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.

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.

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.

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.

Cardiac remodeling in response to 1 year of intensive endurance training.

BACKGROUND: It is unclear whether, and to what extent, the striking cardiac morphological manifestations of endurance athletes are a result of exercise training or a genetically determined characteristic of talented athletes. We hypothesized that prolonged and intensive endurance training in previously sedentary healthy young individuals could induce cardiac remodeling similar to that observed cross-sectionally in elite endurance athletes. METHODS AND RESULTS: Twelve previously sedentary subjects (aged 29±6 years; 7 men and 5 women) trained progressively and intensively for 12 months such that they could compete in a marathon. Magnetic resonance images for assessment of right and left ventricular mass and volumes were obtained at baseline and after 3, 6, 9, and 12 months of training. Maximum oxygen uptake ( max) and cardiac output at rest and during exercise (C2H2 rebreathing) were measured at the same time periods. Pulmonary artery catheterization was performed before and after 1 year of training, and pressure-volume and Starling curves were constructed during decreases (lower body negative pressure) and increases (saline infusion) in cardiac volume. Mean max rose from 40.3±1.6 to 48.7±2.5 mL/kg per minute after 1 year (P<0.00001), associated with an increase in both maximal cardiac output and stroke volume. Left and right ventricular mass increased progressively with training duration and intensity and reached levels similar to those observed in elite endurance athletes. In contrast, left ventricular volume did not change significantly until 6 months of training, although right ventricular volume increased progressively from the outset; Starling and pressure-volume curves approached but did not match those of elite athletes. CONCLUSIONS: One year of prolonged and intensive endurance training leads to cardiac morphological adaptations in previously sedentary young subjects similar to those observed in elite endurance athletes; however, it is not sufficient to achieve similar levels of cardiac compliance and performance. Contrary to conventional thinking, the left ventricle responds to exercise with initial concentric but not eccentric remodeling during the first 6 to 9 months after commencement of endurance training depending on the duration and intensity of exercise. Thereafter, the left ventricle dilates and restores the baseline mass-to-volume ratio. In contrast, the right ventricle responds to endurance training with eccentric remodeling at all levels of training.

Effect of normal aging versus hypertension, abnormal body mass index, and diabetes mellitus on white matter hyperintensity volume.

BACKGROUND AND PURPOSE: The natural history of white matter hyperintensity (WMH) progression resulting from normal aging versus comorbid vascular insults remains unclear. Therefore we investigated age-related differences in WMH volumes among a group with comorbid hypertension, abnormal body mass index, and diabetes mellitus to a normal aging group drawn from the same population lacking any of these comorbidities. METHODS: WMH volumes were acquired using 3T MRI for 2011 Dallas Heart Study participants. The slope of the WMH versus age regression was compared between normal and comorbidity groups<50 and ≥50 years of age where a change in slope was demonstrated. RESULTS: Aging was linearly associated with greater log WMH volume for both normal (P=0.02) and comorbidity (P<0.0001) groups. Beyond 50 years of age, more rapid increases in WMH volumes for age were seen in the group with comorbidities (P<0.0001) but not in the normal group (P=0.173). The between-group difference in slope of expected WMH for age was significantly greater in the comorbidity groups≥50 years of age (P=0.0008) but not <50 years of age (P=0.752). CONCLUSIONS: After 50 years of age, but not before, comorbid hypertension, obesity, and diabetes mellitus were associated with significantly larger WMH volumes for age compared with a normal aging group lacking these conditions. These results support the assertion that age-related differences in WMH volumes are significantly increased in the presence of comorbidities, but the effect is only detectable after 50 years of age.

Fully automated tool to identify the aorta and compute flow using phase-contrast MRI: validation and application in a large population based study.

PURPOSE: To assess if fully automated localization of the aorta can be achieved using phase contrast (PC) MR images. MATERIALS AND METHODS: PC cardiac-gated MR images were obtained as part of a large population-based study. A fully automated process using the Hough transform was developed to localize the ascending aorta (AAo) and descending aorta (DAo). The study was designed to validate this technique by determining: (i) its performance in localizing the AAo and DAo; (ii) its accuracy in generating AAo flow volume and DAo flow volume; and (iii) its robustness on studies with pathological abnormalities or imaging artifacts. RESULTS: The algorithm was applied successfully on 1884 participants. In the randomly selected 50-study validation set, linear regression shows an excellent correlation between the automated (A) and manual (M) methods for AAo flow (r = 0.99) and DAo flow (r = 0.99). Bland-Altman difference analysis demonstrates strong agreement with minimal bias for: AAo flow (mean difference [A-M] = 0.47 ± 2.53 mL), and DAo flow (mean difference [A-M] = 1.74 ± 2.47 mL). CONCLUSION: A robust fully automated tool to localize the aorta and provide flow volume measurements on phase contrast MRI was validated on a large population-based study.

