Frailty and Metabolic Vulnerability in Heart Failure: A Community Cohort Study

BACKGROUND

Frailty is common in heart failure (HF) and is associated with death but not routinely captured clinically. Frailty is linked with inflammation and malnutrition, which can be assessed by a novel plasma multimarker score: the metabolic vulnerability index (MVX). We sought to evaluate the associations between frailty and MVX and their prognostic impact.

METHODS AND RESULTS

In an HF community cohort (2003-2012), we measured frailty as a proportion of deficits present out of 32 physical limitations and comorbidities, MVX by nuclear magnetic resonance spectroscopy, and collected extensive longitudinal clinical data. Patients were categorized by frailty score (≤0.14, >0.14 and ≤0.27, >0.27) and MVX score (≤50, >50 and ≤60, >60 and ≤70, >70). Cox models estimated associations of frailty and MVX with death, adjusted for Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Uno's C-statistic measured the incremental value of MVX beyond frailty and clinical factors. Weibull's accelerated failure time regression assessed whether MVX mediated the association between frailty and death. We studied 985 patients (median age, 77; 48% women). Frailty and MVX were weakly correlated (Spearman's ρ=0.21). The highest frailty group experienced an increased rate of death, independent of MVX, MAGGIC score, and NT-proBNP (hazard ratio, 3.3 [95% CI, 2.5-4.2]). Frailty improved Uno's c-statistic beyond MAGGIC score and NT-proBNP (0.69-0.73). MVX only mediated 3.3% and 4.5% of the association between high and medium frailty groups and death, respectively.

CONCLUSIONS

In this HF cohort, frailty and MVX are weakly correlated. Both independently contribute to stratifying the risk of death, suggesting that they capture distinct domains of vulnerability in HF.

Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort

BACKGROUND

Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD.

METHODS AND RESULTS

Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up.

CONCLUSIONS

VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms

BACKGROUND AND PURPOSE

Feature variability in radiomics studies due to technical and magnet strength parameters is well known and may be addressed through various pre-processing methods. However, very few studies have evaluated downstream impact of variable pre-processing on model classification performance in a multi-class setting. We sought to evaluate the impact of SUSAN denoising and ComBat harmonization on model classification performance.

MATERIALS AND METHODS

A total of 493 cases (410 internal and 83 external dataset) of glioblastoma (GB), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL) underwent semi-automated 3D-segmentation post baseline image processing (BIP) consisting of resampling, realignment, co-registration, skull stripping and image normalization. Post BIP, two sets were generated, one with and another without SUSAN denoising (SD). Radiomics features were extracted from both datasets and batch corrected to produce four datasets: (a) BIP, (b) BIP with SD, (c) BIP with ComBat and (d) BIP with both SD and ComBat harmonization. Performance was then summarized for models using a combination of six feature selection techniques and six machine learning models across four mask-sequence combinations with features derived from one-three (multi-parametric) MRI sequences.

RESULTS

Most top performing models on the external test set used BIP+SD derived features. Overall, use of SD and ComBat harmonization led to a slight but generally consistent improvement in model performance on the external test set.

CONCLUSIONS

The use of image pre-processing steps such as SD and ComBat harmonization may be more useful in a multiinstitutional setting and improve model generalizability. Models derived from only T1-CE images showed comparable performance to models derived from multiparametric MRI.

A complement C4-derived glycopeptide is a biomarker for PMM2-CDG

BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).

Long-Term Ultrasound Twinkling Detectability and Safety of a Polymethyl Methacrylate Soft Tissue Marker Compared to Conventional Breast Biopsy Markers-A Preclinical Study in a Porcine Model

OBJECTIVE

We have studied the use of polymethyl methacrylate (PMMA) as an alternative biopsy marker that is readily detectable with ultrasound Doppler twinkling in cases of in vitro, ex vivo, or limited duration in vivo settings. This study investigates the long-term safety and ultrasound Doppler twinkling detectability of a PMMA breast biopsy marker following local perturbations and different dwell times in a 6-mo animal experiment.

METHODS

This study, which was approved by our Institutional Animal Care and Use Committee, involved three pigs and utilized various markers, including PMMA (Zimmer Biomet), 3D-printed, and Tumark Q markers. Markers were implanted at different times for each pig. Mesh material or ethanol was used to induce a local inflammatory reaction near certain markers. A semiquantitative twinkling score assessed twinkling for actionable localization during monthly ultrasounds. At the primary endpoint, ultrasound-guided localization of lymph nodes with detectable markers was performed. Following surgical resection of the localized nodes, histomorphometric analysis was conducted to evaluate for tissue ingrowth and the formation of a tissue rind around the markers.

RESULTS

No adverse events occurred. Twinkling scores of all markers for all three pigs decreased gradually over time. The Q marker exhibited the highest mean twinkling score followed by the PMMA marker, PMMA with mesh, and Q with ethanol. The 3D-printed marker with mesh and PMMA with ethanol had the lowest scores. All wire-localized lymph nodes were successfully resected. Despite varying percentages of tissue rind around the markers and a significant reduction in overall twinkling (p < 0.001) over time, mean PMMA twinkling scores remained clinically actionable at 6 and 5 mo using a General Electric C1-6 probe and 9L-probe, respectively.

CONCLUSIONS

In this porcine model, the PMMA marker demonstrates an acceptable safety profile. Clinically actionable twinkling aids PMMA marker detection even after 6 mo of dwell time in porcine lymph nodes. The Q marker maintained the greatest twinkling over time compared to all the other markers studied.

Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study

BACKGROUND

Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure.

METHODS

Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed.

RESULTS

The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP.

CONCLUSIONS

This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

Associations of Circulating Vascular Cell Adhesion Molecule-1 and Intercellular Adhesion Molecule-1 With Long-Term Cardiac Function

BACKGROUND

Although VCAM-1 (vascular cell adhesion molecule-1) and ICAM-1 (intercellular adhesion molecule-1) have been associated with incident heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF), the associations of VCAM-1 and ICAM-1 with sensitive measures of cardiac structure/function are unclear. The objective of this study is to evaluate associations between VCAM-1, ICAM-1, and measures of cardiac structure and function as potential pathways through which cellular adhesion molecules promote HFpEF and AF risk.

