Multidisciplinary Approach for Reducing Risk of Catheter-Associated Thrombosis
Bibliographic record
Abstract
Abstract Aim: Catheter-associated thrombosis (CAT) is a potential complication of vascular access devices, including peripherally inserted central catheters (PICCs), which can result in therapy interruption, increased cost of care, and patient consequences including phlebitis and pulmonary embolism. There are multiple modifiable (e.g., catheter size, insertion, and location confirmation methods) and nonmodifiable (e.g., cancer, history of thrombosis) risk factors for CAT. A multidisciplinary approach focused on quality improvement may help to lower risk. Methods: A retrospective study of patients with PICCs placed by vascular access nurses was conducted after a quality improvement initiative at a hospital within a 1200-bed health system in Philadelphia. A pre-post analysis was performed to compare the rates of CAT before and after a multiyear intervention targeting modifiable risk factors. An economic model calculated economic impact based on results of the observational audit. Results: Across the health system, very low CAT rates (1.2%) were observed in the post-intervention period, compared with a pre-intervention rate of 4.6%. The greatest reduction was attributed to the elimination of 6-Fr PICCs as part of the intervention. For every 1000 PICC placements, the economic model predicted cost savings exceeding $1M USD (i.e., $1,399,644) due to avoided thrombosis. Conclusions: This retrospective study demonstrated that small improvements to controllable elements of catheter care in a broad patient population can result in significant reductions in the risk of CAT and associated costs. Further study is required to confirm benefits in larger populations, and to understand which modifications could result in the highest cost savings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".