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Enregistrement W2989332698 · doi:10.1182/blood-2019-129140

The Development and Application of a Clinical Severity Scoring Model for Post-Operative Venous Thromboembolism (VTE)

2019· article· en· W2989332698 sur OpenAlexaff
Aleksandra Kajetanowicz, Sudeep Shivakumar, Steve Doucette, Susan Pleasance, Christopher Green, Allen Tran, David R. Anderson

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueVenous Thromboembolism Diagnosis and Management
Établissements canadiensNova Scotia Health AuthorityDalhousie University
Organismes subventionnairesnon disponible
Mots-clésMedicineRivaroxabanPulmonary embolismDeep veinVenous thrombosisClinical trialThrombosisPhysical therapySeverity of illnessCompression stockingsInternal medicineSurgeryEmergency medicineWarfarin

Résumé

récupéré en direct d'OpenAlex

Background: In studies evaluating the prevention of venous thromboembolic events (VTE - composite of deep vein thrombosis (DVT) and pulmonary embolism (PE)) following surgery, VTE is considered as a binary event (present or absent). There is no consensus in the literature in determining the clinical severity of a post-operative VTE. The objectives of our study were to derive a severity scoring model for VTE in the post-operative setting and then apply the model to outcomes from a clinical trial evaluating aspirin vs. rivaroxaban for extended prophylaxis following total hip or knee arthroplasty (EPCATII). Methods: Thirty-one clinical scenarios were written, each describing a VTE event after a total hip or knee arthroplasty procedure. Each scenario varied the severity of the patient's presenting symptoms, the extent of thrombosis observed on radiographic studies, and the presence or absence of long-term symptoms. These scenarios were incorporated into a web-based survey sent to thrombosis clinicians. Respondents were asked to score each scenario on a 9-point scale based on perceived clinical severity. Responses from 29 clinicians were analyzed using mixed-effects regression models to determine the weight of each variable on the respondents' overall severity scores. The sum of scores for each weighted factor present based on parameter estimates from the model was calculated to categorize the scenarios into high, moderate, or low clinical severity categories. The VTE clinical severity scoring model was applied independently by two clinicians to the 36 cases of confirmed VTE from the EPCAT II trial. Two further reviewers gave their clinical opinion of each case's severity. Kappa scores for inter-rater reliability, and for agreement between clinical opinion and the model were determined. The proportion of EPCAT II cases rated as mild, moderate and severe by the model were determined for rivaroxaban and aspirin groups and then compared using the Cochran-Armitage test for trend. Results: The following factors shown as parameter estimates were associated with higher levels of clinical severity: moderate (1.28, p<0.0001) or severe (2.61, p<0.0001) radiographic findings; high severity symptoms at initial presentation (0.72, p<0.0001); presence of long-term symptoms (1.01, p<0.0001); and PE compared to DVT (1.49, p<0.0001). Based on these parameters, a scoring model was created using the nearest half point for each doubled parameter estimate for ease of calculation. The model classifies post-operative VTE into mild (score <4.5), moderate (score 4.5-8), or severe (score >8) clinical severity categories. Independent application of the model to the EPCAT II VTE cases had agreement in 33 of 36 cases (κ=0.89). The agreement between the clinical opinion and the model was 28 of the 36 cases (κ=-0.14). In all cases of disagreement, the clinical gestalt was more severe than the model. Cases randomized to rivaroxaban were scored as 10 mild (55.6%), 4 moderate (22.2%), and 4 severe (22.2%). Cases randomized to aspirin were scored as 12 mild (66.7%), 4 moderate (22.2%) and 2 severe (11.1%). There were no differences in severity of VTE between the aspirin and rivaroxaban groups (p=0.38). Conclusion: A clinical severity scoring model for post-operative VTE was created based upon scenarios rated by experienced thrombosis clinicians and validated by applying it to EPCAT II cases of VTE. This model may be useful for categorizing clinical importance of VTE in the post-operative setting. There was no apparent difference in the severity of post-operative VTE following total hip or knee arthroplasty whether aspirin or rivaroxaban was used for extended prophylaxis. Disclosures No relevant conflicts of interest to declare.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,040
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,084

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,040
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,336
Écart entre enseignants0,307 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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