Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism.

BACKGROUND. In patients with acute pulmonary embolism (PE), timely intervention (e.g., initiation of anticoagulation) is critical for optimizing clinical outcomes. OBJECTIVE. The purpose of this study was to evaluate the effect of artificial intelligence (AI)-based radiologist worklist reprioritization on report turnaround times for pulmonary CTA (CTPA) examinations positive for acute PE. METHODS. This retrospective single-center study included patients who underwent CTPA before (October 1, 2018-March 31, 2019 [pre-AI period]) and after (October 1, 2019-March 31, 2020 [post-AI period]) implementation of an AI tool that reprioritized CTPA examinations to the top of radiologists' reading worklists if acute PE was detected. EMR and dictation system timestamps were used to determine the wait time (time from examination completion to report initiation), read time (time from report initiation to report availability), and report turnaround time (sum of wait and read times) for the examinations. Times for reports positive for PE, with final radiology reports as reference, were compared between periods. RESULTS. The study included 2501 examinations of 2197 patients (1307 women, 890 men; mean age, 57.4 ± 17.0 [SD] years), including 1335 examinations from the pre-AI period and 1166 from the post-AI period. The frequency of acute PE, based on radiology reports, was 15.1% (201/1335) during the pre-AI period and 12.3% (144/1166) during the post-AI period. During the post-AI period, the AI tool reprioritized 12.7% (148/1166) of examinations. For PE-positive examinations, the post-AI period, compared with the pre-AI period, had significantly shorter mean report turnaround time (47.6 vs 59.9 minutes; mean difference, 12.3 minutes [95% CI, 0.6-26.0 minutes]) and mean wait time (21.4 vs 33.4 minutes; mean difference, 12.0 minutes [95% CI, 0.9-25.3 minutes]) but no significant difference in mean read time (26.3 vs 26.5 minutes; mean difference, 0.2 minutes [95% CI, -2.8 to 3.2 minutes]). During regular operational hours, wait time was significantly shorter in the post-AI than in the pre-AI period for routine-priority examinations (15.3 vs 43.7 minutes; mean difference, 28.4 minutes [95% CI, 2.2-64.7 minutes]) but not for stat- or urgent-priority examinations. CONCLUSION. AI-driven worklist reprioritization yielded reductions in report turnaround time and wait time for PE-positive CTPA examinations. CLINICAL IMPACT. By assisting radiologists in providing rapid diagnoses, the AI tool has potential for enabling earlier interventions for acute PE.

Detection of Incidental Pulmonary Embolism on Conventional Contrast-Enhanced Chest CT: Comparison of an Artificial Intelligence Algorithm and Clinical Reports.

BACKGROUND. Artificial intelligence (AI) algorithms have shown strong performance for detection of pulmonary embolism (PE) on CT examinations performed using a dedicated protocol for PE detection. AI performance is less well studied for detecting PE on examinations ordered for reasons other than suspected PE (i.e., incidental PE [iPE]). OBJECTIVE. The purpose of this study was to assess the diagnostic performance of an AI algorithm for detection of iPE on conventional contrast-enhanced chest CT examinations. METHODS. This retrospective study included 2555 patients (mean age, 53.2 ± 14.5 [SD] years; 1340 women, 1215 men) who underwent 3003 conventional contrast-enhanced chest CT examinations (i.e., not using pulmonary CTA protocols) between September 2019 and February 2020. A commercial AI algorithm was applied to the images to detect acute iPE. A vendor-supplied natural language processing (NLP) algorithm was applied to the clinical reports to identify examinations interpreted as positive for iPE. For all examinations that were positive by the AI-based image review or by NLP-based report review, a multireader adjudication process was implemented to establish a reference standard for iPE. Images were also reviewed to identify explanations of AI misclassifications. RESULTS. On the basis of the adjudication process, the frequency of iPE was 1.3% (40/3003). AI detected four iPEs missed by clinical reports, and clinical reports detected seven iPEs missed by AI. AI, compared with clinical reports, exhibited significantly lower PPV (86.8% vs 97.3%, p = .03) and specificity (99.8% vs 100.0%, p = .045). Differences in sensitivity (82.5% vs 90.0%, p = .37) and NPV (99.8% vs 99.9%, p = .36) were not significant. For AI, neither sensitivity nor specificity varied significantly in association with age, sex, patient status, or cancer-related clinical scenario (all p > .05). Explanations of false-positives by AI included metastatic lymph nodes and pulmonary venous filling defect, and explanations of false-negatives by AI included surgically altered anatomy and small-caliber subsegmental vessels. CONCLUSION. AI had high NPV and moderate PPV for iPE detection, detecting some iPEs missed by radiologists. CLINICAL IMPACT. Potential applications of the AI tool include serving as a second reader to help detect additional iPEs or as a worklist triage tool to allow earlier iPE detection and intervention. Various explanations of AI misclassifications may provide targets for model improvement.

Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System.

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation. The descriptions of our solution and the release of the open-sourced codes may be helpful in future implementation of similar web-based calculators.

Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage.

PURPOSE: To determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on non-contrast-enhanced CT images to radiologists to improve workflow. MATERIALS AND METHODS: In this study, a commercially available machine learning algorithm that flags abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 and March 2019. The algorithm was introduced in three phases: (a) as a "pop-up" widget on ancillary monitors, (b) as a marked examination in reading worklists, and (c) as a marked examination for reprioritization based on the presence of the flag. A statistical approach, which was based on a queuing theory, was implemented to assess the impact of each intervention on queue-adjusted wait and turnaround time compared with historical controls. RESULTS: Notification with a widget or flagging the examination had no effect on queue-adjusted image wait (P > .99) or turnaround time (P = .6). However, a reduction in queue-adjusted wait time was observed between negative (15.45 minutes; 95% CI: 15.07, 15.38) and positive (12.02 minutes; 95% CI: 11.06, 12.97; P < .0001) artificial intelligence-detected ICH examinations with reprioritization. Reduced wait time was present for all order classes but was greatest for examinations ordered as routine for both inpatients and outpatients because of their low priority. CONCLUSION: The approach used to present flags from artificial intelligence and machine learning algorithms to the radiologist can reduce image wait time and turnaround times.© RSNA, 2021See also the commentary by O'Connor and Bhalla in this issue.

Prevalence of pectus excavatum in an adult population-based cohort estimated from radiographic indices of chest wall shape.

BACKGROUND: Pectus excavatum is the most common chest wall skeletal deformity. Although commonly evaluated in adolescence, its prevalence in adults is unknown. METHODS AND FINDINGS: Radiographic indices of chest wall shape were analyzed for participants of the first (n = 2687) and second (n = 1780) phases of the population-based Dallas Heart Study and compared to clinical cases of pectus (n = 297). Thoracic computed tomography imaging studies were examined to calculate the Haller index, a measure of thoracic axial shape, and the Correction index, which quantitates the posterior displacement of the sternum relative to the ribs. At the level of the superior xiphoid, 0.5%, 5% and 0.4% of adult Dallas Heart Study subjects have evidence of pectus excavatum using thresholds of Haller index >3.25, Correction index >10%, or both, respectively. Radiographic measures of pectus are more common in females than males and there is a greater prevalence of pectus in women than men. In the general population, the Haller and Correction indices are associated with height and weight, independent of age, gender, and ethnicity. Repeat imaging of a subset of subjects (n = 992) demonstrated decreases in the mean Haller and Correction indices over seven years, suggesting change to a more circular axial thorax, with less sternal depression, over time. CONCLUSIONS: To our knowledge, this is the first study estimating the prevalence of pectus in an unselected adult population. Despite the higher reported prevalence of pectus cases in adolescent boys, this study demonstrates a higher prevalence of radiographic indices of pectus in adult females.

Identification of High-Risk Left Ventricular Hypertrophy on Calcium Scoring Cardiac Computed Tomography Scans: Validation in the DHS.