Cardiovascular outcome associations among cardiovascular magnetic resonance measures of arterial stiffness: the Dallas heart study.

BACKGROUND: Cardiovascular magnetic resonance (CMR) has been validated for the noninvasive assessment of total arterial compliance and aortic stiffness, but their associations with cardiovascular outcomes is unknown. The purpose of this study was to evaluate associations of CMR measures of total arterial compliance and two CMR measures of aortic stiffness with respect to future cardiovascular events. METHODS: The study consisted of 2122 Dallas Heart Study participants without cardiovascular disease who underwent CMR at 1.5 Tesla. Aortic stiffness was measured by CMR-derived ascending aortic distensibility and aortic arch pulse wave velocity. Total arterial compliance was calculated by dividing left ventricular stroke volume by pulse pressure. Participants were monitored for cardiovascular death, non-fatal cardiac events, and non-fatal extra-cardiac vascular events over 7.8 ± 1.5 years. Cox proportional hazards regression was used to assess for associations between CMR measures and cardiovascular events. RESULTS: Age, systolic blood pressure, and resting heart rate were independently associated with changes in ascending aortic distensibility, arch pulse wave velocity, and total arterial compliance (all p < .0001). A total of 153 participants (6.9%) experienced a cardiovascular event. After adjusting for traditional risk factors, total arterial compliance was modestly associated with increased risk for composite events (HR 1.07 per 1SD, p = 0.03) while the association between ascending aortic distensibility and composite events trended towards significance (HR 1.18 per 1SD, p = 0.08). Total arterial compliance and aortic distensibility were independently associated with nonfatal cardiac events (HR 1.11 per 1SD, p = 0.001 and HR 1.45 per 1SD, p = 0.0005, respectively), but not with cardiovascular death or nonfatal extra-cardiac vascular events. Arch pulse wave velocity was independently associated with nonfatal extra-cardiac vascular events (HR 1.18 per 1SD, p = 0.04) but not with cardiovascular death or nonfatal cardiac events. CONCLUSIONS: In a multiethnic population free of cardiovascular disease, CMR measures of arterial stiffness are associated with future cardiovascular events. Total arterial compliance and aortic distensibility may be stronger predictors of nonfatal cardiac events, while pulse wave velocity may be a stronger predictor of nonfatal extra-cardiac vascular events.

Left atrial structure and function and clinical outcomes in the general population.

AIMS: Left atrial (LA) structural and functional abnormalities may be subclinical phenotypes, which identify individuals at increased risk of adverse outcomes. METHODS AND RESULTS: Maximum LA volume (LAmax) and LA emptying fraction (LAEF) were measured via cardiac magnetic resonance imaging in 1802 participants in the Dallas Heart Study. The associations of LAEF and LAmax indexed to body surface area (LAmax/BSA) with traditional risk factors, natriuretic peptide levels, and left ventricular (LV) structure [end-diastolic volume (EDV) and concentricity(0.67) (mass/EDV(0.67))] and function (ejection fraction) were assessed using linear regression analysis. The incremental prognostic value of LAmax/BSA and LAEF beyond traditional risk factors, LV ejection fraction, and LV mass was assessed using the Cox proportional-hazards model. Both increasing LAmax/BSA and decreasing LAEF were associated with hypertension and natriuretic peptide levels (P < 0.05 for all). In multivariable analysis, LAmax/BSA was most strongly associated with LV end-diastolic volume/BSA, while LAEF was strongly associated with LV ejection fraction and concentricity(0.67). During a median follow-up period of 8.1 years, there were 81 total deaths. Decreasing LAEF [hazard ratio (HR) per 1 standard deviation (SD) (8.0%): 1.56 (1.32-1.87)] but not increasing LAmax/BSA [HR per 1 SD (8.6 mL/m(2)): 1.14 (0.97-1.34)] was independently associated with mortality. Furthermore, the addition of LAEF to a model adjusting Framingham risk score, diabetes, race, LV mass, and ejection fraction improved the c-statistic (c-statistics: 0.78 vs. 0.77; P < 0.05, respectively), whereas the addition of LAmax/BSA did not (c-statistics: 0.76, P = 0.20). CONCLUSION: In the general population, both LAmax/BSA and LAEF are important subclinical phenotypes but LAEF is superior and incremental to LAmax/BSA.

Abdominal aortic atherosclerosis at MR imaging is associated with cardiovascular events: the Dallas heart study.

