disease, and statin use were assessed. Criteria for determination of each condition are shown in Table 2 with details of Synthetic Derivative search criteria for diabetes, hypertension, coronary artery disease, and statin use available in Table 3. Manual review of 500 patients with each definitive classification of a categorical variable was done (eg, 500 patients who were classified as having diabetes, 500 patients who were classified as not having diabetes, etc.) with accuracy ranging from 95% to 100% for each risk factor classification tested. Clinical judgment, shown in Table 4, was used to exclude incorrectly documented values. Each risk factor was then categorized based on percent of patients with definitive diagnosis or laboratory measure available, shown in Table 5.
Table 2.

Method for Determination of Each Demographic or Risk Factor

Method of DeterminationDemographic or Risk Factors
Direct exportGender
Race
Mean of all documented valuesBMI
LDL
Creatinine
Value at most recent clinic clinical encounter*Age
Synthetic Derivative searchCoronary artery disease
Hypertension
Diabetes
Statin use

The method of determining the demographic group or presence of a risk factor is shown. A combination of direct data export, export of laboratory or clinical measurement, and Synthetic Derivative queries was used in collecting risk factor data. Details of synthetic derivative queries for coronary artery disease, hypertension, and diabetes are shown in Table 3. BMI indicates body mass index; LDL, low‐density lipoprotein.

Defined as outpatient clinic visit, inpatient consultation, or documentation of vital signs.

Table 3.

Synthetic Derivative Search Criteria for Risk Factor Determination

Hypertension
YesDefined from combination of ICD9 coding and problem list search excluding common search confounders of the diseaseDocumentation of any of the following ICD9 codes: 401, 402, 403, 404, 405ORAll of the following criteria● Presence of any of the following strings in the problem list: “hypertension”, “HTN”● Presence of none of following strings in the problem list: “portal hypertension”, “pulmonary hypertension”, “RV hypertension”, “intracranial hypertension”, “pHTN”
NoDefined from lack of ICD9 code and lack of mention of disease in any clinical documentAll of the following criteria:● No documentation of ICD9 codes 401, 402, 403, 404, 405● No presence of any of the following strings in any clinical document: “Hypertension”, “HTN”
Unable to classifyDefined from lack of ICD9 coding but mention of disease in clinical document other than problem listAll of the following criteria:● No documentation of any of the following ICD9 codes: 401, 402, 403, 404, 405● No presence of any of the following strings in the problem list: “hypertension”, “HTN”● Presence of any of the following strings in any clinical document excluding problem list: “Hypertension”, “HTN”OR● All of the following criteria:No documentation of any of the following ICD9 codes: 401, 402, 403, 404, 405● Presence of any of the following strings in the problem list: “portal hypertension”, “pulmonary hypertension”, “RV hypertension”, “intracranial hypertension”, “pHTN”
Coronary artery disease requiring intervention
YesDefined from combination of CPT coding and problem list searchDocumentation of any of the following CPT codes: 33510, 33511, 33512, 33513, 33514, 33516, 33517, 33518, 33519, 33521, 33522, 33523, 33530, 33533, 33534, 33535, 33536, 92975, 92980, 92981, 92982, 92984, 92995, 92996ORPresence of the any of the following strings in the problem list: “Coronary Artery Bypass”, “CABG”, “percutaneous coronary intervention”, “PCI”
NoDefined from lack of ICD9 and mention of disease in any clinical documentAll of the following criteria:● No documentation of any of the following CPT codes: 33510, 33511, 33512, 33513, 33514, 33516, 33517, 33518, 33519, 33521, 33522, 33523, 33530, 33533, 33534, 33535, 33536, 92975, 92980, 92981, 92982, 92984, 92995, 92996● No presence of any of following strings in any clinical document: “Coronary Artery Bypass”, “CABG”, “Percutaneous Coronary Intervention”, “PCI”
Unable to classifyDefined from lack of ICD9 coding but mention of disease in clinical document other than problem listAll of the following criteria:● No documentation of any of the following CPT codes: 33510, 33511, 33512, 33513, 33514, 33516, 33517, 33518, 33519, 33521, 33522, 33523, 33530, 33533, 33534, 33535, 33536, 92975, 92980, 92981, 92982, 92984, 92995, 92996● No presence of the any of the following strings in the problem list: “coronary artery bypass”, “CABG”, “percutaneous coronary intervention”, “PCI”● Presence of any of following strings in any clinical document other than the problem list: “coronary artery bypass”, “CABG”, “percutaneous coronary intervention”, “PCI”
Diabetes mellitus
YesDefined from combination of ICD9 coding and problem list searchDocumentation of any ICD9 code that begins with 250ORPresence of any of the following strings in the problem list: “diabetes mel”, “IDDM”, “NIDDM”
NoDefined from lack of ICD9 and mention of disease in any clinical documentAll of the following criteria:● No documentation of any ICD9 code that begins with 250● No presence of any of the following strings in any clinical document: “diabetes”, “IDDM”, “NIDDM”
Unable to classifyDefined from lack of ICD9 coding but mention of disease in clinical document other than problem listAll of the following criteria:● No documentation of any ICD9 code that begins with 250● No presence of any of the following strings in the problem list: “diabetes mel”, “IDDM”, “NIDDM”● Presence of any of the following strings in any clinical document: “diabetes”, “IDDM”, “NIDDM”
Statin use
YesDefined from presence of medication in medication searchPresence of any of the following in medication search: “atorvastatin”, “cerivastatin”, “fluvastatin”, “lovastatin”, “pravastatin”, “rosuvastatin”, “simvastatin”, “Altocor”, “Altoprev”, “Baycol”, “Caduet”, “Canef”, “Crestor”, “Lescol”, “Lipex”, “Lipitor”, “Lipobay”, “Lipostat”, “Mevacor”, “Pravachol”, “Simcor”, “Sortis”, “Torvacard”, “Torvast”, “Totalip”, “Tulip”, “Vytorin”, “Zocor”
NoDefined from lack of presence of medication in medication searchNo presence of any of the following in medication search: “atorvastatin”, “cerivastatin”, “fluvastatin”, “lovastatin”, “pravastatin”, “rosuvastatin”, “simvastatin”, “Altocor”, “Altoprev”, “Baycol”, “Caduet”, “Canef”, “Crestor”, “Lescol”, “Lipex”, “Lipitor”, “Lipobay”, “Lipostat”, “Mevacor”, “Pravachol”, “Simcor”, “Sortis”, “Torvacard”, “Torvast”, “Totalip”, “Tulip”, “Vytorin”, “Zocor”

