Adherence to clinical practice guidelines \nfor using invasive coronary angiography in chronic coronary artery disease
Bibliographic record
Abstract
Clinical practice guidelines (CPG) are proposed as a strategy to reduce practice variation and aim to support the application of evidence-based recommendations in clinical practice. Guideline adherence is understood as the application of CPG in the clinical practice. This cumulative dissertation evaluates the guideline adherence in routine care based on the example of the decision-making process for using invasive coronary angiography (CA) in patients with chronic coronary artery disease (CAD) in Germany. For this purpose, four dissertation projects (DP) are conducted. \n \nDP 1 identifies methods to assess the guideline adherence of health care providers in the care of chronic CA by a scoping review. In DP 2 a prospective observational study (ENLIGHT-KHK) generates evidence on the guideline adherence in patients undergoing a decision-making process for receiving a CA to confirm or exclude an obstructive SCAD. DP 3 conducts a cost-effectiveness analysis based on the ENLIGHT-KHK-trial. The analysis examines the degree of guideline adherence and the corresponding clinical and economic outcomes. Major adverse cardiac events and costs of the complete guideline-adherent use (according to the German National Disease Management Guideline) are compared with those of in the clinical practice observed real-world CA-use. DP 4 examines the degree of guideline adherence according to the German and the European CPG using ENLIGHT-KHK data in patients who were referred to elective CA. \n \nDP 1 identifies similarities and differences in the methods used for assessing guideline adherence. Main steps of assessing guideline adherence were detected, and retrospectively collected secondary data were mostly used as a data source. Differences were detected in the definition of guideline adherence, the evidence underlying the CPG-recommendations, the evaluation and results of guideline adherence. DP 3 shows that in clinical practice ca. 26% of CAs were performed according to the German CPG-recommendations. To improve guideline adherence, a reduction of the amount of CAs in patients with SCAD would be necessary. A guideline-adherent CA-use is less expensive and associated with a slightly lower MACE compared with the observed adherence to CPGs by CA-use in clinical practice for the German Statutory Health Insurance (SHI). In patients referred to CA (DP 4), guideline adherence is 25.4% according to German and 20.4% according to the European CPG. \n \nThe findings of this dissertation indicate that the adherence to CPG is suboptimal in the clinical practice of CA-use in patients susceptive of chronic CAD in Germany for both the German and the European CPG. From the health economic perspective, improving adherence to CPG by (i) reducing the amount of CAs and ii) strengthening the role of non-invasive image guided testing modalities would result in cost savings and slightly lower MACE for the German SHI. \n \nTo improve guideline adherence, implementation by intersectoral strategies consisting of various components might be promising. Especially, these could affect structural barriers and facilitators when translating evidence-based recommendations into clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".