SINGLE WHOLE-LEG COMPRESSION ULTRASOUND FOR EXCLUSION OF DEEP VEIN THROMBOSIS IN SYMPTOMATIC AMBULATORY PATIENTS: A PROSPECTIVE OBSERVATIONAL COHORT STUDY
Notice bibliographique
Résumé
Objectives & Background International guidance has recently recommended serial proximal compression ultrasound (CUS) as first line imaging for suspected deep vein thrombosis (DVT). Limitations with this strategy include attrition, lack of a clear diagnosis, and increased costs associated with serial resource use / clinical review. Single whole-leg CUS is a routine alternative diagnostic strategy that can reduce repeat attendance and identify alternative pathology. We sought to assess the performance characteristics of an established emergency department ambulatory protocol incorporating whole-leg CUS by non-physicians for exclusion of DVT. Methods A prospective observational cohort study, conducted between July 2011 and April 2012. Consecutive, ambulatory, adult patients with suspected DVT and negative or inconclusive whole-leg CUS had anticoagulation initially withheld and were followed up after three months. The primary outcome was a predefined clinically relevant adverse event rate: a subsequent diagnosis of symptomatic venous thromboembolism (VTE) or VTE related death during three month follow up. Secondary outcomes included alternative diagnoses, technical failure rate and characteristics associated with failure. Results 212 patients agreed to participate and were followed for three months. One patient was subsequently diagnosed with an isolated distal DVT. The adverse event rate was thus 1/212, 0.47% (95% confidence interval 0.08 to 2.62%). 150/212 patients were provided with a clear documented alternative diagnosis. CUS directly contributed to or confirmed the alternate diagnosis in 55/150 patients. Technical imaging failure occurred in 11.3% of suspected cases (95% CI 7.7 to 16.3). Several potential predictors of an inconclusive result were identified on multivariate analysis, including obesity, active infection, immobilisation and active cancer. Conclusion Patients who have anticoagulation withheld following a negative or inconclusive whole leg CUS for suspected DVT have a low rate of adverse events at 3 months. Including the calf in ultrasound examination aided and clarified diagnosis in approximately one third of patients. Technical failure remains an issue: several factors were significantly associated with inconclusive results in our cohort and may warrant an alternative diagnostic approach Abstract 008 Table 1 Measuring ED crowding Measure Operational Definition Ability of ambulances to offload patients. An ED is crowded when the 90th percentile time between ambulance arrival and offload is greater than 15 minutes Patients who leave without being seen or treated (LWBS) An ED is crowded when the number of patients who LWBS is greater than or equal to 5%. Time until Triage An ED is crowded when there is a delay greater than 5 minutes from patient arrival to begin their initial triage. ED occupancy rate. An ED is crowded when the occupancy rate is greater than 100%. Patients' total length of stay in the ED An ED is crowded when the 90th percentile patient's, total length of stay is greater than 4 hours. Time until a physician first sees the patient An ED is crowded when an emergent (1 or 2) patient waits longer than 30 minutes to be seen by a physician ED boarding time An ED is crowded when less than 90% of patients have left the ED 2 hour after the admission decision. Number of patients boarding in the ED. Boarders are defined as admitted patients waiting to be placed in an inpatient bed. An ED is crowded when there is greater than 10% occupancy of boarders in the ED ED; Emergency Department Abstract 008 Table 2 Performance of the ICMED against clinician perception of crowding Sensitivity (95% CI) Specificity (95% CI) Ambulance Offload 55.9 (45.3–66.5) 90.0 (83.6–96.4) Nurse Triage 70.6 (60.8–80.3) 76.0 (66.9–85.1) Occupancy 55.9 (45.3–66.5) 78.0 (69.1–86.9) Total stay 55.9 (45.3–66.5) 100.0 (88.8–100) ED Boarding Time 55.9 (45.3–66.5) 100.0 (88.8–100) Time to see a Physician 32.4 (22.4–42.4) 84.0 (76.2–91.8) Patients Boarding 85.3 (77.7–92.9) 70.0 (60.2–79.8) One Violation 100.0 (89.7–100) 38.0 (27.6–48.4) Two Violations 100.0 (89.7–100) 60.0 (49.5–70.5) Three Violations 91.2 (85.1–97.2) 100.0 (92.9–100) Four Violations 50.0 (39.3–60.7) 100.0 (88.8–100) Five Violations 26.5 (17.0–35.9) 100.0 (88.8–100) Six Violations 23.5 (14.5–32.6) 100.0 (88.8–100) Seven Violations 8.8 (2.8–14.9) 100.0 (88.8–100)
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».