Full Synthetic Derivative search criteria for hypertension, diabetes coronary artery disease and statin use are shown below. A combination of keyword search within problem lists and clinical charts, ICD9 codes, CPT codes and medication searches was used for each risk factor. All searches are case insensitive. ICD9: ninth revision of International Classification of Diseases.

Clinical documents includes clinical notes, procedure reports, radiology reports, problem lists, clinical communications, discharge summaries, patient letters, pathology reports or rehabilitation reports.

Table 4.

Data Exclusion Criteria for Inaccurately Reported Data

VariableExcluded Values
Birth yearYear 1900*
BMIValues <14 and >70
LDLValues <1 and >1500
CreatinineValues <0 and >30

Clinical criteria shown here was used to exclude incorrectly documented measurements. Criteria were established based off clinical judgment and applied to all patients. BMI indicates body mass index; LDL, low density lipoprotein.

Exclude because patients whose birthdays are unknown are inaccurately classified as year 1900.

Table 5.

Classification of Comorbidities Based on Data Available

Risk FactorCriteriaVariables% Classified
CompleteData available for more than 99.9% of patientsPresence of aortic stenosis100%
Statin use100%
Race100%
Sex99.99%
Age at last follow‐up99.98%
Seldom‐missedData available for between 67% and 99.9% of patientsCoronary artery disease86.6%
Creatinine76.7%
Hypertension74.2%
Diabetes67.9%
IncompleteData available for less than 67% of patientsBMI62.2%
LDL31.9%

Comorbidities were classified based on percent of patients with data available for that comorbidity. Cutoff percentages for classification category were assigned based on statistical judgment. BMI indicates body mass index; LDL, low density lipoprotein.

