Lipid Testing Trends Before and After Hospitalization for Myocardial Infarction Among Adults in the United States, 2008–2019

Background: Routine monitoring of low-density lipoprotein cholesterol (LDL-C) identifies patients who may benefit from modifying lipid-lowering therapies (LLT). However, the extent to which LDL-C testing is occurring in clinical practice is unclear, specifically among patients hospitalized for a myocardial infarction (MI). Methods: Using US commercial claims data, we identified patients with an incident MI hospitalization between 01/01/2008-03/31/2019. LDL-C testing was assessed in the year before admission (pre-MI) and the year after discharge (post-MI). Changes in LDL-C testing were evaluated using a Poisson model fit to pre-MI rates and extrapolated to the post-MI period. We predicted LDL-C testing rates if no MI had occurred (ie, based on pre-MI trends) and estimated rate differences and ratios (contrasting observed vs predicted rates). Results: Overall, 389,367 patients were hospitalized for their first MI during the study period. In the month following discharge, 9% received LDL-C testing, increasing to 27% at 3 months and 52% at 12 months. Mean rates (tests per 1000 patients per month) in the pre-and post-MI periods were 51.9 (95% CI: 51.7, 52.1) and 84.4 (95% CI: 84.1, 84.6), respectively. Over 12 months post-MI, observed rates were higher than predicted rates; the maximum rate difference was 66 tests per 1000 patients in month 2 (rate ratio 2.2), stabilizing at a difference of 15–20 (ratio 1.2–1.3) for months 6–12. Conclusion: Although LDL-C testing increased following MI hospitalization, rates remained lower than recommended by clinical guidelines. This highlights a potential gap in care, where increased LDL-C testing after MI may provide opportunities for LLT modification and decrease risk of subsequent cardiovascular events

Lipid testing trends in the us before and after the release of the 2013 cholesterol treatment guidelines

Background: The 2013 ACC/AHA cholesterol treatment guidelines removed the recom-mendation to treat adults at risk of cardiovascular disease to goal levels of low-density lipoprotein cholesterol (LDL-C). We anticipated that the frequency of LDL-C testing in clinical practice would decline as a result. To test this hypothesis, we evaluated the frequency of LDL-C testing before and after the guideline release. Methods: We used the MarketScan® Commercial and Medicare Supplemental claims data (1/1/2007–12/31/2016) to identify four cohorts: 1) statin initiators (any intensity), 2) high-intensity statin initiators, 3) ezetimibe initiators, and 4) patients at very high cardiovascular risk (≥2 hospitalizations for myocardial infarction or ischemic stroke, with prevalent statin use). Rates of LDL-C testing by calendar year quarter were estimated for each cohort. To estimate rates in the absence of a guideline change, we fit a time-series model to the pre-guideline rates and extrapolated to the post-guideline period, adjusting for covariates, seasonality, and time trend. Results: Pre-and post-guideline rates (LDL-C tests per 1,000 persons per quarter) were 248 and 235, respectively, for 3.9 million statin initiators; 263 and 246 for 1.3 million high-intensity statin initiators; 277 and 261 for 323,544 ezetimibe initiators; and 180 and 158 for 42,108 very high-risk patients. For all cohorts, observed post-guideline rates were similar to model-predicted rates. On average, the difference between observed and predicted rates was 8.5 for patients initiating any statin; 2.6 for patients initiating a high-intensity statin; 11.4 for patients initiating ezetimibe, and −0.5 for high-risk patients. Conclusion: We observed no discernible impact of the release of the 2013 ACC/AHA guidelines on LDL-C testing rates. Rather, there was a gradual decline in testing rates starting prior to the guideline change and continuing throughout the study period. Our findings suggest that the guidelines had little to no impact on use of LDL-C testing

Quantitative considerations in medium energy ion scattering depth profiling analysis of nanolayers

The high depth resolution capability of medium energy ion scattering (MEIS) is becoming increasingly relevant to the characterisation of nanolayers in e.g. microelectronics. In this paper we examine the attainable quantitative accuracy of MEIS depth profiling. Transparent but reliable analytical calculations are used to illustrate what can ultimately be achieved for dilute impurities in a silicon matrix and the significant element-dependence of the depth scale, for instance, is illustrated this way. Furthermore, the signal intensity-to-concentration conversion and its dependence on the depth of scattering is addressed. Notably, deviations from the Rutherford scattering cross section due to screening effects resulting in a non-coulombic interaction potential and the reduction of the yield owing to neutralization of the exiting, backscattered H+ and He+ projectiles are evaluated. The former mainly affects the scattering off heavy target atoms while the latter is most severe for scattering off light target atoms and can be less accurately predicted. However, a pragmatic approach employing an extensive data set of measured ion fractions for both H+ and He+ ions scattered off a range of surfaces, allows its parameterization. This has enabled the combination of both effects, which provides essential information regarding the yield dependence both on the projectile energy and the mass of the scattering atom. Although, absolute quantification, especially when using He+, may not always be achievable, relative quantification in which the sum of all species in a layer add up to 100%, is generally possible. This conclusion is supported by the provision of some examples of MEIS derived depth profiles of nanolayers. Finally, the relative benefits of either using H+ or He+ ions are briefly considered

Contourite depositional system after the exit of a strait: Case study from the late Miocene South Rifian Corridor, Morocco

Idealized facies of bottom current deposits (contourites) have been established for fine-grained contourite drifts in modern deep-marine sedimentary environments. Their equivalent facies in the ancient record however are only scarcely recognized due to the weathered nature of most fine-grained deposits in outcrop. Facies related to the erosional elements (i.e. contourite channels) of contourite depositional systems have not yet been properly established and related deposits in outcrop appear non-existent. To better understand the sedimentary facies and facies sequences of contourites, the upper Miocene contourite depositional systems of the South Rifian Corridor (Morocco) is investigated. This contourite depositional system formed by the dense palaeo-Mediterranean Outflow Water. Foraminifera assemblages were used for age-constraints (7.51 to 7.35 Ma) and to determine the continental slope depositional domains. Nine sedimentary facies have been recognized based on lithology, grain-size, sedimentary structures and biogenic structures. These facies were subsequently grouped into five facies associations related to the main interpreted depositional processes (hemipelagic settling, contour currents and gravity flows). The vertical sedimentary facies succession records the tectonically induced, southward migration of the contourite depositional systems and the intermittent behaviour of the palaeo-Mediterranean Outflow Water, which is mainly driven by precession and millennial-scale climate variations. Tides substantially modulated the palaeo-Mediterranean Outflow Water on a sub-annual scale. This work shows exceptional examples of muddy and sandy contourite deposits in outcrop by which a facies distribution model from the proximal continental slope, the contourite channel to its adjacent contourite drift, is proposed. This model serves as a reference for contourite recognition both in modern environments and the ancient record. Furthermore, by establishing the hydrodynamics of overflow behaviour a framework is provided that improves process-based interpretation of deep-water bottom current deposits

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and ‘tumor spread through air spaces’ were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk

Genomic–transcriptomic evolution in lung cancer and metastasis

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic–transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary–metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis

Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy