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Background: Protein carbamylation, a post-translational protein modification primarily driven by urea, independently associates with adverse clinical outcomes in patients with CKD. Biomarkers used to quantify carbamylation burden have mainly included carbamylated albumin (C-Alb) and homocitrulline (HCit, carbamylated lysine). In this study, we aimed to compare the prognostic utility of these two markers in order to facilitate comparisons of existing studies employing either marker alone, and to inform future carbamylation studies. Methods: Both serum C-Alb and free HCit levels were assayed from the same timepoint in 1632 individuals with CKD stages 2–4 enrolled in the prospective Chronic Renal Insufficiency Cohort (CRIC) study. Adjusted Cox proportional hazard models were used to assess risks for the outcomes of death (primary) and end stage kidney disease (ESKD) using each marker. C-statistics, net reclassification improvement, and integrated discrimination improvement were used to compare the prognostic value of each marker. Results: Participant demographics included mean (SD) age 59 (11) years; 702 (43%) females; 700 (43%) white. C-Alb and HCit levels were positively correlated with one another (Pearson correlation coefficient 0.64). Higher C-Alb and HCit levels showed similar increased risk of death (e.g., the adjusted hazard ratio [HR] for death in the 4th carbamylation quartile compared to the 1st was 1.90 (95% confidence interval [CI] 1.35–2.66) for C-Alb, and 1.89 [1.27–2.81] for HCit; and on a continuous scale, the adjusted HR for death using C-Alb was 1.24 [1.11 to 1.39] per standard deviation increase, and 1.27 [1.10–1.46] using HCit). Both biomarkers also had similar HRs for ESKD. The C-statistics were similar when adding each carbamylation biomarker to base models (e.g., for mortality models, the C-statistic was 0.725 [0.707–0.743] with C-Alb and 0.725 [0.707–0.743] with HCit, both compared to a base model 0.723). Similarities were also observed for the net reclassification improvement and integrated discrimination improvement metrics. Conclusions: C-Alb and HCit had similar performance across multiple prognostic assessments. The markers appear readily comparable in CKD epidemiological studies. [ABSTRACT FROM AUTHOR]

Background: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impact of incorporating unstructured data to identify WHFE, describing age-, sex-, race and ethnicity-, and left ventricular ejection fraction (LVEF)-specific rates., Methods: Adult members of Kaiser Permanente Southern California (KPSC) with a HF diagnosis between 2014 and 2018 were followed through 2019 to identify hospitalized WHFE. The main outcome was hospitalizations with a principal or secondary HF discharge diagnosis meeting rule-based Natural Language Processing (NLP) criteria for WHFE. In comparison, we examined hospitalizations with a principal discharge diagnosis of HF. Age-, sex-, and race and ethnicity-adjusted rates per 100 person-years (PY) were calculated among age, sex, race and ethnicity (non-Hispanic (NH) Asian/Pacific Islander [API], Hispanic, NH Black, NH White) and LVEF subgroups., Results: Among 44,863 adults with HF, 10,560 (23.5%) had an NLP-defined, hospitalized WHFE. Adjusted rates (per 100 PY) of WHFE using NLP were higher compared to rates based only on HF principal discharge diagnosis codes (12.7 and 9.3, respectively), and this followed similar patterns among subgroups, with the highest rates among adults ≥75 years (16.3 and 11.2), men (13.2 and 9.7), and NH Black (16.9 and 14.3) and Hispanic adults (15.3 and 11.4), and adults with reduced LVEF (16.2 and 14.0). Using NLP disproportionately increased the perceived burden of WHFE among API and adults with mid-range and preserved LVEF., Conclusion: Rule-based NLP improved the capture of hospitalized WHFE above principal discharge diagnosis codes alone. Applying standardized consensus definitions to EHR data may improve understanding of the burden of WHFE and promote optimal care overall and in specific sociodemographic groups., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Atrial fibrillation (AF) often remains undiagnosed, and it independently raises the risk of ischemic stroke, which