BACKGROUND: Coronary artery calcium scoring only represents a small fraction of all information available in noncontrast cardiac computed tomography (CAC-CT). We hypothesized that an automated pipeline using radiomics and machine learning could identify phenotypic information about high-risk left ventricular hypertrophy (LVH) embedded in CAC-CT. METHODS: This was a retrospective analysis of 1982 participants from the DHS (Dallas Heart Study) who underwent CAC-CT and cardiac magnetic resonance. Two hundred twenty-four participants with high-risk LVH were identified by cardiac magnetic resonance. We developed an automated adaptive atlas algorithm to segment the left ventricle on CAC-CT, extracting 107 radiomics features from the volume of interest. Four logistic regression models using different feature selection methods were built to predict high-risk LVH based on CAC-CT radiomics, sex, height, and body surface area in a random training subset of 1587 participants. RESULTS: The respective areas under the receiver operating characteristics curves for the cluster-based model, the logistic regression model after exclusion of highly correlated features, and the penalized logistic regression models using least absolute shrinkage and selection operators with minimum or one SE λ values were 0.74 (95% CI, 0.67-0.82), 0.74 (95% CI, 0.67-0.81), 0.76 (95% CI, 0.69-0.83), and 0.73 (95% CI, 0.66-0.80) for detecting high-risk LVH in a distinct validation subset of 395 participants. CONCLUSIONS: Ventricular segmentation, radiomics features extraction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of LVH in participants undergoing CAC-CT, without the need for additional imaging or radiation exposure. Registration: URL http://www.clinicaltrials.gov. Unique identifier: NCT00344903.

Implementing Shared, Standardized Imaging Protocols to Improve Cross-Enterprise Workflow and Quality.

Value-based imaging requires appropriate utilization and the delivery of consistently high-quality imaging at an acceptable cost. Challenges include developing standardized imaging protocols, ensuring consistent application by technologists, and monitoring quality. These challenges increase as enterprises grow in geographical extent and complexity through mergers or partnerships. Our imaging enterprise includes a university hospital and clinic system, a large county hospital and healthcare system, and a pediatric hospital and health system. Studies across the three systems are interpreted by one large academic radiology group with expertise in various subspecialties. Our goals were as follows: (1) Standardize imaging protocols; (2) adapt the imaging protocols to specific modalities and available equipment; and (3) disseminate this knowledge across all of the sites of care. Our approach involved three components: (1) facilitation of imaging protocol definition across subspecialty radiologist teams; (2) creation of a database which links the clinical imaging protocols to the scanner/machine specific acquisition protocols; and (3) delivery of a protocol library and updates to all users regardless of location. We successfully instituted a process for the development, implementation, and delivery of standardized imaging protocols in a complex, multi-institutional healthcare system. Key elements for success include (1) a Project Champion who is able to articulate the importance of protocol standardization in improving the quality of patient care, (2) strong, effective modality-specific operational committees, (3) a Project Lead to manage the process efficiently, and (4) an electronic publishing of the protocol database to facilitate ease of access and use.

Association of African Ancestry With Electrocardiographic Voltage and Concentric Left Ventricular Hypertrophy: The Dallas Heart Study.

Importance: Compared with white individuals, black individuals have increased electrocardiographic voltage and an increased prevalence of concentric left ventricular (LV) hypertrophy. Whether environmental or genetic factors lead to these racial differences is unknown. Objective: To determine whether proportion of genetically determined African ancestry among self-reported black individuals is associated with increased electrocardiographic voltage and concentric LV hypertrophy (LVH). Design, Setting, and Participants: The Dallas Heart Study is a probability-based cohort study of English- or Spanish-speaking Dallas County, Texas, residents, with deliberate oversampling of black individuals. Participants underwent extensive phenotyping, which included electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), and dual-energy radiography absorptiometry (DEXA) at a single center. Participants aged 18 to 65 years who enrolled in the Dallas Heart Study between July 2000 and December 2002, self-identified as black (n = 1251) or white (n = 826), and had ECG, CMR, and DEXA data were included in this analysis. Data were analyzed from June 2017 to September 2018. Exposures: Proportion of African ancestry. Main Outcomes and Measures: Electrocardiographic voltage (12-lead and 9-lead) and markers of concentric LVH as assessed by CMR (LV concentricity0.67 [LV mass/end-diastolic volume0.67], LV wall thickness [LVWT], and prevalent LVH [defined by LV mass/height2.7]). Results: Of the 2077 participants included in the study, 1138 (54.8%) were women, and the mean (SD) age was 45.2 (9.9) years. Black race and African ancestry were individually associated with increased ECG voltage, LV concentricity0.67, LVWT, and prevalent LVH in multivariable analyses adjusting for age, sex, systolic blood pressure, antihypertensive medication use, and body composition. When African ancestry and black race were entered together into multivariable models, African ancestry but not black race remained associated with ECG voltage, LVWT, LV concentricity0.67, and prevalent LVH. Among black participants, African ancestry remained associated with these 4 phenotypes (12-lead voltage: ß, 0.05; P = .04; LVWT: ß, 0.05; P = .02; LV concentricty0.67: ß, 0.05; P = .045; prevalent LVH: odds ratio, 1.2; 95% CI, 1.03-1.4; P = .02). Conclusions and Relevance: Genetically determined African ancestry was associated with electrocardiographic voltage, measures of concentric LV remodeling, and prevalent LVH. These data support a genetic basis related to African ancestry for the increased prevalence of these cardiovascular traits in black individuals.

