Changing Evaluation and Management of Severe Orbital Infections
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".