Method for Determination of Each Demographic or Risk Factor The method of determining the demographic group or presence of a risk factor is shown. A combination of direct data export, export of laboratory or clinical measurement, and Synthetic Derivative queries was used in collecting risk factor data. Details of synthetic derivative queries for coronary artery disease, hypertension, and diabetes are shown in Table 3. BMI indicates body mass index; LDL, low‐density lipoprotein. Defined as outpatient clinic visit, inpatient consultation, or documentation of vital signs. Synthetic Derivative Search Criteria for Risk Factor Determination Full Synthetic Derivative search criteria for hypertension, diabetes coronary artery disease and statin use are shown below. A combination of keyword search within problem lists and clinical charts, ICD9 codes, CPT codes and medication searches was used for each risk factor. All searches are case insensitive. ICD9: ninth revision of International Classification of Diseases. Clinical documents includes clinical notes, procedure reports, radiology reports, problem lists, clinical communications, discharge summaries, patient letters, pathology reports or rehabilitation reports. Data Exclusion Criteria for Inaccurately Reported Data Clinical criteria shown here was used to exclude incorrectly documented measurements. Criteria were established based off clinical judgment and applied to all patients. BMI indicates body mass index; LDL, low density lipoprotein. Exclude because patients whose birthdays are unknown are inaccurately classified as year 1900. Classification of Comorbidities Based on Data Available Comorbidities were classified based on percent of patients with data available for that comorbidity. Cutoff percentages for classification category were assigned based on statistical judgment. BMI indicates body mass index; LDL, low density lipoprotein. Multiple imputation incorporating severe aortic stenosis and all complete risk factors was used to predict the seldom‐missed risk factors to allow for their inclusion in final analysis. Binary logistic regression was used to predict diabetes, hypertension, and coronary artery disease with random draws under fitted probability models used for imputation. The age effect was nonlinear in all models while creatinine was imputed using a linear model with random residuals added to mean predicted values. Multiple imputation was repeated 5 times to test whether results varied significantly between draws. The absolute number of complete cases was sizeable so the coefficients for imputed variables and race did not vary significantly across trials (Table 6). The final random draw was used for subsequent analysis.
Table 6.

Variation in Coefficients for Race and Imputed Risk Factor Between Different Draws of Imputation Model

DrawRaceDiabetesHypertensionCAD
10.8990.5660.2811.200
20.8980.5570.2791.195
30.9030.5580.2861.203
40.8950.5540.3011.193
50.8990.5640.2961.207

Coefficients for the association between race and imputed risk factors are shown below for 5 consecutive draws. The coefficients do not vary significantly between draws indicating variations due to random draw do not significantly affect the output of the model. CAD indicates coronary artery disease.

Variation in Coefficients for Race and Imputed Risk Factor Between Different Draws of Imputation Model Coefficients for the association between race and imputed risk factors are shown below for 5 consecutive draws. The coefficients do not vary significantly between draws indicating variations due to random draw do not significantly affect the output of the model. CAD indicates coronary artery disease.

Statistical Analysis

A binary logistic model based on the complete and seldom‐missed comorbidities was used to model the probability of severe aortic stenosis. Age and creatinine were modeled as continuous variables and fitted using a restricted cubic spline as to not assume linear relationships between them and severe aortic stenosis. All other variables were modeled as categorical variables. The interaction between race and all other comorbidities was assessed to determine if race significantly affected any other comorbidities' prediction of severe aortic stenosis and no significant interactions were found. This entire analysis was repeated for specific etiologies of severe aortic stenosiscalcific degeneration and bicuspid valve—to determine if the race relationship was present with common etiologies of severe aortic stenosis. All analyses were done using the statistical programming language R, version 3.0.1 and the R rms package.[11-12]

Results

347 126 of the 2 163 553 patients in the Synthetic Derivative had documentation of an echocardiogram, of which 272 525 patients including 222 976 Caucasians (81.8%) and 36 681 African Americans (13.5%) had race denoted. Of these patients, 2598 patients including 2030 (0.91%) Caucasians and 106 (0.29%) African Americans had severe aortic stenosis and 3614 including 3013 (1.35%) Caucasian and 532 (1.45%) African Americans had severe mitral regurgitation. Baseline characteristics of the echocardiogram population by race are shown in Figure 1 and Table 7. The mean age of African‐American patients was slightly lower than Caucasian and a larger proportion of African‐American patients were female. Significantly more African Americans had hypertension and diabetes while the mean creatinine and BMI were slightly higher for African‐American patients. African Americans and Caucasians had similar LDL cholesterol levels while Caucasians were taking statins and had coronary artery disease more often.
Figure 1.

Baseline characteristic of the echocardiogram population by race for continuous characteristics. Baseline characteristic for the echocardiogram population are shown, stratified by race, for continuous risk factors. African‐American subjects are younger and have higher BMI and creatinine than Caucasian subjects. BMI indicates body mass index.

Table 7.