METHODS AND RESULTS

In MESA (Multi-Ethnic Study of Atherosclerosis), we evaluated the associations of circulating VCAM-1 and ICAM-1 at examination 2 (2002-2004) with measures of cardiac structure/function on cardiac magnetic resonance imaging at examination 5 (2010-2011) after multivariable adjustment. Mediation analysis of left atrial (LA) strain on the association between VCAM-1 or ICAM-1 and AF or HFpEF was also performed. Overall, 2304 individuals (63±10 years; 47% men) with VCAM-1 or ICAM-1, cardiac magnetic resonance imaging, and covariate data were included in analysis. Higher VCAM-1 and ICAM-1 were associated with lower LA peak longitudinal strain and worse global circumferential left ventricular strain but were not associated with left ventricular myocardial scar or interstitial fibrosis. Lower LA peak longitudinal strain mediated 8% (95% CI, 2-30) of the relationship between VCAM-1 and HFpEF and 9% (95% CI, 2-21) of the relationship between VCAM-1 and AF.

CONCLUSIONS

Higher VCAM-1 and ICAM-1 were associated with lower LA function and left ventricular systolic function but were not associated with myocardial scar or interstitial fibrosis. VCAM-1 and ICAM-1 may promote HFpEF and AF risk through impaired LA reservoir function.

The Importance of Estimating Excess Deaths Regionally During the COVID-19 Pandemic

National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.

Predicting postoperative systolic dysfunction in mitral regurgitation: CT vs. echocardiography

Introduction

Volume overload from mitral regurgitation can result in left ventricular systolic dysfunction. To prevent this, it is essential to operate before irreversible dysfunction occurs, but the optimal timing of intervention remains unclear. Current echocardiographic guidelines are based on 2D linear measurement thresholds only. We compared volumetric CT-based and 2D echocardiographic indices of LV size and function as predictors of post-operative systolic dysfunction following mitral repair.

Methods

We retrospectively identified patients with primary mitral valve regurgitation who underwent repair between 2005 and 2021. Several indices of LV size and function measured on preoperative cardiac CT were compared with 2D echocardiography in predicting post-operative LV systolic dysfunction (LVEFecho <50%). Area under the curve (AUC) was the primary metric of predictive performance.

Results

A total of 243 patients were included (mean age 57 ± 12 years; 65 females). The most effective CT-based predictors of post-operative LV systolic dysfunction were ejection fraction [LVEFCT; AUC 0.84 (95% CI: 0.77-0.92)] and LV end systolic volume indexed to body surface area [LVESViCT; AUC 0.88 (0.82-0.95)]. The best echocardiographic predictors were LVEFecho [AUC 0.70 (0.58-0.82)] and LVESDecho [AUC 0.79 (0.70-0.89)]. LVEFCT was a significantly better predictor of post-operative LV systolic dysfunction than LVEFecho (p = 0.02) and LVESViCT was a significantly better predictor than LVESDecho (p = 0.03). Ejection fraction measured by CT demonstrated significantly greater reproducibility than echocardiography.

Discussion

CT-based volumetric measurements may be superior to established 2D echocardiographic parameters for predicting LV systolic dysfunction following mitral valve repair. Validation with prospective study is warranted.

Circulating ketone bodies and mortality in heart failure: a community cohort study

Background

The relationship between ketone bodies (KB) and mortality in patients with heart failure (HF) syndrome has not been well established.

Objectives

The aim of this study is to assess the distribution of KB in HF, identify clinical correlates, and examine the associations between plasma KB and all-cause mortality in a population-based HF cohort.

Methods

The plasma KB levels were measured by nuclear magnetic resonance spectroscopy. Multivariable linear regression was used to examine associations between clinical correlates and KB levels. Proportional hazard regression was employed to examine associations between KB (represented as both continuous and categorical variables) and mortality, with adjustment for several clinical covariates.

Results

Among the 1,382 HF patients with KB measurements, the median (IQR) age was 78 (68, 84) and 52% were men. The median (IQR) KB was found to be 180 (134, 308) μM. Higher KB levels were associated with advanced HF (NYHA class III-IV) and higher NT-proBNP levels (both P < 0.001). The median follow-up was 13.9 years, and the 5-year mortality rate was 51.8% [95% confidence interval (CI): 49.1%-54.4%]. The risk of death increased when KB levels were higher (HRhigh vs. low group 1.23; 95% CI: 1.05-1.44), independently of a validated clinical risk score. The association between higher KB and mortality differed by ejection fraction (EF) and was noticeably stronger among patients with preserved EF.

Conclusions

Most patients with HF exhibited KB levels that were consistent with those found in healthy adults. Elevated levels of KB were observed in patients with advanced HF. Higher KB levels were found to be associated with an increased risk of death, particularly in patients with preserved EF.

Quantitative Biomarkers Derived from a Novel, Contrast-Free Ultrasound, High-Definition Microvessel Imaging for Differentiating Choroidal Tumors

Angiogenesis has an essential role in the de novo evolution of choroidal melanoma as well as choroidal nevus transformation into melanoma. Differentiating early-stage melanoma from nevus is of high clinical importance; thus, imaging techniques that provide objective information regarding tumor microvasculature structures could aid accurate early detection. Herein, we investigated the feasibility of quantitative high-definition microvessel imaging (qHDMI) for differentiation of choroidal tumors in humans. This new ultrasound-based technique encompasses a series of morphological filtering and vessel enhancement techniques, enabling the visualization of tumor microvessels as small as 150 microns and extracting vessel morphological features as new tumor biomarkers. Distributional differences between the malignant melanomas and benign nevi were tested on 37 patients with choroidal tumors using a non-parametric Wilcoxon rank-sum test, and statistical significance was declared for biomarkers with p-values < 0.05. The ocular oncology diagnosis was choroidal melanoma (malignant) in 21 and choroidal nevus (benign) in 15 patients. The mean thickness of benign and malignant masses was 1.70 ± 0.40 mm and 3.81 ± 2.63 mm, respectively. Six HDMI biomarkers, including number of vessel segments (p = 0.003), number of branch points (p = 0.003), vessel density (p = 0.03), maximum tortuosity (p = 0.001), microvessel fractal dimension (p = 0.002), and maximum diameter (p = 0.003) exhibited significant distributional differences between the two groups. Contrast-free HDMI provided noninvasive imaging and quantification of microvessels of choroidal tumors. The results of this pilot study indicate the potential use of qHDMI as a complementary tool for characterization of small ocular tumors and early detection of choroidal melanoma.

A Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization

OBJECTIVE

Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging.

METHODS

54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting.

RESULTS

Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025).

CONCLUSIONS

We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions.

SIGNIFICANCE

With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.

Development and Validation of a Protein Risk Score for Mortality in Heart Failure : A Community Cohort Study

BACKGROUND

Heart failure (HF) is a complex clinical syndrome with high mortality. Current risk stratification approaches lack precision. High-throughput proteomics could improve risk prediction. Its use in clinical practice to guide the management of patients with HF depends on validation and evidence of clinical benefit.

