Why the study?
Lipid-lowering therapy remains underused in ASCVD secondary prevention despite guideline LDL-C goals. This study evaluated whether large language models can identify treat-to-target strategy adoption and extract reasons for therapy nonescalation from clinical notes when LDL-C is above goal.
Does a large language model accurately identify treat-to-target strategy use and extract reasons for lipid-lowering therapy nonescalation from clinical notes in patients with ASCVD and above-goal LDL-C?
Cohort (n=13,810)
Yes
Does a large language model accurately identify treat-to-target strategy use and extract reasons for lipid-lowering therapy nonescalation from clinical notes in patients with ASCVD and above-goal LDL-C?
Using a large language model, this study found that nearly 9 in 10 ASCVD clinical encounters with above-goal LDL-C did not result in lipid-lowering therapy escalation, primarily due to adverse effects, patient preferences, and therapeutic inertia.
Answers are grounded in this paper and cite their sources.
High nonescalation of lipid-lowering therapy persists in ASCVD; leaves open whether targeted strategies addressing adverse effects improve guideline adherence.
Importance Despite guideline recommendations for low-density lipoprotein cholesterol (LDL-C) goals in secondary prevention of atherosclerotic cardiovascular disease (ASCVD), lipid-lowering therapy (LLT) remains underused. Identifying treat-to-target strategy adoption and reasons for LLT nonescalation are critical to close guideline-discordant care gaps. Objective To determine whether large language models (LLMs) can identify a treat-to-target strategy use and extract reasons for LLT nonescalation from clinical notes when LDL-C concentration is above goal. Design, Setting, and Participants This was a retrospective cohort study of patients from January 1, 2019, through May 15, 2025. Notes from most recent clinical encounters were retrieved and split into development, test, and inference sets. The setting was a large, integrated health system in Northern California. Adults aged 18 to 90 years with 2 or more clinician-billed encounters for ASCVD and with LDL-C level greater than or equal to 70 mg/dL at the most recent encounter were included in note-based analyses. Exposures Documented LDL-C targets and LLT management decisions. Main Outcomes and Measures Model performance was evaluated on LDL-C target documentation, LDL-C target value, LLT adjustment, and reasons for nonescalation against clinical annotations. Outcome prevalence was assessed on the inference set. Results A total of 28 113 unique patients with ASCVD-related encounters were identified, of whom 13 810 (49.1%; mean [SD] age, 70.9 [11.4] years; 7904 male [57.2%]) had above-goal LDL-C level. Model-annotator and interannotator reliability were not statistically different across outcomes, and micro-weighted F1 scores ranged from 0.80 to 0.98. Of the 13 394 patients with above-goal LDL-C level and 13 354 relevant notes in the inference set, a treat-to-target strategy was identified in 3756 (28.1%) of all relevant notes, most commonly targeting an LDL-C level less than 70 mg/dL (2280 [17.1%]). LLT was not escalated in 11 842 of all relevant notes (88.7%), with 4788 (40.4%) containing at least 1 LLT nonescalation reason, most often due to patient-reported adverse effects (1683 [12.6%]), LDL-C level close to goal (922 [6.9%]), patient preference to avoid LLT (914 [6.8%]), preference for lifestyle changes (811 [6.1%]), and deferral to repeat laboratory tests (617 [4.6%]). Conclusions and Relevance Results of this study show that after using a state-of-the art LLM, nearly 9 in 10 ASCVD encounters with above-goal LDL-C level did not have patient LLT escalated, most often because of adverse effects, patient preference, preference for lifestyle changes, therapeutic inertia (eg, repeat laboratory tests), and near-goal LDL-C level. These findings highlight an avenue for personalizing system-level digital health interventions to help discover and minimize guideline-discordant care gaps in secondary prevention of ASCVD.
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Somani et al. (2026) conducted a cohort in Atherosclerotic cardiovascular disease (ASCVD) with LDL-C >= 70 mg/dL (n=13,810). LLM extraction of documented LDL-C targets and LLT management decisions vs. Clinical annotations was evaluated on LLT nonescalation and treat-to-target strategy identification in clinical notes.
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