Baseline Characteristic of Echocardiogram Population by Race for Categorical Characteristics

Risk FactorClassificationCaucasianAfrican American
Number%Number%
SexMale100 58645.114 74040.2
Female122 37754.921 93559.8
DMYes34 15715.3636017.3
No117 06252.518 64950.8
Imputed71 75732.211 67231.8
HTNYes94 09042.217 11046.6
No71 15631.910 26328.0
Imputed57 73025.9930825.4
CADYes17 7748.013623.7
No174 22878.131 46185.8
Imputed30 97413.9385810.5
Statin useYes62 71928.1787321.5
No160 25771.928 80878.5

Baseline characteristic for the echocardiogram population are shown, stratified by race, for categorical risk factors. Caucasian subjects were more likely to be male, have coronary artery disease and be prescribed statins while African Americans are more likely to have diabetes and hypertension. CAD indicates coronary artery disease; DM, diabetes mellitus; HTN, hypertension.

Baseline characteristic of the echocardiogram population by race for continuous characteristics. Baseline characteristic for the echocardiogram population are shown, stratified by race, for continuous risk factors. African‐American subjects are younger and have higher BMI and creatinine than Caucasian subjects. BMI indicates body mass index. Baseline Characteristic of Echocardiogram Population by Race for Categorical Characteristics Baseline characteristic for the echocardiogram population are shown, stratified by race, for categorical risk factors. Caucasian subjects were more likely to be male, have coronary artery disease and be prescribed statins while African Americans are more likely to have diabetes and hypertension. CAD indicates coronary artery disease; DM, diabetes mellitus; HTN, hypertension. Table 8 and Figure 2 show the likelihood of severe aortic stenosis for each demographic group and based on presence of each risk factor. Correlates of severe aortic stenosis included Caucasian race, male sex, statin use, increased creatinine, extremes in age, presence of diabetes, presence of hypertension, and presence of coronary artery disease. These relationships were also seen in etiology‐specific analysis looking specifically at severe calcific degenerative disease and bicuspid valve disease.
Table 8.

The Likelihood of Severe Aortic Stenosis, Severe Aortic Stenosis due to Calcific Degeneration of Tricuspid Aortic Valve, and Severe Aortic Stenosis due to Bicuspid Valve Disease Given Each Categorical Risk Factor

CharacteristicTotalSASCDTBVD
SexMale115 3261.00%0.55%0.31%
Female144 3120.68%0.44%0.14%
OR* for males1.47*1.25*2.22*
RaceAfrican American36 6810.29%0.18%0.04%
Caucasian222 9760.91%0.54%0.24%
OR* for African Americans0.32*0.33*0.17*
Diabetes mellitusYes40 5811.98%1.41%0.35%
No136 8120.97%0.51%0.30%
OR* ratio for diabetes2.06*2.79*1.17
HypertensionYes111 3541.60%1.04%0.36%
No81 8620.44%0.13%0.19%
OR* for hypertension3.68*8.07*1.90*
Coronary artery diseaseYes19 3924.99%3.32%0.98%
No206 3430.57%0.30%0.18%
OR* for coronary artery disease9.16*11.41*5.49*
Statin useYes70 5921.89%1.24%0.43%
No189 0650.42%0.21%0.13%
OR* for statin use4.57*5.97*3.32*
Overall percent0.82%0.49%0.21%

The overall and etiology‐specific probability of severe aortic stenosis based on presence of each risk factor or demographic is shown. Among patient with a given risk factor classification, the proportion of patients with severe aortic stenosis, severe aortic stenosis due to calcific degeneration of a native tricuspid valve, and severe aortic stenosis due to a bicuspid aortic valve are shown with the overall odds ratio for the difference. Group with higher probability of severe aortic stenosis included males, Caucasians, those with hypertension, diabetes mellitus, or coronary artery disease, and those prescribed statins. BVD indicates proportion with severe aortic stenosis due to bicuspid valve disease; CDT, proportion with severe aortic stenosis due to calcific degeneration of tricuspid aortic valve; SAS, proportion with severe aortic stenosis.

OR: crude odd ratio for developing severe aortic stenosis given a demographic or presence of a risk factor unadjusted for any other factors.

P<0.0001.

Figure 2.

The overall and etiology‐specific likelihood of severe aortic stenosis given each continuous risk factor. The overall and etiology‐specific probability of severe aortic stenosis as a function of age, creatinine, and BMI, unadjusted for other risk factors, is shown. Older patients and patients with higher creatinine had a higher probability of having severe aortic stenosis. BMI indicates body mass index.