OBJECTIVE

To develop and validate a protein risk score for mortality in patients with HF.

DESIGN

Community-based cohort.

SETTING

Southeast Minnesota.

PARTICIPANTS

Patients with HF enrolled between 2003 and 2012 and followed through 2021.

MEASUREMENTS

A total of 7289 plasma proteins in 1351 patients with HF were measured using the SomaScan Assay (SomaLogic). A protein risk score was derived using least absolute shrinkage and selection operator regression and temporal validation in patients enrolled between 2003 and 2007 (development cohort) and 2008 and 2012 (validation cohort). Multivariable Cox regression was used to examine the association between the protein risk score and mortality. The performance of the protein risk score to predict 5-year mortality risk was assessed using calibration plots, decision curves, and relative utility analyses and compared with a clinical model, including the Meta-Analysis Global Group in Chronic Heart Failure mortality risk score and N-terminal pro-B-type natriuretic peptide.

RESULTS

The development (n = 855; median age, 78 years; 50% women; 29% with ejection fraction <40%) and validation cohorts (n = 496; median age, 76 years; 45% women; 33% with ejection fraction <40%) were mostly similar. In the development cohort, 38 unique proteins were selected for the protein risk score. Independent of ejection fraction, the protein risk score demonstrated good calibration, reclassified mortality risk particularly at the extremes of the risk distribution, and showed greater clinical utility compared with the clinical model.

LIMITATION

Participants were predominantly of European ancestry, potentially limiting the generalizability of the findings to different patient populations.

CONCLUSION

Validation of the protein risk score demonstrated good calibration and evidence of predicted benefits to stratify the risk for death in HF superior to that of clinical methods. Further studies are needed to prospectively evaluate the score's performance in diverse populations and determine risk thresholds for interventions.

PRIMARY FUNDING SOURCE

Division of Intramural Research at the National Heart, Lung, and Blood Institute of the National Institutes of Health.

Acute Adverse Events After Iodinated Contrast Agent Administration of 359,977 Injections: A Single-Center Retrospective Study

OBJECTIVE

To assess the effects of patient variables, examination variables, and seasonality on allergic-like and physiologic reactions to iodinated contrast material (ICM).

PATIENTS AND METHODS

All ICM-enhanced computed tomography (CT) examinations performed from June 1, 2009, to May 9, 2017, at our institution were included. Reactions were identified and categorized as allergic-like or physiologic and mild, moderate, or severe. The effect of patient and examination variables on reactions was evaluated by logistic regression models.

RESULTS

A total of 359,977 CT examinations performed on 176,886 unique patients were included. A total of 1150 allergic-like reactions (0.32%; 19 severe [0.005%]) and 679 physiologic reactions (0.19%; 3 severe [0.0008%]) occurred. On multivariable analysis, iopromide had higher rates of reactions compared with iohexol (allergic-like reactions: odds ratio [OR], 3.07 [95% CI, 2.37 to 3.98], P<.0001; physiologic reactions: OR, 2.60 [1.92 to 3.52], P<.0001). Non-White patients had higher rates of reactions compared with White patients (allergic-like reactions: OR, 1.77 [1.36-2.30], P<.0001; physiologic reactions: OR, 1.76 [1.27-2.42], P=.0006). Patient age, sex, prior ICM reaction, ICM dose, CT location, and CT type were also significantly associated with reactions. No significant seasonality trend was observed (P=.07 and .80).

CONCLUSION

Non-White patients and patients administered iopromide had higher rates of acute reactions compared with White patients and patients administered iohexol. Younger patients (<50 years vs 51 to 60 years), female sex, history of ICM allergy or other allergies, ICM dose, and contrast-enhanced CT location and type also correlated with higher acute reaction rates.

Age-Dependent Sex Differences in the Prevalence of Selective Serotonin Reuptake Inhibitor Treatment: A Retrospective Cohort Analysis

Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.

Mendelian randomization in hepatology: A review of principles, opportunities, and challenges

Mendelian randomization has become a popular tool to assess causal relationships using existing observational data. While randomized controlled trials are considered the gold standard for establishing causality between exposures and outcomes, it is not always feasible to conduct a trial. Mendelian randomization is a causal inference method that uses observational data to infer causal relationships by using genetic variation as a surrogate for the exposure of interest. Publications using the approach have increased dramatically in recent years, including in the field of hepatology. In this concise review, we describe the concepts, assumptions, and interpretation of Mendelian randomization as related to studies in hepatology. We focus on the strengths and weaknesses of the approach for a non-statistical audience, using an illustrative example to assess the causal relationship between body mass index and NAFLD.

Association Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P<0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1-SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04-1.12; P<0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; P=0.11) or in the reverse direction (β=-0.012; P=0.076). Additional bidirectional Mendelian randomization analyses revealed that low-density lipoprotein cholesterol had a causal effect on mtDNA CN (β=-0.084; P<0.001), but the reverse direction was not significant (P=0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low-density lipoprotein cholesterol level (P=0.52), whereas there was a strong direct causal effect of higher low-density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=-0.092; P<0.001). Conclusions Our findings indicate that high low-density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.

Non-invasive Measurement of the Viscoelasticity of the Optic Nerve and Sclera for Assessing Papilledema: A Pilot Clinical Study

OBJECTIVE

The purpose of this study was to evaluate our novel ultrasound vibro-elastography (UVE) technique for assessing patients with papilledema by non-invasively measuring shear wave speed (SWS), elasticity and viscosity properties of the optic nerve and sclera.

METHODS

Shear wave speeds were measured at three frequencies-100, 150 and 200 Hz-on the optic nerve and sclera tissues for assessing patients with papilledema resulting from idiopathic intracranial hypertension (IIH). The method was evaluated in six papilledema patients and six controls on two separate locations for each participant (i.e., optic nerve and posterior sclera). SWSs of the optic nerve and sclera were analyzed by using a 2-D speed map technique within a circular region of interest (ROI) (i.e., the diameter of the ROI was 1.5 mm × 3.0 mm at the optic nerve and sclera, respectively). Elasticity and viscosity were then analyzed using the wave speed dispersion over the three frequencies.

