Abstract 4146994: Electronic Health Record Based Clinical Decision Support Increases Guideline-Directed Medical Therapy Initiation or Dosage Intensification in Patients with Heart Failure with Reduced Ejection Fraction: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Guideline-directed medical therapy (GDMT) in patients with heart failure with reduced ejection fraction (HFrEF) remains underprescribed despite overwhelming evidence of clinical benefit. Electronic health record (EHR)-based clinical decision support (CDS) tools provide healthcare providers with evidence-based recommendations and reminders within the electronic health record system. EHR-based CDS tools offer an innovative and economical strategy to enhance GDMT prescription rates. Hypothesis: We hypothesized that EHR-based CDS is associated with increased GDMT initiation or dosage intensification in patients with HFrEF. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) published from inception to May 2024 on four databases: PubMed, Embase, CENTRAL, and MEDLINE. We included RCTs that assessed the impact of EHR-based CDS on GDMT initiation or dosage increase in patients with HFrEF. The primary outcome was a composite of GDMT initiation or dosage increase. Random effects meta-analysis was performed by Review Manager version 5.4 software. I 2 statistics was used to assess heterogeneity. Results: Out of 6716 retrieved studies, 5 RCTs involving 4881 patients met inclusion criteria. The inter-rater agreement was excellent (κ = 0.911). The primary outcome showed an overall effect size of 1.38 (95% CI: 1.00-1.91, P = 0.05; I 2 = 86%). Adjusted relative risk was not reported for most studies, and hence this data could not be provided. Visual inspection of the funnel plot was balanced. Conclusions: This meta-analysis indicates that EHR-based CDS tools show a potential to increase initiation of GDMT or increase GDMT dosage in HFrEF patients. Further investigation is required to validate these findings due to significant heterogeneity and limited included studies.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".