PURPOSE: To determine the value of two abdominal aortic atherosclerosis measurements at magnetic resonance (MR) imaging for predicting future cardiovascular events. MATERIALS AND METHODS: This study was approved by the institutional review board and complied with HIPAA regulations. The study consisted of 2122 participants from the multiethnic, population-based Dallas Heart Study who underwent abdominal aortic MR imaging at 1.5 T. Aortic atherosclerosis was measured by quantifying mean aortic wall thickness (MAWT) and aortic plaque burden. Participants were monitored for cardiovascular death, nonfatal cardiac events, and nonfatal extracardiac vascular events over a mean period of 7.8 years ± 1.5 (standard deviation [SD]). Cox proportional hazards regression was used to assess independent associations of aortic atherosclerosis and cardiovascular events. RESULTS: Increasing MAWT was positively associated with male sex (odds ratio, 3.66; P < .0001), current smoking (odds ratio, 2.53; P < .0001), 10-year increase in age (odds ratio, 2.24; P < .0001), and hypertension (odds ratio, 1.66; P = .0001). A total of 143 participants (6.7%) experienced a cardiovascular event. MAWT conferred an increased risk for composite events (hazard ratio, 1.28 per 1 SD; P = .001). Aortic plaque was not associated with increased risk for composite events. Increasing MAWT and aortic plaque burden both conferred an increased risk for nonfatal extracardiac events (hazard ratio of 1.52 per 1 SD [P < .001] and hazard ratio of 1.46 per 1 SD [P = .03], respectively). CONCLUSION: MR imaging measures of aortic atherosclerosis are predictive of future adverse cardiovascular events.

White matter hyperintensities: use of aortic arch pulse wave velocity to predict volume independent of other cardiovascular risk factors.

PURPOSE: To evaluate the relationship between pulse wave velocity (PWV) from the aortic arch and subsequent cerebral microvascular disease independent of other baseline cardiovascular risk factors among the participants in the multiethnic Dallas Heart Study. MATERIALS AND METHODS: Each subject gave written consent to participate in this HIPAA-compliant, institutional review board-approved prospective study. Aortic arch PWV was measured with phase-contrast magnetic resonance (MR) imaging in a population sample (n = 1270) drawn from the probability-based Dallas Heart Study. Seven years later, the volume of white matter hyperintensities (WMHs) was determined from brain MR images. Linear regression was conducted with aortic arch PWV, 15 other cardiovascular risk factors, and age, sex, and ethnicity included as predictors of WMH. The authors implemented a smoothly clipped absolute deviation-penalized variable selection method to evaluate an optimal predictive risk factor model. RESULTS: Aortic arch PWV helped predict WMH volume independent of the other demographic and cardiovascular risk factors (regression coefficient: 0.29; standard error: 0.06; 95% confidence interval: 0.17, 0.42; P < .0001). The optimal predictor variables of subsequent WMH volume adjusted for sex and ethnicity included aortic arch PWV, age, systolic blood pressure, hypertension treatment, and congestive heart failure. The authors estimated that a 1% increase in aortic arch PWV (in meters per second) is related to a 0.3% increase in subsequent WMH volume (in milliliters) when all other variables in the model are held constant. CONCLUSION: Aortic arch PWV measured with phase-contrast MR imaging is a highly significant independent predictor of subsequent WMH volume, with a higher standardized effect than any other cardiovascular risk factor assessed except for age. In an optimal predictive model of subsequent WMH burden, aortic arch PWV provides a distinct contribution along with systolic blood pressure, hypertension treatment, congestive heart failure, and age.

Automated quantification of white matter disease extent at 3 T: comparison with volumetric readings.

PURPOSE: To develop and validate an algorithm to automatically quantify white matter hyperintensity (WMH) volume. MATERIALS AND METHODS: Images acquired as part of the Dallas Heart Study, a multiethnic, population-based study of cardiovascular health, were used to develop and validate the algorithm. 3D magnetization prepared rapid acquisition gradient echo (MP-RAGE) and 2D fluid-attenuated inversion recovery (FLAIR) images were acquired from 2082 participants. Images from 161 participants (7.7% of the cohort) were used to set an intensity threshold to maximize the agreement between the algorithm and a qualitative rating made by a radiologist. The resulting algorithm was run on the entire cohort and outlier analyses were used to refine the WMH volume measurement. The refined, automatic WMH burden estimate was then compared to manual quantitative measurements of WMH volume in 28 participants distributed across the range of volumes seen in the entire cohort. RESULTS: The algorithm showed good agreement with the volumetric readings of a trained analyst: the Spearman's Rank Order Correlation coefficient was r = 0.87. Linear regression analysis showed a good correlation WMHml[automated] = 1.02 × WMHml[manual] - 0.48. Bland-Altman analysis showed a bias of 0.34 mL and a standard deviation of 2.8 mL over a range of 0.13 to 41 mL. CONCLUSION: We have developed an algorithm that automatically estimates the volume of WMH burden using an MP-RAGE and a FLAIR image. This provides a tool for evaluating the WMH burden of large populations to investigate the relationship between WMH burden and other health factors.

