The effect of computer decision support on optimizing appropriate dosing of novel oral anticoagulant therapy in the IMPACT-AF study
Bibliographic record
Abstract
Abstract Background Guidelines favour use of the non-vitamin K oral anticoagulants (NOACs) over vitamin-K antagonists for stroke prevention in atrial fibrillation (AF). However, studies have shown these agents are being under-dosed relative to the doses recommended in the product labels. Purpose To assess the ability of a CDS tool, employed to support management of patients with AF in primary care, to optimize NOAC prescribing. Methods The Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) study was a cluster randomized controlled trial that assessed the ability of a CDS tool to optimize care of community-based AF patients. Between September 2014 and December 2016, 203 primary care providers (104 randomized to CDS use, 99 to usual care [UC]) and 1133 of their patients (n=597 CDS, n=548 UC) were enrolled. Among other functions, 9 CDS program rules provided recommendations on NOAC prescribing based upon a given patient's clinical profile, as per product labels. Appropriate NOAC prescribing within the IMPACT-AF study population was compared between patients managed with the CDS versus UC at baseline and 12-months. Results Of those patients prescribed a NOAC, a high proportion (approximately 70%) were managed as per NOAC prescribing recommendations at baseline (Fig. 1). At 12 months, this proportion did not appreciably change in the UC arm (Fig. 1). In the CDS arm, an 8.2% absolute/11.8% relative improvement in appropriate NOAC prescribing over baseline was seen at 12-months (Fig. 1). A comparison of patients at baseline and 12-months within each arm revealed a non-significant decline in the level of appropriate NOAC prescribing in the UC group (p=0.53). In the CDS arm, a significant improvement was observed in appropriate NOAC prescribing over time (p<0.001). Conclusion Even prior to any quality improvement efforts, appropriate NOAC prescribing was higher than anticipated in this contemporary cohort of community-based AF patients. At 12-months, significant further improvements were seen in the CDS but not the UC arm. These findings suggest that physician decision support can help enhance appropriate NOAC prescribing in the primary care setting. Figure 1. Appropriate NOAC prescribing Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Bayer Inc.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".