The Likelihood of Severe Aortic Stenosis, Severe Aortic Stenosis due to Calcific Degeneration of Tricuspid Aortic Valve, and Severe Aortic Stenosis due to Bicuspid Valve Disease Given Each Categorical Risk Factor The overall and etiology‐specific probability of severe aortic stenosis based on presence of each risk factor or demographic is shown. Among patient with a given risk factor classification, the proportion of patients with severe aortic stenosis, severe aortic stenosis due to calcific degeneration of a native tricuspid valve, and severe aortic stenosis due to a bicuspid aortic valve are shown with the overall odds ratio for the difference. Group with higher probability of severe aortic stenosis included males, Caucasians, those with hypertension, diabetes mellitus, or coronary artery disease, and those prescribed statins. BVD indicates proportion with severe aortic stenosis due to bicuspid valve disease; CDT, proportion with severe aortic stenosis due to calcific degeneration of tricuspid aortic valve; SAS, proportion with severe aortic stenosis. OR: crude odd ratio for developing severe aortic stenosis given a demographic or presence of a risk factor unadjusted for any other factors. P<0.0001. The overall and etiology‐specific likelihood of severe aortic stenosis given each continuous risk factor. The overall and etiology‐specific probability of severe aortic stenosis as a function of age, creatinine, and BMI, unadjusted for other risk factors, is shown. Older patients and patients with higher creatinine had a higher probability of having severe aortic stenosis. BMI indicates body mass index. In multivariable‐adjusted analysis adjusted for sex, statin use, diabetes, hypertension, age, coronary artery disease, and creatinine, the association of African‐American race with lower risk for severe aortic stenosis remained significant (OR 0.41, 95% CI [0.33, 0.50]). The effect of race and age on prevalence of severe aortic stenosis is shown in Figure 3. The decreased prevalence of severe aortic stenosis in African Americans was present and consistent at all ages.
Figure 3.

Probability of severe aortic stenosis by age and race. The probability of severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians, adjusted for other risk factors, is shown here with distribution of patient ages noted at top. The difference in probability of severe aortic stenosis between the two races is present and significant at all ages.

Probability of severe aortic stenosis by age and race. The probability of severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians, adjusted for other risk factors, is shown here with distribution of patient ages noted at top. The difference in probability of severe aortic stenosis between the two races is present and significant at all ages. The partial effects of each risk factor and demographic group on the probability of severe aortic stenosis are shown in Figure 4. Odds ratios with confidence intervals for categorical risk factors and P values for continuous risk factors are shown. Age was the most significant risk factor while coronary artery disease, diabetes, race, and creatinine were also significant predictors. Sex, statin use, and hypertension had less effect on probability of disease.
Figure 4.

Partial effect of each predictor on model for severe aortic stenosis. The partial effect of each risk factor and demographic group on the probability of severe aortic stenosis is shown below. Odds ratios with 95% confidence intervals are shown for categorical values and P values are shown for continuous variable. Age had the greatest effect followed by coronary artery disease, diabetes, race, and creatinine. Sex, statin use, and hypertension had less predictive value. CAD indicates coronary artery disease; OR, odds ratio; HTN, hypertension.

Partial effect of each predictor on model for severe aortic stenosis. The partial effect of each risk factor and demographic group on the probability of severe aortic stenosis is shown below. Odds ratios with 95% confidence intervals are shown for categorical values and P values are shown for continuous variable. Age had the greatest effect followed by coronary artery disease, diabetes, race, and creatinine. Sex, statin use, and hypertension had less predictive value. CAD indicates coronary artery disease; OR, odds ratio; HTN, hypertension. Etiology‐specific analyses shown in Figures 5 and 6 revealed that the association of African‐American race with lower risk of severe aortic stenosis persisted for both bicuspid aortic valve (OR 0.13 [0.02, 0.80]) and calcific degeneration of native tricuspid aortic valve (OR 0.47 [0.36, 0.61]). The probability of bicuspid aortic valve appeared to have an age‐dependent relationship with African‐Americans children spared and the peak probability of severe aortic stenosis due to bicuspid valve occurring at a younger age in African Americans; however, only a small number of African Americans had bicuspid severe aortic stenosis and the age relationship in African Americans may be artifact. Still, African Americans who developed severe aortic stenosis were significantly less likely to have congenitally bicuspid valve as their underlying etiology (OR 0.49 [0.29, 0.85]).
Figure 5.

Probability of senile calcific severe aortic stenosis by race and age. The probability of senile calcific severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians is shown here with distribution of patient ages noted at top. The difference in probability of senile calcific severe aortic stenosis between the two races is present and significant at all ages.

Figure 6.

Probability of bicuspid severe aortic stenosis by race and age. The probability of bicuspid severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians is shown here with distribution of patient ages noted at top. The difference in probability of bicuspid severe aortic stenosis between the two races is present at all ages.

