Citations

Introduction: The relationship between aortic stenosis (AS) severity, AS-related symptoms, and clinical outcomes is poorly understood. Research Question: Does a history of AS-related symptoms at the time of diagnosis affect clinical outcomes including death and AS-related hospitalization? Methods: Using a combination of natural language processing algorithms and administrative codes, we ascertained physician-assessed AS, AS severity, and AS-related symptoms (chest pain, acute coronary syndrome, syncope, presyncope, orthopnea, dyspnea, PND, and worsening heart failure) in the year prior or up to 60 days after an initial AS diagnosis among 602,821 adults with echocardiograms in a large, integrated health system from 2010-2019. We examined multivariable associations between AS severity and recent AS-related symptoms for the outcomes of death and AS-related hospitalizations using Cox models. Results: Among 40,333 patients identified with incident AS, the majority had recent AS-related symptoms (mild: 80%, mild-moderate: 77%, moderate: 77%, moderate-severe: 85%, and severe AS: 87%). Patients with recent symptoms were older (mean [SD] age 78 [10] vs 75 [11] years; p<0.01), more likely female (51% vs 47%; p<0.01), and had a higher comorbidity burden. In unadjusted analyses, symptom status was a strong discriminator of risk over a median (IQR) of 2.2 (0.8-4.3) years of follow-up ( Figure ). After multivariable adjustment, AS-related symptoms remained strongly associated with death and AS-related hospitalization across the spectrum of AS severity, with moderate symptomatic patients having risk similar to severe asymptomatic patients. Conclusions: The majority of patients diagnosed with AS have recent and/or active AS-related symptoms, which is associated with a significantly increased risk of adverse outcomes. Incorporating symptom status across the individual grades of AS may improve risk stratification beyond AS severity alone.

Abstract Background Artificial intelligence (AI) promises to become an important tool in the practice of laboratory medicine. AI programs are available online that can provide concise medical and laboratory information within seconds after a question is submitted. Methods We posed the following contemporary and relevant question for clinical chemists using an AI program: “Should labs eliminate CK-MB testing?” The results of this inquiry were critically reviewed by a cardiologist, emergency department physician, and clinical laboratorians for accuracy and appropriateness. Results An AI report answering this question was generated within 15 s. The experts felt that the information was inaccurate with the statements that CK-MB results are released earlier than cardiac troponin (cTn), and are more useful for detection of myocardial injury in patients with renal insufficiencies. The summary omitted discussion on the ongoing debate for periprocedural detection of acute myocardial infarction (MI), the perceived value for detection of reinfarction, the value in risk stratification compared to cTn, the economic justification for testing both CK-MB and cTn, and medicolegal aspects of testing when results are discordant. Conclusions At this time, AI does not appear to be ready to be used by clinical laboratories for answering important practice questions.

Abstract In 2021, the Association for Diagnostics & Laboratory Medicine (ADLM) (formerly the American Association for Clinical Chemistry [AACC]) developed a scientific study that aimed to contribute to the understanding of SARS-CoV-2 immunity during the evolving course of the pandemic. This study was led by a group of expert member volunteers and resulted in survey data from 975 individuals and blood collection from 698 of those participants. This paper describes the formulation and execution of this large-scale scientific study, encompassing best practices and insights gained throughout the endeavor.