Optimizing low-density lipoprotein cholesterol (LDL-C) management – a US physician survey of barriers and burdens
Bibliographic record
Abstract
Background and Aims: Improving care of patients with hyperlipidemia requires an understanding of the barriers physicians perceive in prescribing low-density lipoprotein cholesterol (LDL-C)-lowering therapies. This study explores physicians' perceptions of time and resource burdens, identify perceived patient adherence barriers, and examine factors influencing physicians' decision-making in LDL-C management. Methods: This is a non-interventional, cross-sectional, online survey of US-based primary care practitioners (PCP) and cardiologists who recommended or provided lipid-lowering therapy (LLT) to ≥50 adults per month, practiced for ≥2 years, and completed the survey in English. The survey comprised multiple-choice, constant sum, and numerical questions about physician decision-making, patient management, and perceptions of patient attitudes/behaviors regarding LDL-C management. Descriptive univariate analyses were conducted. Results: 200 PCPs and 200 cardiologists completed the survey. Most physicians reported prescribing lipid-lowering therapy (LLT) and that patients declined injectable proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i). They attributed this refusal to cost/insurance, fear/discomfort taking injections, and a preference for oral therapies. Physicians viewed patients with a history of ASCVD, with LLT experience, and those with greater understanding of ASCVD risk to have higher LLT adherence compared to those without. Most physicians spent a median of 10 min in shared decision-making conversations, regardless of therapies they prescribed. They reported needing longer to instruct patients during adherence counseling for PCSK9is than for oral therapies. Conclusions: Our findings suggest patient, clinician, and system barriers may all hinder LDL-C management and adherence. A greater understanding of the association between perceived barriers and real-world behaviors will help optimize lipid management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".