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Record W2564784662 · doi:10.4212/cjhp.v69i6.1608

Characterization of Venous Thromboembolism Risk in Medical Inpatients Using Different Clinical Risk Assessment Models

2016· article· en· W2564784662 on OpenAlexaffvenue
Reza Rafizadeh, Ricky D. Turgeon, Josh Batterink, Victoria Su, Anthony Lau

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2016
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSurrey Memorial HospitalSt. Paul's HospitalVancouver General HospitalBurnaby Hospital
Fundersnot available
KeywordsVenous thromboembolismMedicineRisk assessmentIntensive care medicineInternal medicineThrombosisComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Background: Symptomatic venous thromboembolism (VTE) occurs in about 1% of patients within 3 months after admission to a medical unit. Recent evidence for thromboprophylaxis in an unselected medical inpatient population has suggested only a modest net benefit. Consequently, guidelines recommend careful risk stratification to guide thromboprophylaxis. Objectives: To compare candidacy for thromboprophylaxis according to 4 risk stratification models: a regional preprinted order (PPO) set used in the study institution, the Padua Prediction Score, and the IMPROVE predictive and associative risk assessment models. Methods: A retrospective review of health records was undertaken for patients with no contraindication to pharmacologic thromboprophylaxis who were admitted to the internal medicine service of a teaching hospital between April and July 2013. Results: Of the 298 patients in the study cohort, 238 (80.0%) received pharmacologic thromboprophylaxis on admission, ordered according to the regional PPO. However, according to the Padua and the IMPROVE predictive risk assessment models, only 64 (21.5%) and 21 (7.0%) of the patients, respectively, were eligible for thromboprophylaxis at the time of admission. On the basis of risk factors identified during the subsequent hospital stay, 54 (18.1%) of the patients were eligible for thromboprophylaxis according to the IMPROVE associative model. Chance-corrected agreement between the PPO and the published risk assessment models was generally poor, with kappa coefficients of 0.109 for the PPO compared with the Padua Prediction Score and 0.013 for the PPO compared with the IMPROVE predictive model. Conclusions: These data suggest that quantitative models such as the Padua Prediction Score and the IMPROVE models identify more patients at low risk of venous thromboembolism than do in-hospital qualitative risk assessment models. Adoption of these guideline-based risk assessment models for predicting thromboembolic risk in medical inpatients could reduce the use of pharmacologic thromboprophylaxis from 80% to as low as 7%. Further external prognostic validation of risk assessment models and impact analysis studies may show improvements in safety and resource utilization. RÉSUMÉ Contexte : La thromboembolie veineuse symptomatique se produit chez environ 1 % des patients dans les trois mois suivant leur admission à un service médical. Des données récentes portant sur la thromboprophylaxie chez une population non sélectionnée de patients hospitalisés ne suggéraient qu’un modeste avantage. Par conséquent, les lignes directrices recommandent une stratification du risque rigoureuse pour guider l’emploi d’une thromboprophylaxie. Objectifs : Comparer l’admissibilité à la thromboprophylaxie en fonction de quatre modèles de stratification du risque : un ensemble d’ordonnances préimprimées adopté dans une région et utilisé dans l’établissement à l’étude, le score prédictif de Padua et les modèles prédictifs et associatifs d’évaluation du risque issus de l’étude IMPROVE. Méthodes : Une analyse rétrospective des dossiers médicaux a été menée auprès des patients ne présentant pas de contre-indication à la thromboprophylaxie médicamenteuse qui ont été admis au service de médecine interne d’un hôpital universitaire entre avril et juillet 2013. Résultats : Parmi les 298 patients de l’étude de cohorte, 238 (80,0 %) ont reçu une thromboprophylaxie médicamenteuse au moment de l’admission, prescrite conformément à l’ensemble d’ordonnances préimprimées en usage dans la région. Or, respectivement selon les modèles prédictifs d’évaluation du risque Padua et IMPROVE, seuls 64 (21,5 %) et 21 (7,0 %) des patients étaient admissibles à la thromboprophylaxie au moment de l’admission. En fonction de facteurs de risques identifiés pendant le séjour subséquent à l’hôpital, 54 (18,1 %) des patients étaient admissibles à la thromboprophylaxie selon le modèle associatif IMPROVE. L’accord corrigé pour le hasard entre l’ensemble d’ordonnances préimprimées et les modèles d’évaluation du risque publiés était généralement faible, les coefficients de kappa étant de 0,109 pour l’ensemble d’ordonnances préimprimées comparé au score prédictif de Padua et de 0,013 pour l’ensemble d’ordonnances préimprimées comparé au modèle prédictif IMPROVE. Conclusions : Ces données suggèrent que les modèles quantitatifs comme le score prédictif de Padua et les modèles IMPROVE permettent de dépister plus de patients qui sont à faible risque de thromboembolie veineuse que ne le permettent les modèles qualitatifs d’évaluation du risque propres aux hôpitaux. L’adoption de ces modèles d’évaluation du risque mis de l’avant dans des lignes directrices pour prédire les risques d’événements thromboemboliques chez les patients médicaux hospitalisés pourrait réduire l’utilisation de la thromboprophylaxie médicamenteuse, qui pourrait passer de 80 % à aussi peu que 7 %. De plus amples validations externes quant à la valeur prédictive des modèles d’évaluation du risque et des études d’analyse d’impact pourraient montrer des améliorations à la sécurité et une réduction de l’utilisation des ressources.

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)0.023
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 score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.348
Teacher spread0.309 · 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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Citations11
Published2016
Admission routes2
Has abstractyes

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