Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis.
Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis.
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DOI：
10.1371/journal.pone.0241457
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
VanEpps JS
中科院分区：
文献类型：
--
作者：
VanAken SM;Newton D;VanEpps JS
据估计，每年有44万活跃病例发生，医疗器械相关感染给美国医疗保健系统带来了沉重的负担，2013年的成本约为98亿美元。表皮葡萄球菌是这些器械相关感染的最常见原因，通常涉及具有多重耐药和具有多种毒力因子的分离株。表皮葡萄球菌通常也是无菌血培养物的良性污染物。因此，区分致病性和非致病性分离物的检测将提高诊断的准确性，并防止过度使用/滥用抗生素。尝试使用多位点序列分型（MLST）和机器学习用于此目的的准确性较差（约73%）。在这项研究中，我们试图通过关注表型标记（即抗生素耐药性、人血浆中的生长适应性和生物膜形成能力）和特定毒力基因（即mecA、ses1和sdrF）的存在来提高预测致病性的诊断准确性。采用健康个体的共生分离株（n = 23）、血液培养污染物（n = 21）和被认为是真正菌血症的致病分离株（n = 54）。应用多种机器学习方法来表征菌株的致病性和非致病性。表型标记与毒力基因联合应用使诊断准确率达到82.4%（敏感性84.9%，特异性80.9%）。奥西林耐药性是最重要的变量，其次是血浆中生长速率。这项工作显示了在临床诊断应用中增加表型检测的希望。
With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic accuracy of predicting pathogenicity by focusing on phenotypic markers (i.e., antibiotic resistance, growth fitness in human plasma, and biofilm forming capacity) and the presence of specific virulence genes (i.e., mecA, ses1, and sdrF). Commensal isolates from healthy individuals (n = 23), blood culture contaminants (n = 21), and pathogenic isolates considered true bacteremia (n = 54) were used. Multiple machine learning approaches were applied to characterize strains as pathogenic vs non-pathogenic. The combination of phenotypic markers and virulence genes improved the diagnostic accuracy to 82.4% (sensitivity: 84.9% and specificity: 80.9%). Oxacillin resistance was the most important variable followed by growth rate in plasma. This work shows promise for the addition of phenotypic testing in clinical diagnostic applications.
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影响因子：
5.8
作者：
Asaad, Ahmed Morad;Qureshi, Mohamed Ansar;Hasan, Syed Mujeeb
通讯作者：
Hasan, Syed Mujeeb
DOI：
10.1093/bioinformatics/bty095
发表时间：
2018-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bertelli C;Brinkman FSL
通讯作者：
Brinkman FSL
影响因子：
5.2
作者：
Sabaté Brescó M;Harris LG;Thompson K;Stanic B;Morgenstern M;O'Mahony L;Richards RG;Moriarty TF
通讯作者：
Moriarty TF
影响因子：
14.2
作者：
Elzi, L.;Babouee, B.;Widmer, A. F.
通讯作者：
Widmer, A. F.
影响因子：
7.7
作者：
Evans, Daniel R.;Griffith, Marissa P.;Van Tyne, Daria
通讯作者：
Van Tyne, Daria