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Nonleg Venous Thrombosis in Critically Ill Adults

2014· article· en· W2087233938 on OpenAlexafffund
François Lamontagne, Peter Dodek, Diane Heels‐Ansdell, Maureen O. Meade, Julia Pemberton, Yoanna Skrobik, Ian Seppelt, Nicholas E. Vlahakis, John Muscedere, Graham Reece, Marlies Ostermann, Soundrie Padayachee, Jamal A. Alhashemi, Michael Walsh, Bradley R. Lewis, David Schiff, Alan R. Moody, Nicole Zytaruk, Martine Leblanc

Bibliographic record

VenueJAMA Internal Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of TorontoHôpital Maisonneuve-RosemontQueen's UniversityUniversité de SherbrookeMcMaster UniversitySt. Paul's HospitalSt. Thomas HospitalUniversity of British ColumbiaOttawa Hospital
FundersCanadian Institutes of Health ResearchEisaiHeart and Stroke Foundation of CanadaPfizer
KeywordsMedicineDeep veinPulmonary embolismThrombosisPopulationVenous thrombosisProspective cohort studyCohortCohort studyIntensive careIntensive care unitAnticoagulantSurgeryInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

IMPORTANCE: Critically ill patients are at risk of venous thrombosis, and therefore guidelines recommend daily thromboprophylaxis. Deep vein thrombosis (DVT) commonly occurs in the lower extremities but can occur in other sites including the head and neck, trunk, and upper extremities. The risk of nonleg deep venous thromboses (NLDVTs), predisposing factors, and the association between NLDVTs and pulmonary embolism (PE) or death are unclear. OBJECTIVE: To describe the frequency, anatomical location, risk factors, management, and consequences of NLDVTs in a large cohort of medical-surgical critically ill adults. DESIGN, SETTING, AND PARTICIPANTS: A nested prospective cohort study in the setting of secondary and tertiary care intensive care units (ICUs). The study population comprised 3746 patients, who were expected to remain in the ICU for at least 3 days and were enrolled in a randomized clinical trial of dalteparin vs standard heparin for thromboprophylaxis. MAIN OUTCOMES AND MEASURES: The proportion of patients who had NLDVTs, the mean number per patient, and the anatomical location. We characterized NLDVTs as prevalent or incident (identified within 72 hours of ICU admission or thereafter) and whether they were catheter related or not. We used multivariable regression models to evaluate risk factors for NLDVT and to examine subsequent anticoagulant therapy, associated PE, and death. RESULTS Of 3746 trial patients, 84 (2.2%) developed 1 or more non-leg vein thromboses (superficial or deep, proximal or distal). Thromboses were more commonly incident (n = 75 [2.0%]) than prevalent (n = 9 [0.2%]) (P < .001) and more often deep (n = 67 [1.8%]) than superficial (n = 31 [0.8%]) (P < .001). Cancer was the only independent predictor of incident NLDVT (hazard ratio [HR], 2.22; 95% CI, 1.06-4.65). After adjusting for Acute Physiology and Chronic Health Evaluation (APACHE) II scores, personal or family history of venous thromboembolism, body mass index, vasopressor use, type of thromboprophylaxis, and presence of leg DVT, NLDVTs were associated with an increased risk of PE (HR, 11.83; 95% CI, 4.80-29.18). Nonleg DVTs were not associated with ICU mortality (HR, 1.09; 95% CI, 0.62-1.92) in a model adjusting for age, APACHE II, vasopressor use, mechanical ventilation, renal replacement therapy, and platelet count below 50 × 10(9)/L. CONCLUSIONS AND RELEVANCE Despite universal heparin thromboprophylaxis, nonleg thromboses are found in 2.2% of medical-surgical critically ill patients, primarily in deep veins and proximal veins. Patients who have a malignant condition may have a significantly higher risk of developing NLDVT, and patients with NLDVT, compared with those without, appeared to be at higher risk of PE but not higher risk of death. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00182143.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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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Citations63
Published2014
Admission routes2
Has abstractyes

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