Alhalabi, Lubna

Introduction: Lp(a) is a causal risk factor for ASCVD that is often elevated in patients with personal or family history (FHx) of ASCVD. The 2019 ESC guidelines recommend universal Lp(a) testing, while the 2019 AHA/ACC guidelines include Lp(a) as a ‘risk enhancer’. We used electronic health records (EHR) to assess utilization of Lp(a) testing in patients with ASCVD, premature ASCVD, and FHx of ASCVD at Mayo Clinic sites in Rochester MN, Phoenix AZ and Jacksonville FL and the Mayo Clinic Health System (MCHS) from 1999-2021. Methods: We ascertained Lp(a) testing using LOINC codes and ASCVD status using validated ICD/CPT-based algorithms. ASCVD included coronary heart disease (CHD), cerebrovascular disease (CVD) (ischemic stroke and/or carotid artery stenosis), peripheral artery disease, and abdominal aortic aneurysm. Premature ASCVD was defined as occurring in males < 55 years and females < 65 years. We ascertained FHx using EHR survey data from 2017-2021. We estimated the proportion of patients with ASCVD, premature ASCVD, or FHx of ASCVD who underwent Lp(a) testing. Results: Between 1999-2021, we identified 310,760 patients with at least 1 ASCVD subtype of whom 66,710 had premature disease. FHx data was available starting 2017. For the timeframe 2017-2021, we identified 267,177 patients with FHx of ASCVD and 90,269 ASCVD patients. As summarized in Table 1, Lp(a) was tested only in a small proportion of patients; 7% of ASCVD patients, 11% of premature ASCVD patients, and 6% of patients with FHx of ASCVD. Premature CHD patients had the highest testing proportion (17%). Conclusions: Our results demonstrate underutilization of Lp(a) testing in patients with personal or FHx of ASCVD. Lp(a) is one of the mediators of “ASCVD residual risk” in patients on statins, and with new Lp(a)-lowering medications in phase III clinical trials, patient and provider education will be needed to increase awareness of Lp(a) as a modulator of ASCVD risk.

Introduction: Hospitalization for COVID-19 is often associated with myocardial injury (elevated cardiac troponin [cTn]). In the initial pandemic waves, patients with myocardial injury had increased likelihood of cardiovascular complications and inpatient mortality; however, the differences in impact of distinct subtypes of myocardial injury on mortality is unclear. In addition, little is known about how these outcomes compare with the subsequent, milder, Omicron wave. Methods: Utilizing Mayo Clinic enterprise patients we developed algorithms based on diagnostic/procedural codes, cTn levels, and temporal relationships to positive COVID-19 PCR tests, to determine 5 common subtypes of myocardial injury: stress cardiomyopathy, myocarditis, pericarditis, pulmonary embolism, and myocardial infarction. We examined hospitalized adults with COVID-19; those with elevated cTn designated as cases and others as controls. We fitted Cox models using the following covariates: spline transformed age, sex, race, ethnicity, index date and clinical comorbidities. Patients were analyzed based on admission dates coinciding with either the Alpha-Delta or Omicron waves. Results: Across both wave cohorts, hospitalized patients with acute COVID-19 complicated by myocardial injury had an increased risk of mortality compared to those without ( Figure ). Patients with one of the 5 subtypes examined tended to have higher risk than those in the “other myocardial injury” category. Admission during the Omicron wave, compared to the Alpha-Delta waves, tended to have higher mortality risk in all subtypes except myocarditis. Conclusions: Our algorithms successfully identified subgroups of myocardial injury in patients hospitalized with acute COVID-19 leading to stratification of mortality risk. Surprisingly, among hospitalized patients during the Omicron wave, myocardial injury of most subtypes was tended to have increased risk of mortality compared to the Alpha-Delta waves.

Background: The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict the effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate the implementation of MES, and validated it using ClinVar and UK-Biobank (UKBB) data. Method: We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid-lowering medication use. Results: MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. Conclusion: MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.

Introduction: We aimed to identify and characterize carotid artery stenosis (CAS), defined as ≥40% stenosis in the internal carotid artery (ICA) or the common carotid artery (CCA), among participants of the Mayo Vascular Disease Biorepository (VDB, n = 11,814). Hypothesis: A string-based text processing (TP) tool coded in R can analyze radiology text reports to ascertain and characterize CAS. Methods: After importing a radiology report into R, it was segmented for analysis and each segment was tested for 22 unique features. The TP tool was able to i) identify stenosis in the ICA or the CCA while excluding stenosis of vertebral arteries, subclavian arteries, or external carotid arteries, ii) recognize both quantitative and qualitative descriptions of CAS, iii) interpret numerical expressions by considering the surrounding text, iv) detect negations, and v) ascertain previous revascularization procedures if mentioned in the text. We manually ascertained CAS and characterized it in 800 randomly selected radiology reports to assess the performance of the TP tool in terms of precision, recall, and F-score. Results: We retrieved 21,651 imaging reports for 6,342 unique patients including 20,461 ultrasonography reports, 812 magnetic resonance imaging reports, 357 computed tomography scan reports, and 21interventional radiology reports. The performance of TP is outlined in Table 1 . Conclusion: We demonstrate that a string-based TP tool coded in R is efficient and accurate in identifying and characterizing CAS from carotid radiology reports. The tool does not require natural language processing and could be used in a wide range of settings for genetic and biomarker studies.

The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of pathogenicity of variants in LDLR and FBN1 with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 96% of LDLR variants and 98% of FBN1 variants. In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify splice variants in a rapid automated fashion.

The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.

Background Hypertriglyceridemia is associated with increased risk of coronary heart disease but the association is often attributed to concomitant metabolic abnormalities. We investigated the epidemiology of primary isolated hypertriglyceridemia (PIH) and associated cardiovascular risk in a population‐based setting. Methods and Results We identified adults with at least one triglyceride level ≥500 mg/dL between 1998 and 2015 in Olmsted County, Minnesota. We also identified age‐ and sex‐matched controls with triglyceride levels <150 mg/dL. There were 3329 individuals with elevated triglyceride levels; after excluding those with concomitant hypercholesterolemia, a secondary cause of high triglycerides, age <18 years or an incomplete record, 517 patients (49.4±14.0 years, 72.0% men) had PIH (triglyceride 627.6±183.6 mg/dL). The age‐ and sex‐adjusted prevalence of PIH in adults was 0.80% (0.72–0.87); the diagnosis was recorded in 60%, 46% were on a lipid‐lowering medication for primary prevention and a triglyceride level <150 mg/dL was achieved in 24.1%. The association of PIH with coronary heart disease was attenuated but remained significant after adjustment for demographic, socioeconomic, and conventional cardiovascular risk factors (hazard ratio [HR], 1.53; 95% CI, 1.06‐2.20; P = 0.022). There was no statistically significant association between PIH and cerebrovascular disease (HR, 1.06; 95% CI, 0.65‐1.73, P = 0.813), peripheral artery disease (HR, 1.27; 95% CI, 0.43‐3.75; P = 0.668), or the composite end point of all 3 (HR, 1.28; 95% CI, 0.92‐1.80; P =0.148) in adjusted models. Conclusions PIH was associated with incident coronary heart disease events (although there was attenuation after adjustment for conventional risk factors), supporting a causal role for triglycerides in coronary heart disease. The condition is relatively prevalent but awareness and control are low.