Probability of senile calcific severe aortic stenosis by race and age. The probability of senile calcific severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians is shown here with distribution of patient ages noted at top. The difference in probability of senile calcific severe aortic stenosis between the two races is present and significant at all ages. Probability of bicuspid severe aortic stenosis by race and age. The probability of bicuspid severe aortic stenosis with 95% confidence interval as a function of age for African Americans and Caucasians is shown here with distribution of patient ages noted at top. The difference in probability of bicuspid severe aortic stenosis between the two races is present at all ages.

Discussion

In summary, this study shows that African Americans are significantly less likely to have severe aortic stenosis than Caucasian individuals. The difference cannot be explained by traditional risk factors, age, or etiology of aortic valve disease. A few smaller studies have commented on the relationship between race and aortic valve disease. Novaro and colleagues found that, among individuals referred for cardiac surgery, African Americans had significantly less aortic stenosis, aortic valve calcification, degenerative aortic valve disease, and bicuspid aortic valves and more pathologically normal aortic valves than Caucasian individuals.[6] Likewise, Yeung and colleagues reported that 10% of patients evaluated for aortic valve replacement in their hospital were African American, despite the fact that 37% of individuals in their overall population were African American.[5] Unlike prior studies that have focused on surgical populations, this study assessed the overall and etiology‐specific prevalence of severe aortic stenosis in all patients undergoing echocardiography. Racial differences in comorbid medical conditions including traditional risk factors for aortic stenosis were extensively analyzed and controlled for using multivariable statistical analysis. Not surprisingly, many known risk factors (age, statin use, diabetes, hypertension, and coronary artery disease) were risk factors for severe aortic stenosis in this study sample[2]; however, none of these factors could explain the lower prevalence of severe aortic stenosis in African Americans, and Caucasian race remained an independent risk factor for severe aortic stenosis. Individual analysis of the two most common etiologies of severe aortic stenosis—degenerative calcification of a tricuspid aortic valve and stenosis of a congenitally bicuspid aortic valve—revealed that the decreased prevalence in African Americans applied to both etiologies. Moreover, African Americans with severe aortic stenosis were less likely to have bicuspid valve as their underlying etiology. This is consistent with the previously reported findings that African Americans have a drastically lower risk of bicuspid aortic valves.[13] Extensive efforts were made to confirm the validity of search methods. Criteria were set to ensure that the diagnosis of severe aortic stenosis had high specificity. Single‐reviewer manual chart review was used for each patient with severe aortic stenosis to ensure the diagnosis was made based on established guidelines on minimal valve area (<1 cm2) or that the stenosis was severe enough to necessitate valve replacement. Previously documented racial differences in prevalence of comorbid conditions were again identified in the overall echocardiogram population.[1,14-23] To further test for unforeseen bias against African Americans with severe valvular disease, the rate of another echocardiographically diagnosed pathology, severe mitral regurgitation, was assessed. Mitral valve disorders occur at equal rates in African Americans and Caucasian.[24-26] The rate of severe mitral regurgitation in the study population was similar (1.45% for African Americans, 1.35% for Caucasians) indicating that baseline valvular pathologies were diagnosed at equal rates. This study was not without limitations. The cohort included only patients with clinically indicated echocardiograms at a large academic referral center and may be biased toward patients with more severe disease. Any conclusion regarding the absolute prevalence of severe aortic stenosis is thus limited. Also, risk factors were classified through chart review and thus subject to provider‐by‐provider variations in definition and documentation of comorbid conditions, although there is no reason to believe one race would be affected more than another. The underlying etiology of race‐related differences in aortic stenosis remains to be determined. While numerous studies have identified single nucleotide polymorphisms associated with aortic valve calcification, no studies have noted racial differences in genetic risk factors for the disease.[27-28] Previous studies have assessed racial difference in aortic valve calcification and thickness but no differences between African Americans and Caucasians were identified.[29] Differences, however, have been noted in coronary artery calcification with African Americans significantly less likely to have calcification than whites, although this difference has not been associated with decreased risk of coronary heart disease.[30-34] Further understanding of the genetics and environmental factors underlying the racial difference in prevalence of severe aortic stenosis may result in novel preventative measures, early detection strategies, and therapeutic targets for the condition.

Conclusion

This study utilized a large research medical record to show that African Americans have a significantly lower prevalence of severe aortic stenosis than Caucasians. This difference cannot be explained by traditional risk factors and is present at all ages and for both common etiologies. Further genetic and laboratory investigation is warranted to determine the underlying mechanism for the lower prevalence.
  29 in total

1.  Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study.