RESULTS

We measured values of SWS at both locations, optic nerve and sclera, of the right eye and left eye at three different frequencies in IIH patients and controls. The SWS (mean ± standard deviation [m/s]) of the right eye was significantly higher at the sclera in IIH patients compared with controls (i.e., patients vs. controls: 5.91 ± 0.54 vs. 3.86 ± 0.56, p < 0.0001 at 100 Hz), but there was no significant difference at the optic nerve (i.e., patients vs. controls: 3.62 ± 0.39 vs. 3.36 ± 0.35, p = 0.1100 at 100Hz). We observed increased elasticity (kPa) in IIH patients, indicating there are significant differences in elasticity between patients and controls at the optic nerve and sclera (i.e., right eye [patients vs. controls]: 14.42 ± 6.59 vs. 6.5 ± 5.71, p = 0.0065 [optic nerve]; 33.04 ± 10.62 vs. 9.16 ± 7.15, p < 0.0001 [sclera]). Viscosity was also (Pa·s) higher in the sclera and optic nerve of the left eye (i.e., left eye [patient vs. control]: 8.89 ± 4.37 vs. 7.27 ± 5.01, p = 0.3790 (optic nerve); 16.05 ± 10.79 vs. 8.49 ± 6.09, p < 0.0194 [sclera]).

CONCLUSION

This research illustrates the feasibility of using our UVE system to evaluate stiffness of different tissues in the eye non-invasively. It suggests that the viscoelasticity of the posterior sclera is higher than that of the optic nerve. We found that the posterior sclera is stiffer than the optic nerve in patients with papilledema resulting from IIH, making UVE a potential non-invasive technique for assessing papilledema.

Association Between Fluctuations in Blood Lipid Levels Over Time With Incident Alzheimer Disease and Alzheimer Disease-Related Dementias

BACKGROUND AND OBJECTIVES

Prevention strategies for Alzheimer disease and Alzheimer disease-related dementias (AD/ADRDs) are urgently needed. Lipid variability, or fluctuations in blood lipid levels at different points in time, has not been examined extensively and may contribute to the risk of AD/ADRD. Lipid panels are a part of routine screening in clinical practice and routinely available in electronic health records (EHR). Thus, in a large geographically defined population-based cohort, we investigated the variation of multiple lipid types and their association to the development of AD/ADRD.

METHODS

All residents living in Olmsted County, Minnesota on the index date January 1, 2006, aged 60 years or older without an AD/ADRD diagnosis were identified. Persons with ≥3 lipid measurements including total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), or high-density lipoprotein cholesterol (HDL-C) in the 5 years before index date were included. Lipid variation was defined as any change in individual's lipid levels over time regardless of direction and was measured using variability independent of the mean (VIM). Associations between lipid variation quintiles and incident AD/ADRD were assessed using Cox proportional hazards regression. Participants were followed through 2018 for incident AD/ADRD.

RESULTS

The final analysis included 11,571 participants (mean age 71 years; 54% female). Median follow-up was 12.9 years with 2,473 incident AD/ADRD cases. After adjustment for confounding variables including sex, race, baseline lipid measurements, education, BMI, and lipid-lowering treatment, participants in the highest quintile of total cholesterol variability had a 19% increased risk of incident AD/ADRD, and those in highest quintile of triglycerides, variability had a 23% increased risk.

DISCUSSION

In a large EHR derived cohort, those in the highest quintile of variability for total cholesterol and triglyceride levels had an increased risk of incident AD/ADRD. Further studies to identify the mechanisms behind this association are needed.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods

PURPOSE

To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

METHODOLOGY

Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC.

RESULTS

Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]).

CONCLUSION

Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Missense Genetic Variation of ICAM1 and Incident Heart Failure

BACKGROUND

Intercellular adhesion molecule-1 (ICAM-1) is a cell surface protein that participates in endothelial activation and is hypothesized to play a central role in heart failure (HF). We evaluated associations of ICAM1 missense genetic variants with circulating ICAM-1 levels and with incident HF.

METHODS AND RESULTS

We identified 3 missense variants within ICAM1 (rs5491, rs5498 and rs1799969) and evaluated their associations with ICAM-1 levels in the Coronary Artery Risk Development in Young Adults Study and the Multi-Ethnic Study of Atherosclerosis (MESA). We determined the association among these 3 variants and incident HF in MESA. We separately evaluated significant associations in the Atherosclerosis Risk in Communities (ARIC) study. Of the 3 missense variants, rs5491 was common in Black participants (minor allele frequency [MAF] > 20%) and rare in other race/ethnic groups (MAF < 5%). In Black participants, the presence of rs5491 was associated with higher levels of circulating ICAM-1 at 2 timepoints separated by 8 years. Among Black participants in MESA (n = 1600), the presence of rs5491 was associated with an increased risk of incident HF with preserved ejection fraction (HFpEF; HR = 2.30; [95% CI 1.25-4.21; P = 0.007]). The other ICAM1 missense variants (rs5498 and rs1799969) were associated with ICAM-1 levels, but there were no associations with HF. In ARIC, rs5491 was significantly associated with incident HF (HR = 1.24 [95% CI 1.02 - 1.51]; P = 0.03), with a similar direction of effect for HFpEF that was not statistically significant.

CONCLUSIONS

A common ICAM1 missense variant among Black individuals may be associated with increased risk of HF, which may be HFpEF-specific.

Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients

CONTEXT

Metformin is the first-line drug for treating diabetes but has a high failure rate.

OBJECTIVE

To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure.

METHODS

A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure.

RESULTS

In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001).

CONCLUSION

Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach

PURPOSE

Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo.

EXPERIMENTAL DESIGN

The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology.

RESULTS

Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%.

CONCLUSIONS

The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

Health Care Utilization and Death in Patients With Heart Failure During the COVID-19 Pandemic

Objective

To compare the 1-year health care utilization and mortality in persons living with heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic.

Patients and Methods

Residents of a 9-county area in southeastern Minnesota aged 18 years or older with a HF diagnosis on January 1, 2019; January 1, 2020; and January 1, 2021, were identified and followed up for 1-year for vital status, emergency department (ED) visits, and hospitalizations.

Results

We identified 5631 patients with HF (mean age, 76 years; 53% men) on January 1, 2019, 5996 patients (mean age, 76 years; 52% men) on January 1, 2020, and 6162 patients (mean age, 75 years; 54% men) on January 1, 2021. After adjustment for comorbidities and risk factors, patients with HF in 2020 and patients with HF in 2021 experienced similar risks of mortality compared with those in 2019. After adjustment, patients with HF in 2020 and 2021 were less likely to experience all-cause hospitalizations (2020: rate ratio [RR], 0.88; 95% CI, 0.81-0.95; 2021: RR, 0.90; 95% CI, 0.83-0.97) compared with patients in 2019. Patients with HF in 2020 were also less likely to experience ED visits (RR, 0.85; 95% CI, 0.80-0.92).

