Risks and cost burden of venous thromboembolism and bleeding for patients undergoing total hip or knee replacement in a managed-care population
Bibliographic record
Abstract
BACKGROUND: Total hip and total knee replacement (THR/TKR) patients are at increased risk of developing venous thromboembolism (VTE). VTE prevention using anticoagulation therapy increases the risk of bleeding. Therefore, any assessment of the cost of VTE and its prevention should also take into consideration risks and costs of bleeding. OBJECTIVE: To assess the risks of developing VTE and bleeding in patients after THR or TKR given real-world use of thromboprophylaxis, and to quantify the incremental cost associated with each. METHODS: Analyses of insurance healthcare claims from the Ingenix IMPACT National Managed Care Database(TM) from January 2004 to December 2008 were conducted. Subjects were ≥18 years and had ≥1 procedure code for THR or TKR. Patients had to have ≥180 days of observation prior to surgery and were observed for ≤3 months after THR or TKR. VTE was defined as ≥1 diagnosis code for deep vein thrombosis or pulmonary embolism. Bleeding events were classified as major or non-major. Risks of VTE or bleeding events were calculated as number of patients with an event divided by number of patients with the procedure. Incremental all-cause healthcare costs associated with VTE or bleeding were calculated as the difference between cohorts of patients without VTE or bleeding matched 1:1 to patients with VTE or bleeding. RESULTS: Of 119,729 patients (43,670 THR and 76,059 TKR), 7974 had a VTE event and 4849 had a bleeding event (2216 major bleeding [a subset of 'any bleeding']). The risks of VTE, any bleeding, and major bleeding were 6.7, 4.0, and 1.9 events, respectively, per 100 patients. Up to 3 months after THR/TKR, mean incremental all-cause healthcare costs per patient per month associated with VTE, bleeding, and major bleeding were $2729, $2696, and $4304, respectively. Total monthly costs versus matched controls over 3 months were: VTE: $12,333 vs. $9604; any bleeding: $12,481 vs. $9785; major bleeding: $14,015 vs. $9710; p < 0.001 for all. LIMITATIONS: Key limitations included potential inaccuracies or omissions in procedures, diagnoses, or costs of claims data; lack of information on the amount of blood transfused or decreases in the hemoglobin level to evaluate the severity of a bleeding event; and potential biases due to the observational design of the study. CONCLUSION: From the managed-care population perspective, in THR/TKR patients the greater incidence of VTE compared to any bleeding and major bleeding translated into a higher cumulative cost burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".