Authors:  Ruben L J Osnabrugge; Darren Mylotte; Stuart J Head; Nicolas M Van Mieghem; Vuyisile T Nkomo; Corinne M LeReun; Ad J J C Bogers; Nicolo Piazza; A Pieter Kappetein
Journal:  J Am Coll Cardiol       Date:  2013-05-30       Impact factor: 24.094

2.  Clinical factors associated with calcific aortic valve disease. Cardiovascular Health Study.

Authors:  B F Stewart; D Siscovick; B K Lind; J M Gardin; J S Gottdiener; V E Smith; D W Kitzman; C M Otto
Journal:  J Am Coll Cardiol       Date:  1997-03-01       Impact factor: 24.094

3.  Bicuspid aortic valve: inter-racial difference in frequency and aortic dimensions.

Authors:  Sonal Chandra; Roberto M Lang; Jeremy Nicolarsen; Etienne Gayat; Kirk T Spencer; Victor Mor-Avi; Marion A Hofmann Bowman
Journal:  JACC Cardiovasc Imaging       Date:  2012-10

4.  Racial differences in coronary calcium prevalence among high-risk adults.

Authors:  W Tang; R C Detrano; O S Brezden; D Georgiou; W J French; N D Wong; T M Doherty; B H Brundage
Journal:  Am J Cardiol       Date:  1995-06-01       Impact factor: 2.778

5.  Prevalence of mitral valve prolapse and congenital bicuspid aortic valves in black and white patients undergoing cardiac valve operations.

Authors:  Gian M Novaro; Penny L Houghtaling; A Marc Gillinov; Eugene H Blackstone; Craig R Asher
Journal:  Am J Cardiol       Date:  2012-12-28       Impact factor: 2.778

6.  Prevalence and clinical determinants of mitral, tricuspid, and aortic regurgitation (the Framingham Heart Study)

Authors:  J P Singh; J C Evans; D Levy; M G Larson; L A Freed; D L Fuller; B Lehman; E J Benjamin
Journal:  Am J Cardiol       Date:  1999-03-15       Impact factor: 2.778

7.  Racial differences in rates of aortic valve replacement in patients with severe aortic stenosis.

Authors:  Michael Yeung; Jimmy Kerrigan; Sandeep Sodhi; Pei-Hsiu Huang; Eric Novak; Hersh Maniar; Alan Zajarias
Journal:  Am J Cardiol       Date:  2013-06-20       Impact factor: 2.778

8.  Effect of overweight and obesity on cardiovascular events in asymptomatic aortic stenosis: a SEAS substudy (Simvastatin Ezetimibe in Aortic Stenosis).

Authors:  Barbara P Rogge; Dana Cramariuc; Mai Tone Lønnebakken; Christa Gohlke-Bärwolf; John B Chambers; Kurt Boman; Eva Gerdts
Journal:  J Am Coll Cardiol       Date:  2013-06-13       Impact factor: 24.094

9.  Differences in life expectancy due to race and educational differences are widening, and many may not catch up.

Authors:  S Jay Olshansky; Toni Antonucci; Lisa Berkman; Robert H Binstock; Axel Boersch-Supan; John T Cacioppo; Bruce A Carnes; Laura L Carstensen; Linda P Fried; Dana P Goldman; James Jackson; Martin Kohli; John Rother; Yuhui Zheng; John Rowe
Journal:  Health Aff (Millwood)       Date:  2012-08       Impact factor: 6.301

10.  Genetic associations with valvular calcification and aortic stenosis.

Authors:  George Thanassoulis; Catherine Y Campbell; David S Owens; J Gustav Smith; Albert V Smith; Gina M Peloso; Kathleen F Kerr; Sonali Pechlivanis; Matthew J Budoff; Tamara B Harris; Rajeev Malhotra; Kevin D O'Brien; Pia R Kamstrup; Børge G Nordestgaard; Anne Tybjaerg-Hansen; Matthew A Allison; Thor Aspelund; Michael H Criqui; Susan R Heckbert; Shih-Jen Hwang; Yongmei Liu; Marketa Sjogren; Jesper van der Pals; Hagen Kälsch; Thomas W Mühleisen; Markus M Nöthen; L Adrienne Cupples; Muriel Caslake; Emanuele Di Angelantonio; John Danesh; Jerome I Rotter; Sigurdur Sigurdsson; Quenna Wong; Raimund Erbel; Sekar Kathiresan; Olle Melander; Vilmundur Gudnason; Christopher J O'Donnell; Wendy S Post
Journal:  N Engl J Med       Date:  2013-02-07       Impact factor: 91.245

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  29 in total

1.  Improvements in global longitudinal strain after transcatheter aortic valve replacement according to race.

Authors:  Aamir H Twing; Brody Slostad; Christina Anderson; Sreenivas Konda; Elliott M Groves; Mayank M Kansal
Journal:  Am J Cardiovasc Dis       Date:  2021-04-15

2.  Large-scale community echocardiographic screening reveals a major burden of undiagnosed valvular heart disease in older people: the OxVALVE Population Cohort Study.