Non-contrast quantitative pulmonary perfusion using flow alternating inversion recovery at 3T: A preliminary study.

PURPOSE: To demonstrate the initial feasibility of non-contrast quantitative pulmonary perfusion imaging at 3T using flow alternating inversion recovery (FAIR), and to evaluate the intra-session and inter-session reliability of FAIR measurements at 3T. MATERIALS AND METHODS: Nine healthy volunteers were imaged using our own implementation of FAIR pulse sequence at 3T. Quantitative FAIR perfusion, both with and without larger pulmonary vessels, was correlated with global phase contrast (PC) measured blood flow in the right pulmonary artery (RPA). The same volunteers were also imaged with SPECT perfusion using technetium-99m-macroaggregated albumin and relative dispersion (RD) was assessed between FAIR and SPECT perfusion. Four additional healthy volunteers were evaluated for FAIR repeatability, using intra-class correlation coefficient (ICC) and Bland-Altman analysis. p<0.05 was considered statistically significant. RESULTS: FAIR perfusion across all subjects was 858±605mL/100g/min (with vessels) and 629±294mL/100g/min (without vessels) and correlated significantly with the PC measured blood flow in the RPA (r=0.62, p<0.01 with vessels; r=0.73, p<0.001 without vessels). The median RD of FAIR perfusion across all subjects was 0.73 (with vessels) and 0.49 (without vessels), compared against 0.23 with SPECT perfusion. The intra/inter-session ICC of FAIR perfusion with vessels was 0.95/0.59 and improved to 0.96/0.72, when vessels were removed. CONCLUSIONS: Non-contrast quantitative pulmonary perfusion imaging using FAIR is feasible at 3T. This may serve as a reliable method to assess regional lung perfusion at 3T to characterize and monitor treatment response in chronic lung disease without the concerns of repeated exposure to ionizing radiation or the accumulation of exogenous contrast agent.

Association of Concentric Left Ventricular Hypertrophy With Subsequent Change in Left Ventricular End-Diastolic Volume: The Dallas Heart Study.

BACKGROUND: In the conventional paradigm of the progression of left ventricular hypertrophy, a thick-walled left ventricle (LV) ultimately transitions to a dilated cardiomyopathy. There are scant data in humans demonstrating whether this transition occurs commonly without an interval myocardial infarction. METHODS AND RESULTS: Participants (n=1282) from the Dallas Heart Study underwent serial cardiac magnetic resonance ≈7 years apart. Those with interval cardiovascular events and a dilated LV (increased LV end-diastolic volume [EDV] indexed to body surface area) at baseline were excluded. Multivariable linear regression models tested the association of concentric hypertrophy (increased LV mass and LV mass/volume0.67) with change in LVEDV. The study cohort had a median age of 44 years, 57% women, 43% black, and 11% (n=142) baseline concentric hypertrophy. The change in LVEDV in those with versus without concentric hypertrophy was 1 mL (-9 to 12) versus -2 mL (-11 to 7), respectively, P<0.01. In multivariable linear regression models, concentric hypertrophy was associated with larger follow-up LVEDV (P≤0.01). The progression to a dilated LV was uncommon (2%, n=25). CONCLUSIONS: In the absence of interval myocardial infarction, concentric hypertrophy was associated with a small, but significantly greater, increase in LVEDV after 7-year follow-up. However, the degree of LV enlargement was minimal, and few participants developed a dilated LV. These data suggest that if concentric hypertrophy does progress to a dilated cardiomyopathy, such a transition would occur over a much longer timeframe (eg, decades) and perhaps less common than previously thought. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00344903.

