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Enregistrement W4200186584 · doi:10.1542/gr.46-4-46

Changing Evaluation and Management of Severe Orbital Infections

2021· article· en· W4200186584 sur OpenAlexaboutno aff

Notice bibliographique

RevueAAP Grand Rounds · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueSinusitis and nasal conditions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIconCellulitisPediatricsSurgery

Résumé

récupéré en direct d'OpenAlex

Source: Krueger C, Mahant S, Begum N, et al Changes in the management of severe orbital infections over seventeen years. Hosp Pediatr. 2021;11(6):613-621; doi:10.1542/hpeds.2020-001818Investigators from The Hospital for Sick Children and the University of Toronto, both in Toronto, Canada, conducted a retrospective study to compare management and outcomes among children hospitalized at their institution with severe orbital infections (including periorbital cellulitis, orbital cellulitis, and subperiosteal or orbital abscess) during 2 periods (2000–2005 and 2012–2016). Study participants were identified by ICD-9 and ICD-10 codes, Canadian version. The medical records of the identified children were reviewed, and data on demographics, antibiotic usage, use of adjuvant therapies (including intranasal corticosteroids and intranasal saline rinses), results of radiologic tests, length of stay (LOS), and complications were abstracted. Antibiotics were categorized as narrow or broad spectrum based on a standardized classification system. Differences in patient characteristics, management, and outcomes were assessed with t-tests or Mann-Whitney U tests for continuous data and chi-square of Fisher Exact tests for categorical data.There were 318 children identified with severe orbital infections, including 143 from 2000–2005 (Time Period 1) and 175 from 2012 2012–2016 (Time Period 2). The median age of study patients was 5.4 years, and 68.9% were male. Overall, the demographics of study participants from the 2 time periods were similar. Children with periorbital cellulitis were significantly younger than those diagnosed with orbital cellulitis (mean ages 4.0 and 6.4 years, respectively; P ≤0.01). Overall, a bacteriology etiology was identified in limited number of patients; the most common organisms isolated were Streptococcus anginosus (N = 21) and S aureus (N = 17). Among the 199 children (62.6%) for whom computerized tomography (CT) scans were obtained, 88% had sinusitis, 80% had orbital involvement, and 55% had orbital or subperiosteal abscess. The rate of CT use in patients during the 2 time periods were similar (60% in Time Period 1 and 65% in Time Period 2); disease severity based on CT findings also were similar. There was a significant increase in use of MRI in Time Period 2 compared to Time Period 1 (11% and 4%, respectively; P = 0.04). There also were significant increases in the number of intravenous antibiotics prescribed per patient in Time Period 2 (median value 3 vs 1 in Time Period 1; P ≤0.01), use of broad-spectrum antibiotics, use of intranasal corticosteroids (49% and 3%, respectively; P ≤0.01), and intranasal saline rinses (48% and 1%, respectively; P ≤0.01). LOS for children with periorbital cellulitis decreased for those hospitalized in Time Period 2 (median 45.3 hours vs 67.3 hours for those in Time Period 1; P = 0.01), but the LOS was similar for those with orbital cellulitis. Overall, severe complications occurred in 3.8% of study patients and included Potty’s puffy tumor (N = 8), intracranial extension of infection (N = 8), and cavernous sinus thrombosis (N = 3).The authors conclude that management of severe orbital infections changed over time, including the use of more numerous and broader-spectrum antibiotics.Dr Winer has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.The authors of the current study have shown that at their institution, management and treatment of orbital cellulitis have changed, with upward trends in subspecialty consultation, use of MRI, and the number/breadth of antibiotics. As a retrospective study, it fell to the researchers to determine post-hoc which patients had had orbital cellulitis.Clinically, however, the differentiation between pre-septal and orbital cellulitis often is a difficult process requiring meticulous history taking, physical examination including subjective pain rating, and imaging. It is important to understand the pathophysiology and etiology of the 2 distinct diseases, as doing so has the potential to decrease testing and better focus empiric antibiotics.1 Pre-septal cellulitis typically is associated with a superficial injury or lesion and usually is caused by Staphylococcus species or Streptococcus species.1 On the other hand, orbital cellulitis more commonly is caused by extension of sinus or maxillary dental disease, and is more likely to be caused by gram-negative bacteria, such as non-typable Haemophilus influenzae2 and anaerobes.Interestingly, approximately 20% of the patients in the current study were listed as having cellulitis limited to the periorbital space yet were included as having severe orbital infections. Here is where history becomes so important in separating pre-septal cellulitis from orbital cellulitis. It is exceedingly rare for pre-septal cellulitis originating from a superficial lesion to extend into the orbital space, but periorbital infections originating from sinusitis or maxillary dental infection have bacterial profile and complications more similar to orbital infections.3The work-up for severe orbital infections has changed to include broader empiric antibiotic therapy, subspecialty consultation, and MRI.Bacterial cultures were performed in fewer than 10% of the children with severe orbital infections in the current study. This information is critical to guide evaluation and treatment recommendations.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,395
Score d'incertitude au seuil0,217

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,024
Tête enseignante GPT0,307
Écart entre enseignants0,283 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2021
Routes d'admission1
Résumé présentoui

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