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Enregistrement W3183668890 · doi:10.1111/acem.13238

Hot Off the Press: Which Febrile Children With Sickle Cell Disease Need a Chest X‐ray?

2017· letter· en· W3183668890 sur OpenAlexaff
Justin Morgenstern, Corey Heitz, William K. Milne

Notice bibliographique

RevueAcademic Emergency Medicine · 2017
Typeletter
Langueen
DomaineMedicine
ThématiqueUltrasound in Clinical Applications
Établissements canadiensWestern UniversityMarkham Stouffville Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineEmergency departmentForearmConfidence intervalLikelihood ratios in diagnostic testingGold standard (test)Prospective cohort studyPediatricsPopulationPhysical therapySurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

Pediatric musculoskeletal injuries are seen frequently in the emergency department (ED). Between 25 and 50% of all children will sustain a fracture before the age of 16, with the distal forearm being the most common location to fracture.1-3 The traditional diagnostic approach uses x-ray to identify fractures, but obtaining x-rays can be painful, as well as adding time and cost to ED visits.4, 5 Recently, there has been interest in the use of point-of-care ultrasound (POCUS) to diagnosis pediatric fractures, but the inclusion of children with clinically obvious deformities may have overestimated ultrasound accuracy in previous trials.6−9 This study aims to determine the sensitivity of POCUS for nonangulated pediatric forearm fractures, while also measuring patient important outcomes such as pain, caregiver satisfaction, and procedure duration.10 This prospective, cross-sectional diagnostic study examined the performance of POCUS in the diagnosis of suspected nonangulated forearm fractures in pediatric patients aged 4–17 years. X-ray was considered the criterion standard. The test characteristics reported are a sensitivity of 94.7% (95% confidence interval [CI] = 89.7%–99.8%), a specificity of 93.5% (95% CI = 88.6%–98.5%), a positive likelihood ratio of 14.6, and a negative likelihood ratio of 0.6. This was a well-done diagnostic study, with a clearly defined patient population based in the ED, in which all patients underwent both the study test (POCUS) and a clinical standard (x-ray). There are some limitations. A convenience sample was used, which could result in selection bias if the included patients were in some way different from those who were not included. The accuracy of POCUS is user-dependent. The use of expert sonographers in this study provides a look at the accuracy of POCUS in ideal circumstances, but limits generalizability, as the average emergency physician may not possess these skills. On the other hand, sonographers were blinded to injury mechanism. Although this provides us with a more accurate look at POCUS in isolation, it may underestimate the diagnostic value of POCUS in practice, where images are guided and interpreted in the context of the history and physical examination. There is also a question of what constitutes the ideal criterion standard for fractures. X-ray was used as the criterion standard in this study, but we know that x-rays are imperfect. There were six factures identified by POCUS that were deemed false positives based on the x-ray results. However, it is possible these were real fractures that were missed by x-ray, but without clinical follow-up we cannot know. Similarly, without clinical follow-up, this study cannot tell us if the injuries missed by POCUS were clinically important. There were four missed injuries: one buckle fracture and three ulnar styloid fractures. If POCUS is going to be widely used to diagnose fractures, it would be ideal to see a randomized controlled trial comparing POCUS to x-ray as the initial diagnostic strategy and focusing on clinical outcomes in follow up as the primary outcome. A total of 169 children were enrolled in the study and 76 (45%) were diagnosed with fractures. The mean age was 11 years with 52% being male. Most fractures (80.3%) were buckle fractures. Sensitivity of POCUS (the primary outcome) was 94.7% (95% CI = 89.7%–99.8%). The remaining test characteristics for POCUS were a specificity of 93.5% (95% CI = 88.6%–98.5%), positive predictive value of 92.3% (95% CI = 86.4%–98.2%), a negative predictive value of 95.6% (95% CI = 91.4%–99.8%), a positive likelihood ratio of 14.6, and a negative likelihood ratio of 0.6. Inter-rater agreement between the bedside ultrasonography and an expert sonographer reviewing the images is reported as excellent, with a kappa of 0.74. As compared to x-ray, patients reported less pain with POCUS. Ninety percent of caregivers were “satisfied” or “very satisfied” with POCUS. Acute musculoskeletal injuries are common in pediatrics and accurate diagnosis is important. This study illustrates that POCUS, when performed by experienced sonographers, has a high diagnostic accuracy for nonangulated distal forearm fractures, but will miss some fractures. It is valuable to know that POCUS takes less time than x-rays, has a low level of reported pain, and has a high level of caregiver satisfaction. Although POCUS probably has a role in some clinical settings, these results do not support widespread adoption as a replacement for x-ray. Casey Parker: This is another paper showing that POCUS can give us good, rapid information. It remains difficult to use US purely in practice as our teams and orthopedic colleagues remain “in the dark.” I have a few comments/questions about the paper: Dr. Poonai's reply: Thanks for the comments and tips, Casey. Here are my responses: 1) You're correct that in the clinical setting we incorporate a history and exam but blinding was incorporated into the protocol so as not to inflate the test performance characteristics. 2) There were no data on these as we took the pediatric radiologist's interpretation as the criterion standard. Not all children receive follow-up x-rays. Dr. Poonai's reply: Interesting question, Dara. This is a great example of the clinical context in which many POCUS aficionados find the technology to be quite useful. Although adequately powered, I will admit that our study was small to moderately sized and, at this stage, I may be reluctant to use POCUS exclusively to rule out a fracture. However, our specificity was quite high, suggesting that if a fracture is seen on POCUS, the x-ray may not be needed. This depends on obtaining a reliable history from the child with respect to the mechanism of injury (read: consistent with injury pattern so as not to miss NAI) and location of the pain. Hope that helps. Eve Purdy: Thanks for the great episode. I am very skeptical that our orthopedic colleagues (at least where I work) would be keen for this to be used as a replacement for X-ray—although I certainly can see why and how it would be useful in different settings explored (LMIC, camps, etc.). Dr. Poonai's reply: You've made some great points, Eve. And yes, I fully agree that practice patterns across institutions, healthcare systems, and countries are important consideration in how the results of our study impact practice. As far as medicolegal issues, at our center, image acquisition (stills or video clips) using POCUS are uploaded onto a central registry called Qpath. From there, images can be read, annotated, and archived. This may be one avenue for ensuring that there's a paper trail. Pediatric forearm fractures can be assessed quickly and with minimal pain using POCUS in the ED. The accuracy is good, but with a sensitivity of 95% in expert hands, POCUS is probably not ready for routine use. For best accuracy, POCUS results should be always considered in the context of the history and physical examination.

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)0,001
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,052
Tête enseignante GPT0,346
Écart entre enseignants0,294 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2017
Routes d'admission1
Résumé présentoui

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