Conclusion

In this large population-based study in southeastern Minnesota, we observed an approximately 10% decrease in hospitalizations among patients with HF in 2020 and 2021 and a 15% decrease in ED visits in 2020 compared with those in 2019. Despite the change in health care utilization, we found no difference in the 1-year mortality between patients with HF in 2020 and those in 2021 compared with those in 2019. It is unknown whether any longer-term consequences will be observed.

Evaluating Variability of Commercial Liver Fibrosis Elastography Phantoms

OBJECTIVE

Liver fibrosis has been found to increase the mechanical stiffness of the liver. To mimic different stages of liver fibrosis, commercially available phantoms (Model 039, CIRS, Inc.) have been produced for clinical quality assurance and research purposes. The purpose of this study was to investigate the mechanical property variability of the phantoms in two lots of CIRS Model 039 phantoms.

METHODS

Each lot consisted of phantoms of four stiffness types, and there were 8-10 phantoms of each type. Shear wave elastography measurements were conducted on each phantom at 10 different angles. Group velocity measurements and phase velocity curves were calculated for every SWE acquisition. Multilevel functional principal component analysis (MFPCA) was performed on phase velocity data, which decomposes each phase velocity curve into the sum of eigenfunctions of two levels. The variance of the component scores of levels 1 and 2 were used to represent inter-phantom and intra-phantom variability, respectively. The 95% confidence intervals of phase velocity in a phantom type were calculated to reflect curve variability.

DISCUSSION

The standard deviations of the group velocity for phantoms of any type were less than 0.04 and 0.02 m/s for lots 1 and 2, respectively. For both lots, in every type, the phase velocity curves of most individual phantoms fall within the 95% confidence interval.

CONCLUSION

MFPCA is an effective tool for analyzing the inter- and intra-phantom variability of phase velocity curves. Given the known variability of a fully tested lot, estimation of the variability of a new lot can be performed with a reduced number of phantoms tested.

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

Clinical follow-up in patients with moderate or severe allergic-like reactions to iodinated contrast material

OBJECTIVE

To examine follow-up care in patients with a history of acute allergic-like reaction to iodinated contrast material (ICM), including subsequent imaging management, allergy consultation, and repeat ICM exposure and reactions.

METHODS

All patients who had a moderate or severe acute allergic-like reaction to ICM following contrast-enhanced (CE)CT exam from June 1, 2009 -January 1, 2022 at our institution were included. Chart review was performed to determine 1) whether subsequent imaging was not performed or altered in these patients, 2) whether the patient underwent a subsequent CECT exam, and 3) whether the patient had an allergist consultation.

RESULTS

A total of 251 patients were identified. One-third of patients (90/251, 36%) had at least one change to their subsequent imaging management due to their reaction, including performing an unenhanced CT (62/251, 25%) or MRI (22/251, 8.8%) instead of a CECT, or not performing a CECT when otherwise clinically indicated (20/251, 8.0%). Patients with a prior severe reaction were more likely to have a change in management than patients with a prior moderate reaction (Severe: 22/32 (69%) vs. Moderate: 68/219 (31%), p<.0001). Only 17 patients (6.8%) had an allergy consult for their ICM reaction. A total of 90 patients underwent 274 subsequent CECT exams. Repeat allergic-like reactions were observed in one quarter of patients (24/90, 27%) and a tenth of CECT exams (29/274, 11%).

DISCUSSION

One-third of patients with a history of a moderate or severe allergic-like reaction to ICM had their subsequent imaging care modified due to their reaction.

Variability in Lipid Levels and Risk for Cardiovascular Disease: An Electronic Health Record-Based Population Cohort Study

Background Larger within-patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record-based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD-free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06-1.37]). Results were similar for low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. Conclusions In a large electronic health record-based population cohort, high variability in total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Vitrification versus slow freezing of human ovarian tissue: a systematic review and meta-analysis of histological outcomes

A systematic review and meta-analysis of pertinent literature published from 2006 to January 2022 were conducted to study and compare vitrification and slow freezing, the two prominent methods of ovarian tissue cryopreservation. The primary outcome measures for this study were (1) proportion of intact primordial follicles, (2) proportion of intact stromal cells, (3) proportion of DNA fragmentation in primordial follicles, and (4) mean primordial follicle density. This meta-analysis of 19 studies revealed a significantly greater proportion of intact stromal cells in vitrified tissue versus slow-frozen tissue. No significant differences upon pooled analyses were observed between the two cryopreservation methods with respect to the proportion of intact primordial follicles, proportion of DNA fragmentation, or mean primordial follicle density. Due to differences seen in stromal cell viability, vitrification may be a preferred option to preserve histology of tissue. However, more work should be done to compare the two freezing techniques with less heterogeneity caused by patients, samples, and protocols.

Abstract P173: Proteomic Assessment of Progressive Chronic Renal Insufficiency Risk and Mortality in a Heart Failure Community Study

Introduction: Over six million U.S. adults have heart failure (HF). Evaluation of kidney function is critical to the care of patients with HF but may not be fully captured by traditional measures, such as estimated glomerular filtration rate (eGFR) or the Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Identifying patients at highest risk of progressive chronic renal insufficiency (PCRI) may improve risk stratification in HF, beyond these traditional measures. High-throughput proteomics has allowed for the development of PCRI risk scores based on novel kidney function biomarkers. However, the prognostic value of a proteomic-based PCRI risk score in HF has not been explored.

Hypothesis: Proteomic-based PCRI risk scores will improve risk stratification in HF.

Methods: Clinical data and plasma were collected from 1,389 patients in a HF community cohort from Southeastern Minnesota (2003-2012). Results from the aptamer-based technology SomaScan® were used to derive PCRI risk scores using the SomaSignal™ Kidney Prognosis test, a protein-based algorithm developed to predict risk of PCRI within four years. Cox proportional hazard models were used to estimate the association between quintiles of PCRI and mortality, after adjustment for the MAGGIC score.

Results: PCRI risk scores were available for 1,349 patients who were on average 75±13 years of age, 48% female, and had a median eGFR of 57 mL/min/1.73m 2 . There was a positive association with mortality across PCRI risk quintiles, after adjustment for the MAGGIC score. (Figure). Overall, the highest quintile was associated over a two-fold higher risk of mortality compared with the lowest quintile (HR 2.3, 95% CI 1.8,2.8). The higher risk of mortality remained in analyses stratified by patients with (HR 1.5, 95% CI 1.1,2.1) and without (HR 2.2, 95% CI 1.4,3.5) an eGFR <60 mL/min/1.73 m 2 .

Conclusions: In this community HF cohort, proteomic-based PCRI risk scores improve risk stratification, independent of traditional measures of kidney function.