is largely reversible by oral anticoagulation. Although randomized trials using longer term screening approaches increase identification of AF, no studies have established that AF screening lowers stroke rates., Objectives: To address this knowledge gap, the GUARD-AF (Reducing Stroke by Screening for Undiagnosed Atrial Fibrillation in Elderly Individuals) trial screened participants in primary care practices using a 14-day continuous electrocardiographic monitor to determine whether screening for AF coupled with physician/patient decision-making to use oral anticoagulation reduces stroke and provides a net clinical benefit compared with usual care., Methods: GUARD-AF was a prospective, parallel-group, randomized controlled trial designed to test whether screening for AF in people aged ≥70 years using a 14-day single-lead continuous electrocardiographic patch monitor could identify patients with undiagnosed AF and reduce stroke. Participants were randomized 1:1 to screening or usual care. The primary efficacy and safety outcomes were hospitalization due to all-cause stroke and bleeding, respectively. Analyses used the intention-to-treat population., Results: Enrollment began on December 17, 2019, and involved 149 primary care sites across the United States. The COVID-19 pandemic led to premature termination of enrollment, with 11,905 participants in the intention-to-treat population. Median follow-up was 15.3 months (Q1-Q3: 13.8-17.6 months). Median age was 75 years (Q1-Q3: 72-79 years), and 56.6% were female. The risk of stroke in the screening group was 0.7% vs 0.6% in the usual care group (HR: 1.10; 95% CI: 0.69-1.75). The risk of bleeding was 1.0% in the screening group vs 1.1% in the usual care group (HR: 0.87; 95% CI: 0.60-1.26). Diagnosis of AF was 5% in the screening group and 3.3% in the usual care group, and initiation of oral anticoagulation after randomization was 4.2% and 2.8%, respectively., Conclusions: In this trial, there was no evidence that screening for AF using a 14-day continuous electrocardiographic monitor in people ≥70 years of age seen in primary care practice reduces stroke hospitalizations. Event rates were low, however, and the trial did not enroll the planned sample size.(Reducing Stroke by Screening for Undiagnosed Atrial Fibrillation in Elderly Individuals [GUARD-AF]; NCT04126486)., Competing Interests: Funding Support and Author Disclosures The Bristol Myers Squibb/Pfizer Alliance funded the GUARD-AF trial but was not involved in data collection, analysis, or interpretation; manuscript preparation; or decision to publish. Dr Lopes has received research grants or contracts from Amgen, Bristol Myers Squibb, GlaxoSmithKline, Medtronic, Pfizer, and Sanofi; has received funding for educational activities or lectures from Pfizer, Daiichi-Sankyo, and Novo Nordisk; and has received funding for consulting or other services from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, and Novo Nordisk. Dr Atlas has received sponsored research support from Bristol Myers Squibb/Pfizer and the American Heart Association (18SFRN34250007); and has consulted for Boehringer Ingelheim, Bristol Myers Squibb, Fitbit, Pfizer, and Premier. Dr Go has received a research grant from Bristol Myers Squibb/Pfizer Alliance. Dr Lubitz is an employee of Novartis. Dr McManus has received grants or research support from Apple, Fitbit, Boehringer Ingelheim, Pfizer, Bristol Myers Squibb, and Flexcon; and has received consulting fees from Fitbit, NAMSA, Avania, and Heart Rhythm Society. Dr Rothberg is a consultant for Blue Cross Blue Shield Association. Prof Crosson is a former employee of iRhythm Technologies,. Dr Aronson and Mr Patlakh are employees of Bristol Myers Squibb. Ms. Mills is a former employee of Bristol Myers Squibb, Inc. Dr O’Brien has received institutional research funding from Pfizer. Dr Singer has received research support from Bristol Myers Squibb; and has consulted for the Bristol Myers Squibb, Pfizer, Medtronic, and Fitbit (Google). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs)., Methods and Results: We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n = 22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively., Conclusion: When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)