Kim Min-Chul, Kim Sujin, Cho Eun Been, Lee Guen Young, Choi Seong-Ho, Kim Seon Ok, Chung Jin-Won
Division of Infectious Diseases, Department of Internal Medicine, Chung-Ang University Hospital, Seoul 06973, Korea.
Department of Radiology, Chung-Ang University Hospital, Seoul 06973, Korea.
J Clin Med. 2020 Sep 21;9(9):3040. doi: 10.3390/jcm9093040.
We developed a new magnetic resonance indicator for necrotizing fasciitis (MRINEC) algorithm for differentiating necrotizing fasciitis (NF) from severe cellulitis (SC). All adults with suspected NF between 2010 and 2018 in a tertiary hospital in South Korea were enrolled. Sixty-one patients were diagnosed with NF and 28 with SC. Among them, 34 with NF and 15 with SC underwent magnetic resonance imaging (MRI). The MRINEC algorithm, a two-step decision tree including T2 hyperintensity of intermuscular deep fascia and diffuse T2 hyperintensity of deep peripheral fascia, diagnosed NF with 94% sensitivity (95% confidence interval (CI), 80-99%) and 60% specificity (95% CI, 32-84%). The algorithm accurately diagnosed all 15 NF patients with a high (≥8) laboratory risk indicator for necrotizing fasciitis (LRINEC) score. Among the five patients with an intermediate (6-7) LRINEC score, sensitivity and specificity were 100% (95% CI, 78-100%) and 0% (95% CI, 0-84%), respectively. Finally, among the 29 patients with a low (≤5) LRINEC score, the algorithm had a sensitivity and specificity of 88% (95% CI, 62-98%) and 69% (95% CI, 39-91%), respectively. The MRINEC algorithm may be a useful adjuvant method for diagnosing NF, especially when NF is suspected in patients with a low LRINEC score.
我们开发了一种用于区分坏死性筋膜炎(NF)和严重蜂窝织炎(SC)的坏死性筋膜炎磁共振指标(MRINEC)算法。纳入了2010年至2018年韩国一家三级医院所有疑似NF的成年患者。61例患者被诊断为NF,28例为SC。其中,34例NF患者和15例SC患者接受了磁共振成像(MRI)检查。MRINEC算法是一种两步决策树,包括肌间深筋膜T2高信号和深部周围筋膜弥漫性T2高信号,诊断NF的敏感性为94%(95%置信区间(CI),80 - 99%),特异性为60%(95% CI,32 - 84%)。该算法准确诊断了所有15例坏死性筋膜炎实验室风险指标(LRINEC)评分高(≥8)的NF患者。在5例LRINEC评分为中等(6 - 7)的患者中,敏感性和特异性分别为100%(95% CI,78 - 100%)和0%(95% CI,0 - 84%)。最后,在29例LRINEC评分低(≤5)的患者中,该算法的敏感性和特异性分别为88%(95% CI,62 - 98%)和69%(95% CI,39 - 91%)。MRINEC算法可能是诊断NF的一种有用辅助方法,特别是当LRINEC评分低的患者疑似NF时。