| Literature DB >> 34331114 |
Jaakko Heikkinen1, Janne Nurminen1, Jarno Velhonoja2, Heikki Irjala2, Tatu Happonen1, Tero Soukka3, Kimmo Mattila1,4, Jussi Hirvonen5,6.
Abstract
OBJECTIVES: Due to its superior soft-tissue contrast and ability to delineate abscesses, MRI has high diagnostic accuracy in neck infections. Whether MRI findings can predict the clinical course in these patients is unknown. The purpose of this study was to determine the clinical and prognostic significance of various MRI findings in emergency patients with acute neck infections.Entities:
Keywords: Emergency medicine; Infection; Magnetic resonance imaging; Neck
Mesh:
Year: 2021 PMID: 34331114 PMCID: PMC8794929 DOI: 10.1007/s00330-021-08200-5
Source DB: PubMed Journal: Eur Radiol ISSN: 0938-7994 Impact factor: 5.315
Patient characteristics
| Characteristic | |
|---|---|
| Number of patients | 371 |
| Age (years, mean ± SD) | 41 ± 20 |
| Male ( | 226 (61%) |
| Female ( | 145 (39%) |
| BMI (kg/m2) | 27.7 |
| CRP (mg/L) | 121a ± 86 |
| WBC (× 109/L) | 14.6b ± 12 |
| Body temperature (°C) | 37.5c ± 0.8 |
| Duration of symptoms before imaging (days) | 5.0d ± 4.4 |
| ICU | 50 (14%) |
| Length of hospital stay (LOS) (days) | 4.4 ± 5.4e |
| Surgery | 254 (69%) |
Data available for (a) 364, (b) 362, (c) 250, (d) 363, and (e) 364 patients; LOS could not be determined in 7 patients because of transfers to other hospitals. Seventeen patients (5%) were not hospitalized
Fig. 1Anatomical localization of neck infections
Imaging outcomes. Values are N (%) or mean ± SD
| Outcome | |
|---|---|
| Abscess | 260 (73%)a |
| Maximal abscess diameter (mm) | 35 ± 22 |
| RPE, presence | 201 (54%) |
| Level (median) | C3 (50%) |
| Enhancing type | 149 (74%) |
| Fluid type | 52 (26%) |
| Thickness (mm) | 4.7 ± 2.3 |
| Suprahyoid | 84 (42%) |
| Infrahyoid | 2 (1.0%) |
| Both | 115 (57%) |
| ME, presence | 81 (26%)b |
| Anterior | 48 (59%) |
| Posterior | 39 (48%) |
| Both | 6 (7.4%) |
aTrue positives
bIn whom ME could be evaluated (311 of 371 studies)
Fig. 2Different types of RPE in axial fat-suppressed T2-weighted Dixon images (first row), axial pre-contrast T1-weighted images (second row), axial post-contrast in-phase Dixon T1-weighted images (third row), and axial ADC maps (fourth row), from three patients (a–c). a A 63-year-old male with tonsillitis had high T2 signal, contrast enhancement on T1, and no purulence on ADC (enhancement-type RPE). b A 23-year-old female with parotitis had high T2-signal, non-enhancing fluid on T1, and no purulence on ADC (fluid-type RPE). c A 41-year-old female with a throat infection had an intermediate T2-signal, non-enhancing fluid on T1, and low ADC consistent with purulent fluid (true retropharyngeal abscess)
Fig. 3Examples of RPE on axial fat-suppressed T2-weighted Dixon images. a A 17-year-old male with left-sided peritonsillar infection and abscess. b A 13-year-old male with right-sided odontogenic infection. c An 18-year-old male with superficial (lateral) lymphadenitis. d A 59-year-old male with infected thyroid mass (papillary carcinoma)
Fig. 4Examples of anterior (a) and posterior (b) mediastinal edema on axial fat-suppressed T2-weighted Dixon images. a A 46-year-old female with tonsillitis had edema in the anterior mediastinum at the level of the manubrium sterni (asterisk). b A 48-year-old female with peritonsillar and parapharyngeal abscesses had edema in the posterior mediastinum at the level of the Th3 vertebra (asterisk)
Fig. 5Clinical significance of RPE (a) and ME (b). Presence of RPE and ME were predictors of ICU treatment, larger abscesses, and higher CRP; ME was also a predictor of longer LOS and thicker RPE
Multivariate models for predicting ICU treatment. Odds ratios, p values, and Nagelkerke R2 values from binary logistic regression analyses
| Variable | Odds ratio | Odds ratio | Odds ratio | |||
| CRP | 1.01 | < 0.001 | 1.01 | 0.001 | 1.01 | 0.015 |
| RPE | 5.78 | 0.002 | 5.07 | 0.004 | ||
| ME | 2.64 | 0.011 | 2.09 | 0.067 | ||
| Abscess diameter | 1.02 | 0.006 | ||||
| Model | 0.17 | 0.29 | 0.33 |
Model 1 only has a clinical predictor, model 2 has a clinical predictor and MRI edema patterns, and model 3 adds the maximal abscess diameter in patients with an abscess. All models have 307 patients in whom ME could be evaluated and other data was available. In model 3, patients with no MRI evidence of abscess or false-positive abscesses in surgery were assigned “0” for maximal abscess diameter
Multivariate models for predicting LOS. Standardized beta values, p values, and R2 values from linear regression analyses
| Variable | Beta | Beta | Beta | |||
| CRP | 0.32 | < 0.001 | 0.26 | < 0.001 | 0.21 | < 0.001 |
| WBC | 0.10 | 0.089 | 0.09 | 0.124 | 0.05 | 0.331 |
| RPE | 0.002 | 0.969 | −0.03 | 0.657 | ||
| ME | 0.18 | 0.002 | 0.15 | 0.012 | ||
| Abscess diameter | 0.22 | < 0.001 | ||||
| Model | 0.13 | 0.16 | 0.20 |
Model 1 only has clinical predictors, model 2 has clinical predictors and MRI edema patterns, and model 3 adds the maximal abscess diameter in patients with an abscess. All models have 307 patients in whom ME could be evaluated and other data was available. In model 3, patients with no MRI evidence of abscess or false-positive abscesses in surgery were assigned “0” for maximal abscess diameter