Risk scores versus natriuretic peptides for identifying prevalent stage B heart failure.

BACKGROUND: Identifying asymptomatic individuals with American Heart Association/American College of Cardiology stage B heart failure (HF) in the population is an important step to prevent the development of symptomatic HF. The comparative utility of 2 screening strategies (biomarkers vs risk scores) in identifying prevalent stage B HF is unknown. METHODS: Participants 30 to 65 years old without symptomatic HF in the Dallas Heart Study who had a cardiac magnetic resonance imaging were included (n = 2,277). Stage B HF (n = 284) was defined by left ventricular (LV) hypertrophy, reduced LV ejection fraction, or prior myocardial infarction. We compared the utility of 2 risk scores (Health Aging and Body Composition HF risk score and the Framingham Heart Failure risk score) with B-type natriuretic peptide (BNP) and N-terminal pro-BNP in identifying stage B HF using logistic regression. RESULTS: Depending upon the method of indexing LV mass (body surface area, fat-free mass, or height(2.7)), the c-statistic for the Health Aging and Body Composition HF risk score (0.73, 0.75, and 0.64, respectively) was greater than that for BNP (0.62, 0.70, and 0.57, respectively) and N-terminal pro-BNP (0.62, 0.69, and 0.56, respectively) (P < .01 for all). These findings were similar for the Framingham Heart Failure risk score except when LV mass was indexed to fat-free mass. Addition of natriuretic peptide levels to the risk scores resulted in a modest but significant improvement in discrimination of stage B HF (Δ c-statistic, 0.01-0.03, P < .05 for all). CONCLUSIONS: Screening for stage B HF in the population is enhanced when natriuretic peptides are measured in addition to, rather than in place of, traditional risk scores.

Sex, race, and age distributions of mean aortic wall thickness in a multiethnic population-based sample.

BACKGROUND: Reference values and age-related changes of the wall thickness of the abdominal aorta have not been described in the general population. We characterized age-, race-, and gender-specific distributions, and yearly rates of change of mean aortic wall thickness (MAWT), and associations between MAWT and cardiovascular risk factors in a multi-ethnic population-based probability sample. METHODS: Magnetic resonance imaging measurements of MAWT were performed on 2466 free-living white, black, and Hispanic adult subjects. MAWT race/ethnicity- and gender-specific percentile values across age were estimated using regression analyses. RESULTS: MAWT was greater in men than in women and increased linearly with age in all the groups and across all the percentiles. Hispanic women had the thinnest and black men the thickest aortas. Black men had the highest and white women the lowest age-related MAWT increase. Age, gender, ethnicity, smoking status, systolic blood pressure, low-density lipoprotein-cholesterol levels, high-density lipoprotein-cholesterol levels, and fasting glucose levels were independent predictors of MAWT. CONCLUSIONS: Age, gender, and racial/ethnic differences in MAWT distributions exist in the general population. Such differences should be considered in future investigations assessing aortic atherosclerosis and the effects of anti-atherosclerotic therapies.

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.

Association of Health Aging and Body Composition (ABC) Heart Failure score with cardiac structural and functional abnormalities in young individuals.

BACKGROUND: The Health ABC Heart Failure score has recently been shown to predict 5-year risk of incident heart failure in the elderly. We tested whether this risk score is associated with subclinical phenotypes of heart failure in a younger population. METHODS: We stratified participants in the Dallas Heart Study aged 30 to 65 years who had a cardiac magnetic resonance imaging and no self-reported history of heart failure or cardiomyopathy into 4 previously defined Health ABC Heart Failure risk groups: low (<5%), average (5%-10%), high (10%-20%), and very high (>20% risk for heart failure within 5 years). We compared left ventricular (LV) structural and functional parameters and levels of B-type natriuretic peptide (BNP) and N-terminal proBNP among the 4 groups. RESULTS: In the study cohort (N = 2,540), the percentage of subjects in the low-, average-, high-, and very high risk groups was 78%, 15%, 6%, and 1%, respectively. Indexed LV mass (80 +/- 15 vs 90 +/- 20 vs 95 +/- 25 vs 116 +/- 41 g/m(2)), concentricity (1.6 +/- 0.3 vs 1.8 +/- 0.4 vs 2.0 +/- 0.5 vs 2.2 +/- 0.7 g/mL), median BNP (2.8 vs 3.7 vs 4.9 vs 7.5 pg/mL) and N-terminal proBNP (26 vs 30 vs 40 vs 58 pg/mL), and prevalent LV systolic dysfunction and LV hypertrophy progressively increased across risk groups (P < .001 for all) independent of gender or method of indexing LV mass. CONCLUSIONS: The Health ABC Heart Failure score was associated with subclinical cardiac structural changes in the general population 30 to 65 years of age, suggesting that it may be a valid tool for identification of young individuals at increased risk for heart failure.