Abstract P524: Metabolic Vulnerability and Frailty for Risk Stratification in Heart Failure: A Community Cohort Study

Introduction: Over 6 million people in the U.S. have heart failure (HF), less than half are expected to survive beyond 5 years. The need to better stratify mortality risk in HF is recognized. Frailty is associated with mortality in HF but not routinely measured clinically. As frailty is linked to inflammation and malnutrition, we hypothesized that the Metabolic Vulnerability Index (MVX) a multimarker score of systemic inflammation (small HDL particles, GlycA) and malnutrition (leucine, valine, isoleucine, citrate), could serve as a biomarker of frailty to predict mortality risk.

Methods: Clinical data and plasma were collected from 1,389 patients from a HF community cohort between 2003-2012. We measured frailty using the Rockwood Index as the proportion of deficits present out of 32 physical limitations and comorbidities. MVX was calculated from the nuclear magnetic resonance LipoProfile® test. Patients were categorized by frailty (0-0.15; 0.16-0.27; 0.28-0.78) and MVX (33.4-50, 50-60, 60-70, 70-85.8) cutpoints. Cox models estimated the association of frailty and MVX assignment with mortality, adjusted for Meta-Analysis Global Group in Chronic HF (MAGGIC) score, a validated clinical risk score for HF mortality.

Results: Frailty and MVX scores were available in 985 patients (median age 77, IQR: 67-84; 48% women). Higher frailty was associated with higher MVX (p-trend < 0.001). The highest frailty and MVX groups experienced large increases in risk of death, after adjustment for MAGGIC score (HR=3.3, 95% CI=2.6-4.2) and (HR=2.7, 95% CI=2.1-3.5), respectively. When adjusted for one another and MAGGIC score, MVX and frailty associations with death were only minimally attenuated: frailty (HR=3.2, 95% CI=2.5-4.0) and MVX (HR=2.4, 95% CI=1.9-3.2) (Figure 1).

Conclusion: In this community cohort of patients with HF, frailty and MVX are positively associated with one another. However, both indicators are independently associated with an increased risk of death and can contribute to risk stratification.

Abstract P177: Circulating Ketone Bodies and Mortality in Heart Failure: A Community Cohort Study

Background: Heart failure (HF) is associated with metabolic alterations, including ketogenesis. However, determinants of ketogenesis and risk of mortality in HF is not defined. Total ketone bodies (KB) include β-hydroxybutyrate, acetoacetate, and acetone and can be measured in plasma by nuclear magnetic resonance (NMR). The aim of this study is to determine the relationship between KB and clinical characteristics in a community HF cohort and to assess the association between KB and all-cause mortality.

Methods: A population-based cohort of 1,389 HF patients was prospectively enrolled between 2003 and 2012. Plasma KB was measured by LP4 NMR LipoProfile ® assay/test on the Vantera® NMR analyzer platform. A conditional inference tree method (ctree R Package) was used to determine optimal KB group cut points. Associations between clinical characteristics and KB were measured with Wilcoxon rank sum test and Pearson’s Chi-squared test. Kaplan-Meier method estimated survival. Cox regression analyses were used to estimate associations between KB concentrations and mortality.

Results: Among the 1,382 HF patients with KB measurements, the median age was 78 years (IQR 68-84) and 52% were men. Median KB was 180 μM (IQR 134-308). Patients were divided into two groups with lower KB (≤471.5 μM) and higher KB (>471.5 μM). Patients with higher KB (N=210) had lower BMI, higher BNP, and were more likely to be in the New York Heart Association class III-IV; however, these patients were less likely to have hyperlipidemia, coronary disease, or diabetes mellitus (P < 0.05). Age, sex, creatinine, ejection fraction, or Meta-Analysis Global Group in Chronic HF (MAGGIC) score did not differ by KB group. Higher KB was associated with worse survival (figure). After adjustment for the MAGGIC score, higher KB was associated with increased risk of mortality (HR 1.3; 95% CI, 1.08-1.48).

Conclusions: In this community HF cohort, higher KB was associated with increased mortality, independent of the MAGGIC score.

Abstract P178: Incremental Value of Charlson Comorbidity Index Over Disease-Centric Score in Heart Failure

Introduction: Heart Failure (HF) is a syndrome of high comorbidity and mortality. The impact of comorbidities on life expectancy in HF is well studied, but guideline-recommended disease-centric risk scores such as the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) score may not fully account for the effect of comorbidities. The Charlson Comorbidity Index (Charlson) is a validated measure that quantifies the prognostic impact of comorbidities.

Objective: To examine the incremental value of Charlson over MAGGIC in predicting death in HF.

Methods: In a population-based HF cohort, we computed MAGGIC and Charlson scores using electronically linked medical records. Patients were divided based on ejection fraction (EF) into reduced [EF <50%, HFrEF] or preserved [EF ≥50%, HFpEF]. Cox regression was performed to assess the association between the risk scores and mortality. The incremental value of Charlson over MAGGIC was tested by calculating the area under the ROC [receiver operating characteristic] curves (AUCs) with 95% confidence interval (CI).

Results: We studied 1,388 patients with HF validated using the Framingham criteria (mean age, 75±13 years, 48% female, mean EF, 49±17%). The median Charlson was 7.0 (interquartile range 5.0-9.0) and was higher in HFpEF than HFrEF (7.0 [5.0-9.0] vs. 6.0 [4.0-8.3] respectively. Over a median follow-up of 13.9 years (13.3-14.2), mortality was 17.4% (95% CI, 15.4 -19.3%) and 36.8% (95% CI, 34.2-39.3%) at 1, and 3 years respectively. In regression analysis, increasing MAGGIC and Charlson were independently associated with death (HR 1.09, 95% CI 1.08-1.11 and HR,1.22, 95% CI 1.20-1.24, both p <0.001) irrespective of EF. Discrimination ability was improved by adding Charlson over MAGGIC (Table), reaching statistical significance at 3 years.

Conclusion: In this HF cohort, the Charlson index provided an added value over MAGGIC in predicting the risk of death at 3 years. These findings emphasize the importance of accounting for comorbidities in mid-term risk stratification in HF.

Improved cochlear implant electrode localization using coregistration of pre- and postoperative CT

BACKGROUND AND PURPOSE

Artifact from cochlear implant electrodes degrades image resolution on CT. Here, we describe the use of coregistered pre- and postoperative CT images to reduce metallic artifact from the electrodes to assess its position more accurately within the cochlear lumen.

METHODS

Pre- and postoperative CTs were reviewed after coregistration/overlay of both exams. Images were evaluated by two neuroradiologists for scalar location of electrodes tip (± scalar translocation), tip fold over, and angular depth of insertion.