Defining coronary artery calcium concordance and repeatability - Implications for development and change: The Dallas Heart Study.

BACKGROUND: Development and change of coronary artery calcium (CAC) are associated with coronary heart disease. Interpretation of serial CAC measurements will require better understanding of changes in CAC beyond the variability in the test itself. METHODS: Dallas Heart Study participants (2888) with duplicate CAC scans obtained minutes apart were analyzed to determine interscan concordance and 95% confidence bounds (ie: repeatability limits) for each discrete CAC value. These data derived cutoffs were then used to define change above measurement variation and determine the frequency of CAC development and change among 1779 subjects with follow up CAC scans performed 6.9 years later. RESULTS: Binary concordance (0 vs. >0) was 91%. The value of CAC denoting true development of CAC by exceeding the 95% confidence bounds for a single score of 0 was 2.7 Agatston units (AU). Among those with scores >0, the 95% confidence bounds for CAC change were determined by the following formulas: for CAC≤100AU: 5.6√CAC + 0.3*CAC - 3.1; for CAC>100AU: 12.4√CAC - 67.7. Using these parameters, CAC development occurred in 15.0% and CAC change occurred in 48.9%. Although 225 individuals (24.9%) had a decrease in CAC over follow up, only 1 (0.1%) crossed the lower confidence bound. Compared with prior reported definition of CAC development (ie: >0), the novel threshold of 2.7AU resulted in better measures of model performance. In contrast, for CAC change, no consistent differences in performance metrics were observed compared with previously reported definitions. CONCLUSION: There is significant interscan variability in CAC measurement, including around scores of 0. Incorporating repeatability estimates may help discern true differences from those due to measurement variability, an approach that may enhance determination of CAC development and change.

Dynamic Relation of Changes in Weight and Indices of Fat Distribution With Cardiac Structure and Function: The Dallas Heart Study.

BACKGROUND: Obesity may increase heart failure risk through cardiac remodeling. Cross-sectional associations between adiposity and cardiac structure and function have been elucidated, but the impact of longitudinal changes in adiposity on cardiac remodeling is less well understood. METHODS AND RESULTS: Participants in the Dallas Heart Study without cardiovascular disease or left ventricular dysfunction underwent assessment of body weight, anthropometrics, and cardiac magnetic resonance imaging at baseline and 7 years later. Associations between changes in indices of generalized and central adiposity with changes in left ventricular mass, volume, mass/volume ratio (concentricity), wall thickness, and ejection fraction were assessed using multivariable linear regression. The study cohort (n=1262) mean age was 44 years with 57% women, 44% black, and 36% obese participants. At follow-up, 41% had ≥5% weight gain, and 15% had ≥5% weight loss. Greater weight gain was associated with younger age, lower risk factor burden, and lower body mass index at baseline. In multivariable models adjusting for age, sex, race, comorbid conditions at baseline and follow-up, baseline adiposity, and cardiac measurement, increasing weight was associated with increases in left ventricular mass (ß=0.10, P<0.0001), wall thickness (ß=0.10, P<0.0001), and concentricity (ß=0.06, P=0.002), with modest effects on end-diastolic volume (ß=0.04, P=0.044) and ejection fraction (ß=0.05, P=0.046). Similar results were seen with other adiposity indices. CONCLUSIONS: Concentric left ventricular remodeling is the predominant phenotype linked to increasing adiposity in middle age. Our findings support the importance of weight management to prevent secular changes in adiposity, concentric remodeling, and eventual heart failure over time.

