High Variation Between Hospitals in Vena Cava Filter Use for Venous Thromboembolism
Bibliographic record
Abstract
BACKGROUND: The extent to which vena cava filter (VCF) use varies between hospitals in the management of acute venous thromboembolism (VTE) is not clear. METHODS: We conducted a retrospective observational study that compared the frequency of VCF use among California hospitals from January 1, 2006, through December 31, 2010. Using administrative hospital discharge data, we followed explicit criteria to identify nontrauma patients with acute VTE, and determined the frequency of VCF placement in each of the hospitals that admitted more than 55 VTE patients. Multivariable hierarchical regression models to predict VCF use included important clinical and demographic variables as fixed effects and hospital as a random effect. RESULTS: Among the 263 hospitals included, 130 643 acute VTE hospitalizations occurred with the placement of 19 537 VCFs (14.95%). Variation in the percentage of acute VTE hospitalizations that included VCF placement was very high, from 0% to 38.96% (interquartile range, 6.23%-18.14%), with 18.49% of the observed variation due to differences among the hospitals that provided care. Significant clinical predictors of VCF use included acute bleeding at the time of admission (odds ratio, 3.4 [95% CI, 3.2-3.6]), a major operation after admission for VTE (3.4 [3.3-3.5]), presence of metastatic cancer (1.7 [1.6-1.8]), and extreme severity of illness (2.5 [2.3-2.7] vs mild). Insertion of VCFs occurred more frequently than expected in 109 hospitals and less frequently in 59. Hospital characteristics associated with VCF use included a small number of beds (odds ratio, 0.2 [95% CI, 0.2-0.4], <100 vs >400 beds), a rural location (0.4 [0.2-0.5]), and other private vs Kaiser hospitals (1.5 [1.1-2.0]). Use of VCFs varied widely even in geographically proximate areas. CONCLUSIONS: The frequency of VCF use in patients with acute VTE varied widely and depended on which hospital provided the care, even after adjusting for clinical and socioeconomic factors. Further research is needed to determine whether this variation is associated with local cultural differences between hospitals or with differences in the availability of interventional radiologists or specialists, or whether it reflects the absence of high-quality evidence that VCFs are effective.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".