RESULTS

Thirty-four patients were included in the final cohort. Transscalar migration was present in three (8.8%) cases (one case demonstrated tip fold over), with initial disagreement regarding transscalar migration in 1 out of 34 patients (2.9%). Agreement regarding depth of insertion was present in 31 (91.1%) cases. Five-point Likert scales were used to compare the ability to resolve the proximity of electrodes to the lateral/outer cochlear wall without and with overlay, which is a qualitative measure of artifact from the array. Likert scores showed definitive benefit of metal artifact reduction using overlayed images with an average score of 4.34.

CONCLUSION

This study demonstrates a novel technique of using fused coregistration of pre- and postoperative CTs for the purpose of artifact reduction/electrode localization. It is anticipated that this technique will permit more accurate localization of the electrodes for improvement in surgical technique and electrode array design.

Secretory leukocyte protease inhibitor and risk of heart failure in the Multi-Ethnic Study of Atherosclerosis

Circulating protease inhibitors are important regulators of inflammation that are implicated in the pathophysiology of heart failure (HF). Secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor which protects pulmonary tissues against inflammatory damage; however, its role in HF is not well understood. We sought to evaluate associations of circulating SLPI and genetically-mediated serum SLPI with incident HF and its subtypes in a multi-ethnic cohort of adults using clinical and genetic epidemiological approaches. Among 2,297 participants in the Multi-Ethnic Study of Atherosclerosis (MESA), each doubling of serum SLPI was independently associated with incident HF (HR 1.77; 95% CI 1.02-3.02; P = 0.04), particularly incident HF with preserved ejection fraction (HFpEF; HR 2.44; 95% CI 1.23-4.84; P = 0.01) but not HF with reduced ejection fraction (HFrEF; HR 0.95; 95% CI 0.36-2.46; P = 0.91). Previously reported circulating SLPI protein quantitative trait loci (pQTLs) were not associated with serum SLPI levels or incident HF among MESA participants. In conclusion, baseline serum SLPI levels, but not genetically-determined serum SLPI, were significantly associated with incident HF and HFpEF over long-term follow-up in a multi-ethnic cohort. Serum circulating SLPI may be a correlate of inflammation that sheds insight on the pathobiology of HFpEF.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Morphometric analysis of tumor microvessels for detection of hepatocellular carcinoma using contrast-free ultrasound imaging: A feasibility study

Introduction

A contrast-free ultrasound microvasculature imaging technique was evaluated in this study to determine whether extracting morphological features of the vascular networks in hepatic lesions can be beneficial in differentiating benign and malignant tumors (hepatocellular carcinoma (HCC) in particular).

Methods

A total of 29 lesions from 22 patients were included in this work. A post-processing algorithm consisting of clutter filtering, denoising, and vessel enhancement steps was implemented on ultrasound data to visualize microvessel structures. These structures were then further characterized and quantified through additional image processing. A total of nine morphological metrics were examined to compare different groups of lesions. A two-sided Wilcoxon rank sum test was used for statistical analysis.

Results

In the malignant versus benign comparison, six of the metrics manifested statistical significance. Comparing only HCC cases with the benign, only three of the metrics were significantly different. No statistically significant distinction was observed between different malignancies (HCC versus cholangiocarcinoma and metastatic adenocarcinoma) for any of the metrics.

Discussion

Obtained results suggest that designing predictive models based on such morphological characteristics on a larger sample size may prove helpful in differentiating benign from malignant liver masses.

Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics

OBJECTIVE

Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability.

METHODS

Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability.

RESULTS

Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73-0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80).

CONCLUSION

Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability.

ADVANCES IN KNOWLEDGE

Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

White blood cells and chronic rhinosinusitis: a Mendelian randomization study

BACKGROUND

Risk factors for the pathogenesis of chronic rhinosinusitis (CRS) remain largely undetermined, which is likely due to the heterogeneity of the disease. White blood cell counts have been largely unexplored as a risk factor for CRS even though different types of white blood cells are involved in the inflammatory process of CRS.

OBJECTIVE

To investigate causal associations between different types of white blood cells on risk of CRS utilizing a Mendelian randomization (MR) analysis.

METHODS

A two-sample MR analysis was performed using respective GWAS summary statistics for the exposure traits (neutrophil count, eosinophil count, basophil count, lymphocyte count, and monocyte count) and outcome trait (CRS). For the exposure traits, the European Bioinformatics Institute database of complete GWAS summary data was used. For the outcome trait, summary statistics for CRS GWAS were obtained from FinnGen. Primary analysis for MR was performed using inverse-variance weighted two-sample MR. Sensitivity analyses included weighted median, MR-Egger, and MR-PRESSO (raw and outlier-corrected).

RESULTS

Eosinophils were associated with CRS (OR = 1.55 [95% CI 1.38, 1.73]; p = 4.3E-14). Eosinophil results were similar across additional MR methods. MR results did not demonstrate significant causal relationships between neutrophils, lymphocytes, monocytes, or basophils with CRS. No significant pleiotropic bias was observed.

CONCLUSIONS

In a two-sample MR analysis, a potential causal link between blood eosinophil counts and CRS has been demonstrated. In addition, causal relationships between blood counts among other white blood cell types and CRS were not found. Further studies involving genetic variation in CRS are needed to corroborate genetic causal effects for CRS.

Nuclear Magnetic Resonance Metabolomic Profiling and Urine Chemistries in Incident Kidney Stone Formers Compared with Controls

BACKGROUND

The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls.

METHODS

High-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status.

RESULTS

Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites.

CONCLUSIONS

Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis

BACKGROUND & AIMS

Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.

METHODS

Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale.

RESULTS

Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5).

CONCLUSIONS

Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Rethinking blood eosinophil counts: Epidemiology, associated chronic diseases, and increased risks of cardiovascular disease

Background

The distribution and determinants of blood eosinophil counts in the general population are unclear. Furthermore, whether elevated blood eosinophil counts increase risk for cardiovascular disease (CVD) and other chronic diseases, other than atopic conditions, remains uncertain.

Objective

We sought to describe the distribution of eosinophil counts in the general population and determine the association of eosinophil count with prevalent chronic disease and incident CVD.

Methods

A population-based adult cohort was followed from January 1, 2006, to December 31, 2020. Electronic health record data regarding demographic characteristics, prevalent clinical characteristics, and incident CVD were extracted. Associations between blood eosinophil counts and demographic characteristics, chronic diseases, laboratory values, and risks of incident CVD were assessed using chi-square test, ANOVA, and Cox proportional hazards regression.