Hemodynamic and Mechanical Properties of the Proximal Aorta in Young and Middle-Aged Adults With Isolated Systolic Hypertension: The Dallas Heart Study.

The aim of this study was to assess characteristic impedance (Zc) of the proximal aorta in young and middle-aged individuals with isolated systolic hypertension (ISH). Zc is an index of aortic stiffness relative to aortic size. In the Dallas Heart Study, 2001 untreated participants 18 to 64 years of age (mean age: 42.3 years; 44% black race) were divided into the following groups based on office blood pressure (BP) measurements: (1) optimal BP (systolic BP [SBP] <120 mm Hg and diastolic BP [DBP] <80 mm Hg; n=837); (2) prehypertension (SBP 120-139 mm Hg and DBP 80-89 mm Hg; n=821); (3) ISH (SBP ≥140 mm Hg and DBP <90 mm Hg; n=121); (4) isolated diastolic hypertension (SBP <140 mm Hg and DBP ≥90 mm Hg; n=44); and (5) systolic-diastolic hypertension (SBP ≥140 mm Hg and DBP ≥90 mm Hg; n=178). Zc, aortic arch pulse wave velocity, and minimum ascending aortic size were quantified using cardiovascular magnetic resonance. In multivariable-adjusted linear models, Zc was highest in the ISH group compared with the optimal BP, isolated diastolic hypertension, or systolic-diastolic hypertension groups (103.2±4.0 versus 68.3±2.1, 75.4±6.0, and 88.9±4.8 dyne*seconds/cm5, respectively; all P<0.05). The Zc-ISH association did not differ by race. Aortic pulse wave velocity was highest in the ISH group compared with the optimal BP, isolated diastolic hypertension, or systolic-diastolic hypertension groups (6.3±0.3 versus 4.3±0.1, 4.4±0.4 and 5.5±0.3 m/s, respectively; all P<0.05), whereas aortic size was similar across groups (all P>0.2). Results were similar in a subgroup of 1551 participants 18 to 49 years of age. In a multiracial population-based sample, we found evidence of a mismatch between proximal aortic stiffness and diameter in young and middle-aged adults with ISH.

Hippocampal volume in patients with asthma: Results from the Dallas Heart Study.

INTRODUCTION: Asthma is associated with an increased risk of mild cognitive impairment and dementia. Depression and oral corticosteroid use are associated with atrophy of the hippocampus and are common in asthma. However, minimal neuroimaging data are available in asthma patients. METHODS: We conducted a retrospective analysis of 1,287 adult participants from the Dallas Heart Study, an epidemiological sample of Dallas County residents. Study outcome variables were hippocampal volumes measured by FreeSurfer. ANOVA was used to examine a gender difference in hippocampal volumes. General Linear Models (GLM) were conducted to examine asthma diagnosis association with hippocampal volumes. RESULTS: The prevalence rate of asthma among our study sample was 10.8% with 9.6% in males and 11.7% in females. After controlling for demographic characteristics, participants with asthma had significantly smaller total, right, and left hippocampal volumes than those without asthma. The association of asthma with smaller hippocampal volume was significant among males but not among females. CONCLUSION: Hippocampal volume in a large and diverse sample of adults was significantly smaller in people with asthma as compared to those without asthma. These findings suggest that asthma may be associated with structural brain differences. Thus, medical illnesses without obvious direct neurodegenerative or even vascular involvement can be associated with brain changes. Because the hippocampus is a brain region involved in memory formation, these findings may have implications for treatment adherence that could have important implications for asthma treatment. Study limitations are the reliance on a self-reported asthma diagnosis and lack of additional asthma clinical information.

Ethnic Difference in Proximal Aortic Stiffness: An Observation From the Dallas Heart Study.

