All-cause and disease-related health care costs associated with recurrent venous thromboembolism
Bibliographic record
Abstract
It was the objective of this study to quantify the risk of complications and the incremental health care costs associated with recurrent VTE events. Health care insurance claims from the Ingenix IMPACT database from 01/2004-09/2008 were analysed. Subjects aged ≥18 years on the date of first recurrent VTE diagnosis with ≥12 months of baseline observation prior to the index recurrent VTE were matched 1:1 with no-recurrent VTE patients based on propensity scores. The risk of developing post-thrombotic syndrome (PTS) and other disease-related diagnoses (thrombocytopenia, superficial venous thrombosis, venous ulcer, pulmonary hypertension, stasis dermatitis, and venous insufficiency) was compared between the recurrent and no-recurrent VTE groups for up to one year. All-cause and disease-related costs per patient per year (PPPY) were calculated. The recurrent VTE and no-recurrent VTE cohorts (8,001 subjects in each group) were matched with respect to age, gender, and comorbidities. The risk ratios (RRs) indicated that the risk of developing post-event complications was significantly higher for the recurrent VTE group compared to the no-recurrent VTE group (RR [95% CI]: PTS: 2.7 [2.4 - 2.9], p-value <0.01). Patients with recurrent VTE had significantly higher average PPPY all-cause costs compared to no-recurrent VTE patients ($86,744 versus $37,525, cost difference: $49,219 [€33,617]; 95% CI= 46,253-51,989). Corresponding disease-related health care costs PPPY were also significantly higher for the recurrent VTE group ($11,120 vs $1,262, cost difference: $9,858 [€6,733]; 95% CI= $9,081-$10,476). In conclusion, in this large matched-cohort study, recurrent VTE patients had significantly higher risk of complications and health care costs compared to no-recurrent VTE patients.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".