Authors:  Joanna L d'Arcy; Sean Coffey; Margaret A Loudon; Andrew Kennedy; Jonathan Pearson-Stuttard; Jacqueline Birks; Eleni Frangou; Andrew J Farmer; David Mant; Jo Wilson; Saul G Myerson; Bernard D Prendergast
Journal:  Eur Heart J       Date:  2016-06-26       Impact factor: 29.983

3.  Mortality Due to Aortic Stenosis in the United States, 2008-2017.

Authors:  Graham H Bevan; David A Zidar; Richard A Josephson; Sadeer G Al-Kindi
Journal:  JAMA       Date:  2019-06-11       Impact factor: 56.272

4.  Sex, Race, and Socioeconomic Disparities in Patients With Aortic Stenosis (from a Nationwide Inpatient Sample).

Authors:  Hind A Beydoun; May A Beydoun; Hailun Liang; Greg A Dore; Danielle Shaked; Alan B Zonderman; Shaker M Eid
Journal:  Am J Cardiol       Date:  2016-06-27       Impact factor: 2.778

Review 5.  A review of racial disparities in transcatheter aortic valve replacement (TAVR): accessibility, referrals and implantation.

Authors:  Tamunoinemi Bob-Manuel; Arindam Sharma; Amit Nanda; Devarshi Ardeshna; William Paul Skelton; Rami N Khouzam
Journal:  Ann Transl Med       Date:  2018-01

Review 6.  Racial and ethnic disparities in outcomes after heart transplantation: A systematic review of contributing factors and future directions to close the outcomes gap.

Authors:  Alanna A Morris; Evan P Kransdorf; Bernice L Coleman; Monica Colvin
Journal:  J Heart Lung Transplant       Date:  2016-02-12       Impact factor: 10.247

7.  Relation of aortic valve calcium to chronic kidney disease (from the Chronic Renal Insufficiency Cohort Study).

Authors:  Marie A Guerraty; Boyang Chai; Jesse Y Hsu; Akinlolu O Ojo; Yanlin Gao; Wei Yang; Martin G Keane; Matthew J Budoff; Emile R Mohler
Journal:  Am J Cardiol       Date:  2015-02-12       Impact factor: 2.778

8.  Socioeconomic and Racial Disparities: a Case-Control Study of Patients Receiving Transcatheter Aortic Valve Replacement for Severe Aortic Stenosis.

Authors:  Anna Sleder; Shiloh Tackett; Matthew Cerasale; Chetan Mittal; Iyad Isseh; Ryhm Radjef; Andrew Taylor; Rashad Farha; Oleksandra Lupak; Dana Larkin; Lois Lamerato; George Divine; Kimberlydawn Wisdom; Kimberly Baker-Genaw; William O'Neill
Journal:  J Racial Ethn Health Disparities       Date:  2016-12-30

9.  Racial/Ethnic Disparities in Patients Undergoing Transcatheter Aortic Valve Replacement: Insights from the Healthcare Cost and Utilization Project's National Inpatient Sample.

Authors:  Dagmar F Hernandez-Suarez; Sagar Ranka; Pedro Villablanca; Nicole Yordan-Lopez; Lorena González-Sepúlveda; Jose Wiley; Cristina Sanina; Abiel Roche-Lima; Brenda G Nieves-Rodriguez; Stacey Thomas; Pedro Cox-Alomar; Angel Lopez-Candales; Harish Ramakrishna
Journal:  Cardiovasc Revasc Med       Date:  2019-04-09

10.  B-Type Natriuretic Peptide Levels and Mortality in Patients With and Without Heart Failure.

Authors:  Michelle K York; Deepak K Gupta; Cassandra F Reynolds; Eric Farber-Eger; Quinn S Wells; Katherine N Bachmann; Meng Xu; Frank E Harrell; Thomas J Wang
Journal:  J Am Coll Cardiol       Date:  2018-05-15       Impact factor: 24.094

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