OBJECTIVES: This study aims to compare ethnic difference in proximal aortic pulse wave velocity (PWV) and characteristic impedance (Zc). BACKGROUND: Increased aortic stiffness is an independent predictor of target organ damage, incident hypertension, and all-cause mortality. However, previous studies have not directly assessed proximal aortic function in Blacks, the ethnic population with disproportionately high risk for incident hypertension and target organ complications. METHODS: We evaluated the multiethnic, population-based DHS (Dallas Heart Study) participants (N = 2,544, 54.2% women, 49.7% Black) who underwent cardiac magnetic resonance at 1.5-T. Aortic stiffness and Zc were determined from aortic arch PWV and lumen area measurements. Linear regression was used to evaluate ethnic differences in proximal aortic wall stiffness using aortic arch PWV and Zc as dependent variables with and without adjustment for traditional cardiovascular risk factors. Because cardiac output was significantly higher in Blacks compared to Whites and Hispanics, additional comparisons of PWV and Zc were performed after adjustment for cardiac output and peripheral vascular resistance. RESULTS: Compared with Whites, both Blacks and Hispanics had higher levels of aortic arch PWV (4.25, 95% confidence interval [CI]: 4.15 to 4.35 m/s, vs. 4.72, 95% CI: 4.64 to 4.81 m/s, vs. 4.48, 95% CI: 4.33 to 4.63 m/s, respectively, both p < 0.05 vs. White), and Zc (64.9, 95% CI: 63.3 to 66.6 dyne·s/cm5, vs. 75.6, 95% CI: 74.0 to 77.2 dyne·s/cm5, vs. 70.1, 95% CI: 67.6 to 72.8 dyne·s/cm5, respectively, both p < 0.01 vs. White) after adjustment for age, age squared, sex, body mass index, height, mean arterial blood pressure, antihypertensive treatment, heart rate, total cholesterol, diabetes mellitus, and smoking. Compared with Hispanics, Blacks also had higher level of both PWV and Zc (both p < 0.01). Ethnic differences in PWV and Zc persisted after adjustment for cardiac output and peripheral vascular resistance. CONCLUSIONS: In a multiethnic population-based-sample, Blacks and Hispanics had higher proximal aortic stiffness compared with Whites independent of blood pressure and relevant risk factors.

Cardiovascular Risk Factors Associated with Smaller Brain Volumes in Regions Identified as Early Predictors of Cognitive Decline.

PURPOSE: To determine in a large multiethnic cohort the cardiovascular and genetic risk factors associated with smaller volume in the hippocampus, precuneus, and posterior cingulate, and their association with preclinical deficits in cognitive performance in patients younger and older than 50 years. MATERIALS AND METHODS: The institutional review board approved the study and all participants provided written informed consent. Eligible for this study were 1629 participants (700 men and 929 women; mean age, 50.0 years ± 10.2 [standard deviation]) drawn from the population-based Dallas Heart Study who underwent laboratory and clinical analysis in an initial baseline visit and approximately 7 years later underwent brain magnetic resonance imaging with automated volumetry and cognitive assessment with the Montreal Cognitive Assessment (MoCA). Regression analysis showed associations between risk factors and segmental volumes, and associations between these volumes with cognitive performance in participants younger and older than 50 years. RESULTS: Lower hippocampal volume was associated with previous alcohol consumption (standardized estimate, -0.04; P = .039) and smoking (standardized estimate, -0.04; P = .048). Several risk factors correlated with lower total brain, posterior cingulate, and precuneus volumes. Higher total (standardized estimate, 0.06; P = .050), high-density lipoprotein (standardized estimate, 0.07; P = .003), and low-density lipoprotein (standardized estimate, 0.04; P = .037) cholesterol levels were associated with larger posterior cingulate volume, and higher triglyceride levels (standardized estimate, 0.06; P = .004) were associated with larger precuneus volume. Total MoCA score was associated with posterior cingulate volume (standardized estimate, 0.13; P = .001) in younger individuals and with hippocampal (standardized estimate, 0.06; P < .05) and precuneus (standardized estimate, 0.08; P < .023) volumes in older adults. CONCLUSION: Smaller volumes in specific brain regions considered to be early markers of dementia risk were associated with specific cardiovascular disease risk factors and cognitive deficits in a predominantly midlife multiethnic population-based sample. Additionally, the risk factors most associated with these brain volumes differed in participants younger and older than 50 years, as did the association between brain volume and MoCA score.