Effectiveness of a computerized decision support system for anticoagulation management in hemodialysis patients: A before–after study
Bibliographic record
Abstract
Introduction The risk-benefit profile for warfarin anticoagulation in hemodialysis (HD) patients differs compared with the non-HD population. HD patients are at increased risk of both thromboembolism and bleeding related to anticoagulation therapy. In addition, anticoagulation control may be more difficult to achieve in the HD population due to frequent comorbidities, subclinical Vitamin K deficiency, altered pharmacokinetics due to uremia and the concurrent use of multiple medications. While computerized decision support systems (CDSS) to assist with anticoagulation management are safe and effective in the non-HD population, they have not been well studied in HD outpatients. Methods A before-after study compared anticoagulation control for HD outpatients receiving warfarin at a tertiary medical center in Canada during two time periods: an initial period of nephrologist-led anticoagulation management and a second period after implementation of a pharmacist-led, CDSS-assisted anticoagulation management strategy. Findings Forty-two patients were included. Following implementation of the CDSS-assisted strategy, there was no significant change in median therapeutic time-in-range (3.7% difference (IQR, -9.5% to 20.6%); P = 0.247). Median change in INR testing frequency was 1.2 (IQR, 0.1-2.2; P = 0.0001) fewer tests per patient per month, which equates to approximately 15 fewer tests per patient per year. Adverse events were similar. Discussion Implementing a CDSS-assisted strategy for anticoagulation management in HD outpatients is effective. Doing so may lead to modest cost savings related to less frequent INR testing.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".