Atorvastatin for Anthracycline-Associated Cardiac Dysfunction: The STOP-CA Randomized Clinical Trial.

Importance: Anthracyclines treat a broad range of cancers. Basic and retrospective clinical data have suggested that use of atorvastatin may be associated with a reduction in cardiac dysfunction due to anthracycline use. Objective: To test whether atorvastatin is associated with a reduction in the proportion of patients with lymphoma receiving anthracyclines who develop cardiac dysfunction. Design, Setting, and Participants: Double-blind randomized clinical trial conducted at 9 academic medical centers in the US and Canada among 300 patients with lymphoma who were scheduled to receive anthracycline-based chemotherapy. Enrollment occurred between January 25, 2017, and September 10, 2021, with final follow-up on October 10, 2022. Interventions: Participants were randomized to receive atorvastatin, 40 mg/d (n = 150), or placebo (n = 150) for 12 months. Main Outcomes and Measures: The primary outcome was the proportion of participants with an absolute decline in left ventricular ejection fraction (LVEF) of ≥10% from prior to chemotherapy to a final value of <55% over 12 months. A secondary outcome was the proportion of participants with an absolute decline in LVEF of ≥5% from prior to chemotherapy to a final value of <55% over 12 months. Results: Of the 300 participants randomized (mean age, 50 [SD, 17] years; 142 women [47%]), 286 (95%) completed the trial. Among the entire cohort, the baseline mean LVEF was 63% (SD, 4.6%) and the follow-up LVEF was 58% (SD, 5.7%). Study drug adherence was noted in 91% of participants. At 12-month follow-up, 46 (15%) had a decline in LVEF of 10% or greater from prior to chemotherapy to a final value of less than 55%. The incidence of the primary end point was 9% (13/150) in the atorvastatin group and 22% (33/150) in the placebo group (P = .002). The odds of a 10% or greater decline in LVEF to a final value of less than 55% after anthracycline treatment was almost 3 times greater for participants randomized to placebo compared with those randomized to atorvastatin (odds ratio, 2.9; 95% CI, 1.4-6.4). Compared with placebo, atorvastatin also reduced the incidence of the secondary end point (13% vs 29%; P = .001). There were 13 adjudicated heart failure events (4%) over 24 months of follow-up. There was no difference in the rates of incident heart failure between study groups (3% with atorvastatin, 6% with placebo; P = .26). The number of serious related adverse events was low and similar between groups. Conclusions and Relevance: Among patients with lymphoma treated with anthracycline-based chemotherapy, atorvastatin reduced the incidence of cardiac dysfunction. This finding may support the use of atorvastatin in patients with lymphoma at high risk of cardiac dysfunction due to anthracycline use. Trial Registration: ClinicalTrials.gov Identifier: NCT02943590.

Enhancing Precision in Detecting Severe Immune-Related Adverse Events: Comparative Analysis of Large Language Models and International Classification of Disease Codes in Patient Records.

PURPOSE: Current approaches to accurately identify immune-related adverse events (irAEs) in large retrospective studies are limited. Large language models (LLMs) offer a potential solution to this challenge, given their high performance in natural language comprehension tasks. Therefore, we investigated the use of an LLM to identify irAEs among hospitalized patients, comparing its performance with manual adjudication and International Classification of Disease (ICD) codes. METHODS: Hospital admissions of patients receiving immune checkpoint inhibitor (ICI) therapy at a single institution from February 5, 2011, to September 5, 2023, were individually reviewed and adjudicated for the presence of irAEs. ICD codes and an LLM with retrieval-augmented generation were applied to detect frequent irAEs (ICI-induced colitis, hepatitis, and pneumonitis) and the most fatal irAE (ICI-myocarditis) from electronic health records. The performance between ICD codes and LLM was compared via sensitivity and specificity with an α = .05, relative to the gold standard of manual adjudication. External validation was performed using a data set of hospital admissions from June 1, 2018, to May 31, 2019, from a second institution. RESULTS: Of the 7,555 admissions for patients on ICI therapy in the initial cohort, 2.0% were adjudicated to be due to ICI-colitis, 1.1% ICI-hepatitis, 0.7% ICI-pneumonitis, and 0.8% ICI-myocarditis. The LLM demonstrated higher sensitivity than ICD codes (94.7% v 68.7%), achieving significance for ICI-hepatitis (P < .001), myocarditis (P < .001), and pneumonitis (P = .003) while yielding similar specificities (93.7% v 92.4%). The LLM spent an average of 9.53 seconds/chart in comparison with an estimated 15 minutes for adjudication. In the validation cohort (N = 1,270), the mean LLM sensitivity and specificity were 98.1% and 95.7%, respectively. CONCLUSION: LLMs are a useful tool for the detection of irAEs, outperforming ICD codes in sensitivity and adjudication in efficiency.

Digital phenotyping: An equal opportunity approach to reducing disparities in Alzheimer's disease and related dementia research.

A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations. In this perspective, we describe research efforts and challenges of cohort studies from three regions of the world: Central America, East Africa, and East Asia. These cohorts are engaging with the Davos Alzheimer's Collaborative (DAC), a global partnership that brings together cohorts from around the world to advance understanding of AD. Each cohort is poised to leverage the widespread use of mobile devices to integrate digital phenotyping into current methodologies and mitigate the lack of representativeness in AD research of racial and ethnic minorities across the globe. In addition to methods that these three cohorts are already using, DAC has developed a digital phenotyping protocol that can collect ADRD-related data remotely via smartphone and/or in clinic via a tablet to generate a common data elements digital dataset that can be harmonized with additional clinical and molecular data being collected at each cohort site and when combined across cohorts and made accessible can provide a global data resource that is more racially/ethnically represented of the world population.