Results

Blood eosinophil counts increased with age, body mass index, and reported smoking and tobacco use. The prevalence of chronic obstructive pulmonary disease, hypertension, cardiac arrhythmias, hyperlipidemia, diabetes mellitus, chronic kidney disease, and cancer increased as eosinophil counts increased. Eosinophil counts were significantly associated with coronary heart disease (hazard ratio [HR], 1.44; 95% CI, 1.12-1.84) and heart failure (HR, 1.62; 95% CI, 1.30-2.01) in fully adjusted models and with stroke/transient ischemic attack (HR, 1.37; 95% CI, 1.16-1.61) and CVD death (HR, 1.49; 95% CI, 1.10-2.00) in a model adjusting for age, sex, race, and ethnicity.

Conclusions

Blood eosinophil counts differ by demographic and clinical characteristics as well as by prevalent chronic disease. Moreover, elevated eosinophil counts are associated with risk of CVD. Further prospective investigations are needed to determine the utility of eosinophil counts as a biomarker for CVD risk.

Ultrasonographic Detection and Surgical Retrieval of a Nonmetallic Twinkle Marker in Breast Cancer: Pilot Study

Purpose To evaluate the short-term safety of a nonmetallic twinkle marker and compare its conspicuity at color Doppler US with that of standard breast biopsy clips and radioactive seeds by using B-mode US in axillary lymph nodes. Materials and Methods This prospective study (November 2020-July 2021) of participants with node-positive breast cancer who completed chemotherapy involved placing a twinkle marker at the time of preoperative radioactive seed localization. A five-point scoring system (1 = easiest, 5 = most difficult) was used to rate the ease of identifying the clip, seed, and twinkle marker on postlocalization sonograms, mammograms, specimen radiographs, and gross pathologic specimens. Descriptive statistics were used. Results Eight women (mean age, 57 years ± 16 [SD]) were enrolled. The median scores for US conspicuity of each device were 3.9 (range, 3.7-5.0) for the radioactive seed, 2.4 (range, 1.0-5.0) for the clip, and 2.0 (range, 1.0-4.3) for the twinkle marker. In six of eight participants, the twinkle marker was the most identifiable at US. The seeds, clips, and twinkle markers were scored "very easy" to identify on seven of eight postlocalization mammograms. The surgeon retrieved all eight twinkle markers 1-3 days after localization. In all 16 interpretations, the seeds, clips, and twinkle markers were rated as very easy to identify on specimen radiographs. The clip was the most difficult device to identify at pathologic examination in all participants, and the twinkle marker was the easiest to identify in seven of eight participants. Conclusion This pilot study demonstrates that the safety and ease of US detection of a twinkling tissue marker may be comparable to a biopsy clip. Keywords: Ultrasonography, US-Doppler, Breast, Localization, Surgery Clinical trial registration no. NCT04674852 © RSNA, 2022.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

Ultra-high-resolution imaging of the shoulder and pelvis using photon-counting-detector CT: a feasibility study in patients

OBJECTIVE

To evaluate ultra-high-resolution (UHR) imaging of large joints using an investigational photon-counting detector (PCD) CT.

MATERIALS AND METHODS

Patients undergoing clinical shoulder or pelvis energy-integrating-detector (EID) CT exam were scanned using the UHR mode of the PCD-CT system. Axial EID-CT images (1-mm sections) and PCD-CT images (0.6-mm sections) were reconstructed using Br62/Br64 and Br76 kernels, respectively. Two musculoskeletal radiologists rated visualization of anatomic structures using a 5-point Likert scale. Wilcoxon rank-sum test was used for statistical analysis of reader scores, and paired t-test was used for comparing bone CT numbers and image noise from PCD-CT and EID-CT.

RESULTS

Thirty-two patients (17 shoulders and 15 pelvis) were prospectively recruited for this feasibility study. Mean age for shoulder exams was 67.3 ± 15.5 years (11 females) and 47.2 ± 15.8 years (11 females) for pelvis exams. The mean volume CT dose index was lower on PCD-CT compared to that on EID-CT (shoulders: 18 mGy vs. 34 mGy, pelvis: 11.6 mGy vs. 16.7 mGy). PCD-CT was rated significantly better than EID-CT (p < 0.001) for anatomic-structure visualization. Trabecular delineation in shoulders (mean score = 4.24 ± 0.73) and femoroacetabular joint visualization in the pelvis (mean score = 3.67 ± 1.03) received the highest scores. PCD-CT demonstrated significant increase in bone CT number (p < 0.001) relative to EID-CT; no significant difference in image noise was found between PCD-CT and EID-CT.

CONCLUSION

The evaluated PCD-CT system provided improved visualization of osseous structures in the shoulders and pelvises at a 31-47% lower radiation dose compared to EID-CT.

KEY POINTS

• A full field-of-view PCD-CT with 0.151 mm × 0.176 mm detector pixel size (isocenter) facilitates bilateral, high-resolution imaging of shoulders and pelvis. • The evaluated investigational PCD-CT system was rated superior by two musculoskeletal radiologists for anatomic structure visualization in shoulders and pelvises despite a 31-47% lower radiation dose compared to EID-CT. • PCD-CT demonstrated significantly higher bone CT number compared to EID-CT, while no significant difference in image noise was observed between PCD-CT and EID-CT despite a 31-47% dose reduction on PCD-CT.

Added value of mass characteristic frequency to 2-D shear wave elastography for differentiation of benign and malignant thyroid nodules

Mass characteristic frequency (f_mass) is a novel shear wave (SW) parameter that represents the ratio of the averaged minimum SW speed within the regions of interest to the largest dimension of the mass. Our study objective was to evaluate if the addition of f_mass to conventional 2-D shear wave elastography (SWE) parameters would improve the differentiation of benign from malignant thyroid nodules. Our cohort comprised 107 patients with 113 thyroid nodules, of which 67 (59%) were malignant. Two-dimensional SWE data were obtained using the Supersonic Imagine Aixplorer ultrasound system equipped with a 44- to 15-MHz15-MHz linear array transducer. A receiver operating characteristic curve was generated based on a multivariable logistic regression analysis to evaluate the ability of SWE parameters with/without f_mass and with/without clinical factors to discriminate benign from malignant thyroid nodules. The addition of f_mass to conventional SW elasticity parameters increased the area under the curve from 0.808 to 0.871 (p = 0.02). The combination of SW elasticity parameters plus f_mass plus clinical factors provided the strongest thyroid nodule malignancy probability estimate, with a sensitivity of 93.4% and specificity of 91.1% at the optimal threshold. In summary, f_mass can be a valuable addition to conventional 2-D SWE parameters.

ACE2 and TMPRSS2 SARS-CoV-2 infectivity genes: deep mutational scanning and characterization of missense variants

The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a 'landing pad' system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.