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Extended dalteparin prophylaxis for venous thromboembolic events: cost-utility analysis in patients undergoing major orthopedic surgery.

2009· article· en· W2945710995 on OpenAlexaffabout
George Dranitsaris, Carmine Stumpo, Reginald E. Smith, William R. Bartle

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsAugmentium Pharma Consulting (Canada)
Fundersnot available
KeywordsMedicineWarfarinPulmonary embolismOrthopedic surgeryArthroplastyKnee replacementVenous thrombosisDeep veinHip replacementPlaceboHip fractureThrombosisSurgeryAnesthesiaInternal medicineAtrial fibrillationOsteoporosis

Abstract

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BACKGROUND: Deep vein thrombosis (DVT) and pulmonary embolism (PE) are manifestations of venous thromboembolic events (VTEs). Patients undergoing major surgical procedures such as total hip replacement (THR), total knee replacement (TKR), and hip fracture surgery (HFS) are at an elevated risk for VTEs. The American College of Chest Physicians' (ACCP) guidelines recommend that such patients receive thromboprophylaxis for at least 10 days. In patients undergoing THR or HFS, extended prophylaxis for up to 28-35 days is the recommended approach for those at high risk of thromboembolic events. The NAFT (North American Fragmin Trial) compared the prophylactic efficacy of dalteparin with that of warfarin during the in-hospital period, and with that of placebo during the period of hospital discharge until day 35 postsurgery, in patients who underwent total hip arthroplasty. During both the in-hospital and the postdischarge time periods, dalteparin significantly reduced the occurrence of DVT. Given the clinical relevance of these results, the low specificity of the ACCP recommendations regarding optimal prophylaxis duration, and the importance of optimizing the efficiency of DVT prophylaxis in the practice setting, a cost-utility analysis was conducted comparing dalteparin 10-day and 35-day (extended) with a warfarin 10-day protocol, in patients undergoing major orthopedic surgeries such as THR, TKR, or HFS. DESIGN AND SETTING: A three-arm decision model was developed using the prevalence of symptomatic DVT from NAFT publications, epidemiologic studies, and published meta-analyses. Healthcare resource use was abstracted from a survey of clinicians and from the economic literature. Utility estimates were obtained by interviewing a sample of 24 people from the general public using the time trade-off technique. The clinical, economic and utility data were then used to estimate the cost per quality-adjusted life-year (QALY) gained with dalteparin for 10 or 35 days relative to 10 days of warfarin. STUDY PERSPECTIVE: Canadian provincial healthcare system. MAIN OUTCOME MEASURES AND RESULTS: The cost per QALY gained with 10 days of dalteparin was below $Can1000 for all the surgeries evaluated (all costs are reported in 2007 Canadian dollars [$Can1 = $US1, as of December 2007]). In the case of extended prophylaxis, the incremental cost per QALY gained with 35 days of dalteparin over warfarin was $Can40 100, $Can46 500, and $Can31 200 for patients undergoing THR, TKR, and HFS, respectively. Reducing the duration of prophylaxis from 35 to 28 days generated ratios that were below $Can35 000 for all three surgeries evaluated. CONCLUSION: Ten days of dalteparin following major orthopedic surgery is a clinically and economically attractive alternative to warfarin for DVT prophylaxis. In the case of the 35-day dalteparin protocol, the results also indicated acceptable economic value to a publicly funded healthcare system, particularly in the settings of HFS and THR. In addition, reducing the duration of prophylaxis to 28 days postsurgery would be associated with a more favorable return on public healthcare expenditures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.024
GPT teacher head0.263
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2009
Admission routes2
Has abstractyes

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