Citations

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Background: In the emergency setting, many diagnostic pathways incorporate change in high-sensitivity cardiac troponin (hs-cTn) concentrations (i.e., the delta) to classify patients as low-risk (rule-out) or high-risk (rule-in) for possible myocardial infarction (MI). However, the impact of analytical variation on the delta for correct classification is unknown, especially at concentrations below and around the 99th percentile. Our objective was to assess the impact of delta variation for correct risk classification across the European Society of Cardiology (ESC 0/1 h and 0/2 h), the High-STEACS, and the common change criteria (3C) pathways. Methods: A yearlong accuracy study for hs-cTnT was performed where laboratories across Canada tested three patient-based samples (level 1 target value = 6 ng/L, level 2 target value = 9 ng/L, level 3 target value = 12 ng/L) monthly across 41 different analyzers. The assigned low-delta between levels 1 and 2 was 3 ng/L (i.e., 9 − 6 = 3 ng/L) and the assigned high-delta between levels 1 and 3 was 6 ng/L (i.e., 12 − 6 = 6 ng/L). The low- and high-deltas for each analyzer were determined monthly from the measured values, with the difference calculated from the assigned deltas. The obtained deltas were then assessed via the different pathways on correct classification (i.e., percent correct with 95% confidence intervals, CI) and using non-parametric analyses. Results: The median (interquartile range) difference between the measured versus assigned low-delta (n = 436) and high-delta (n = 439) was −1 ng/L (−1 to 0). The correct classification differed among the pathways. The ESC 0/1 h pathway yielded the lowest percentage of correct classification at 35.3% (95% CI: 30.8 to 40.0) for the low-delta and 90.0% (95% CI: 86.8 to 92.6) for the high-delta. The 3C and ESC 0/2 h pathways yielded higher and equivalent estimates on correct classification: 95.2% (95% CI: 92.7 to 97.0) for the low-delta and 98.2% (95% CI: 96.4 to 99.2) for the high-delta. The High-STEACS pathway yielded 99.5% (95% CI: 98.4 to 99.9) of correct classifications for the high-delta but only 36.2% (95% CI: 31.7 to 40.9) for the low-delta. Conclusions: Analytical variation will impact risk classification for MI when using hs-cTn deltas alone per the pathways. The 3C and ESC 0/2 h pathways have <5% misclassification when using deltas for hs-cTnT in this dataset. Additional studies with different hs-cTnI assays at concentrations below and near the 99th percentile are warranted to confirm these findings.

Abstract Performing bedside procedures requires knowledge, reasoning, physical adeptness, and self-confidence; however, no consensus on a specific, comprehensive strategy for bedside procedure training and implementation is available. Bedside procedure training and credentialing processes across large institutions may vary among departments and specialties, leading to variable standards, creating an environment that lacks consistent accountability, and making quality improvement difficult. In this Scholarly Perspective, the authors describe a standardized bedside procedure training and certification process for graduate medical education with a common, institution-wide educational framework for teaching and assessing the following 7 important bedside procedures: paracentesis; thoracentesis; central venous catheterization; arterial catheterization; bladder catheterization or Foley catheterization; lumbar puncture; and nasogastric, orogastric, and nasoenteric tube placement. The proposed framework is a 4-stage process that includes 1 preparatory learning stage with simulation practice for knowledge acquisition and 3 clinical stages to guide learners from low-risk to high-risk practice and from high to low supervision. The pilot rollout took place at Henry Ford Hospital from December 2020 to July 2021 for 165 residents in the emergency medicine and/or internal medicine residency programs. The program was fully implemented institution-wide in July 2021. Assessment strategies encompass critical action checklists to confirm procedural understanding and a global rating scale to measure performance quality. A major aim of the bedside procedure training and certification was to standardize assessments so that physician trainers from multiple specialties could train, assess, and supervise any participating trainee, regardless of discipline. The authors list considerations revealed from the pilot rollout regarding electronic tracking systems and several benefits and implementation challenges to establishing institution-wide standards. The proposed framework was assembled by a multidisciplinary physician task force and will assist other institutions in adopting best approaches for training physicians in performing these critically important and difficult-to-perform procedures.

AbstractThe intricate interplay of anemia and iron overload under the pathophysiological umbrella of ineffective erythropoiesis in non-transfusion-dependent β-thalassemia (NTDT) results in a complex variety of clinical phenotypes that are challenging to diagnose and manage. In this article, we use a clinical framework rooted in pathophysiology to present 4 common scenarios of patients with NTDT. Starting from practical considerations in the diagnosis of NTDT, we delineate our strategy for the longitudinal care of patients who exhibit different constellations of symptoms and complications. We highlight the use of transfusion therapy and novel agents, such as luspatercept, in the patient with anemia-related complications. We also describe our approach to chelation therapy in the patient with iron overload. Although tackling every specific complication of NTDT is beyond the scope of this article, we touch on the management of the various morbidities and multisystem manifestations of the disease.

Abstract Background Vitamin D supplementation is common practice for neonates and infants due to limited stores of vitamin D at birth. Although not commonly encountered, vitamin D toxicity can occur due to over-supplementation. However, toxic concentrations are often not included in method validation experiments, and assays often are not validated in the neonatal population. Methods We compared serial 25 hydroxy vitamin D [25(OH)D] measurements in pre-term neonates receiving 25(OH)D supplementation and identified 12 patients wherein concentrations of 25(OH)D were above 50 ng/mL (125 nM) that required additional investigations as the 25(OH)D results did not match the clinical picture. Available samples were compared across 4 immunoassay platforms (LIAISON XL, Roche Cobas e602, Abbott Alinity i, and Siemens Centaur XP) and LC–MS/MS. Results Concentrations of 25(OH)D observed on one individual immunoassay platform (LIAISON XL) fluctuated substantially between subsequent blood draws in select neonates with elevated concentrations. Serum samples from these patients showed variable agreement between LC–MS/MS and other immunoassay platforms. These fluctuations were not explained by the presence of 3-epimer-25(OH)D or 24,25(OH)2D. Conclusions Although we were unable to identify a cause for the variable elevated results, our findings suggest that neonatal 25(OH)D measurements alone should not be used for assessment of nutritional monitoring, and that clinical correlation and other laboratory parameters including ionized calcium should be considered.

AbstractAn increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.