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Contemporary Estimates of the Incidence of Venous Thromboembolism: A Population-Based Cohort Study

2012· article· en· W2979521610 on OpenAlexaffabout
Vicky Tagalakis, Valérie Patenaude, Susan R. Kahn, Samy Suissa

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicinePulmonary embolismIncidence (geometry)PopulationDeep veinCohortDiagnosis codeRetrospective cohort studyReimbursementCohort studyVenous thrombosisPediatricsThrombosisEmergency medicineHealth careInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Abstract 1143 Background: Venous thromboembolism (VTE) is a growing public health problem due largely to the aging population and the increasing prevalence of known risk factors such as surgery and cancer-related treatments. As a result, the true burden of VTE is not fully known and more contemporary estimates of incidence are needed. Objectives: We estimated the incidence of a first VTE event in a general population. Methods: This retrospective, observational study used the linked administrative healthcare databases of the province of Québec, Canada, including the province-wide hospitalization database (MED-ÉCHO) and the healthcare services database of RAMQ which oversees all physician reimbursement claims for services provided to Québec residents. From a source population of all RAMQ beneficiaries with a physician visit or a hospitalization associated with an ICD-9-CM or ICD-10-CA diagnosis code for deep vein thrombosis (DVT) or pulmonary embolism (PE) recorded between January 1, 2000 and December 31, 2009 and without a DVT or PE code prior to January 1, 2000, we identified a cohort of Québec residents with definite incident VTE and a cohort with definite or probable incident VTE. We used a priori determined diagnostic algorithms using RAMQ and MED-ÉCHO data to identify definite and probable cases of VTE. Subjects were followed forward in time from first-time VTE occurrence until the earliest of either death or end of study period (December 31, 2009). Incidence rates of first VTE, DVT alone, and PE with or without DVT were calculated by dividing the number of new cases by the total person-years at risk in the population of Québec residents eligible for RAMQ between 2000 and 2009. Age-specific incidence rates and associated 95% confidence intervals (CI) were calculated using achieved age during follow-up, and as a result patients contributed person-time in different age categories while aging during follow-up. Crude and age-adjusted incidence rate ratios (IRR) were reported comparing rates among women and men. Results: From the 245 452 Québec residents between 2000 and 2009 with at least 1 VTE diagnosis in RAMQ or MED-ÉCHO (source population), we identified 67 410 cases with definite VTE and 35 123 cases with probable VTE. The incidence rate of definite VTE was 0.91 per 1000 person-years (95% CI: 0.90–0.91). For DVT alone, the incidence was 0.53 per 1000 person-years (95% CI: 0.52–0.52) and for PE with or without DVT it was 0.38 per 1000 person-years (95% CI: 0.38–0.38). The incidence rates increased with age, and rates in patients 70 years of age and older were more than 4 times higher than rates in patients who were 40–69 years of age (Table 1). The VTE incidence rate was 0.99 per 1000 person-years (95% CI: 0.98–1.00) in women as compared to 0.82 per 1000 person-years (95% CI: 0.81–0.83) in men. The IRR was 1.19 (95% CI: 1.17–1.22) but this sex difference was no longer seen when adjusted for age (IRR 0.98; 95% CI: 0.96–1.01). The corresponding VTE, DVT alone, and PE incidence rates per 1000 person-years for definite or probable VTE were 1.24 (95% CI: 1.23–1.24), 0.79 (95% CI: 0.78–0.79), and 0.45 (95% CI: 0.45–0.46), respectively. Conclusion: Our study provides real-world contemporary estimates of VTE incidence. The risk in the general population is about 0.9 to 1.2 per 1000 person-years and is highest in the elderly. These data may help inform public healthcare planning and future research. Disclosures: No relevant conflicts of interest to declare.

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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)0.002
metaresearch head score (Gemma)0.005
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 score0.729
Threshold uncertainty score0.545

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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