A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS.
A PREVENTIVE TOOL FOR PREDICTING BLOODSTREAM INFECTIONS IN CHILDREN WITH BURNS.
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预测烧伤儿童血流感染的预防工具。
DOI：
10.1097/shk.0000000000002075
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Tompkins,RonaldG
中科院分区：
文献类型：
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作者：
Tsurumi,Amy;Flaherty,PatrickJ;Que,Yok-Ai;Ryan,ColleenM;Banerjee,Ankita;Chakraborty,Arijit;Almpani,Marianna;Shankar,Malavika;Goverman,Jeremy;Schulz3rd,JohnT;Sheridan,RobertL;Friedstat,Jonathan;Hickey,SeanA;Tompkins,RonaldG
引言：尽管儿科烧伤护理取得了重大进展，但血流感染（BSI）仍然是恢复期间的一个紧迫挑战。在BSI发生之前准确预测BSI的个性化医学方法将有助于预防工作并改善患者结局。研究方法：我们分析了多中心炎症和宿主对损伤的反应（“Glue Grant”）队列中严重烧伤（总烧伤表面积[TBSA]≥ 20%）患者的血液转录组。我们的研究包括82名儿童（年龄< 16岁）患者，在观察到的BSI发作前至少3天采集血液样本。我们应用最小绝对收缩和选择算子（LASSO）机器学习算法来选择一组预测BSI结果的生物标志物。结果如下：我们开发了一组对应于6个注释基因的10个探针组（ARG 2 [泛素化酶2]、CPT 1A [肉毒碱棕榈酰转移酶1A]、FYB [FYN结合蛋白]、ITCH [瘙痒E3泛素蛋白连接酶]、MACF 1 [微管肌动蛋白交联因子1]和SSH 2 [弹弓蛋白磷酸酶2]），两种未表征的（L0 C101928635，L0 C101929599）和两个未注释的区域。与TBSA（0.708; 95%CI，0.588-0.824）或TBSA和吸入性损伤状态（0.792; 95%CI，0.676-0.892）模型相比，我们的多生物标志物面板模型产生了高度准确的预测（受试者工作特征曲线下面积，0.938; 95%置信区间[CI]，0.881- 0.981）。将多生物标志物组与TBSA和吸入性损伤状态相结合的模型进一步提高了预测（0.978; 95%CI，0.941-1.000）。结论：多生物标志物面板模型产生了一个高度准确的预测BSI发病前。及早了解患者的风险状况将指导临床医生采取快速预防措施以限制感染，促进抗生素管理，这可能有助于缓解当前的抗生素耐药性危机，缩短住院时间和医疗资源负担，降低医疗成本，并显着改善患者的结果。此外，生物标志物的身份和分子功能可能有助于开发新的预防干预措施。基础尽管在儿科烧伤护理方面取得了重大进展，但血流感染（BSI）在恢复过程中仍然是一个常见但令人信服的挑战。皮肤层是抵御病原体的最外层屏障，皮肤层的损伤以及热损伤后的代谢和免疫改变使患者特别容易受到BSI的影响（1-6）。此外，侵入性器械的广泛使用和对伤口处理的需求为这些患者的BSI易感性增加了额外的复杂性（1，2）。严重热损伤并发继发性BSI与患者发病率和死亡率的急剧上升相关。与此同时，热损伤患者的BSI不可避免地导致住院时间延长，资源利用率增加，最终导致医疗保健费用增加（7-10）。
Introduction: Despite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a compelling challenge during recovery. A personalized medicine approach for accurate prediction of BSIs before they occur would contribute to prevention efforts and improve patient outcomes. Methods: We analyzed the blood transcriptome of severely burned (total burn surface area [TBSA]≥ 20%) patients in the multicenter Inflammation and Host Response to Injury (“Glue Grant”) cohort. Our study included 82 pediatric (aged< 16 years) patients, with blood samples at least 3 days before the observed BSI episode. We applied the least absolute shrinkage and selection operator (LASSO) machine-learning algorithm to select a panel of biomarkers predictive of BSI outcome. Results: We developed a panel of 10 probe sets corresponding to six annotated genes (ARG2 [arginase 2], CPT1A [carnitine palmitoyltransferase 1A], FYB [FYN binding protein], ITCH [itchy E3 ubiquitin protein ligase], MACF1 [microtubule actin crosslinking factor 1], and SSH2 [slingshot protein phosphatase 2]), two uncharacterized (LOC101928635, LOC101929599), and two unannotated regions. Our multibiomarker panel model yielded highly accurate prediction (area under the receiver operating characteristic curve, 0.938; 95% confidence interval [CI], 0.881–0.981) compared with models with TBSA (0.708; 95% CI, 0.588–0.824) or TBSA and inhalation injury status (0.792; 95% CI, 0.676–0.892). A model combining the multibiomarker panel with TBSA and inhalation injury status further improved prediction (0.978; 95% CI, 0.941–1.000). Conclusions: The multibiomarker panel model yielded a highly accurate prediction of BSIs before their onset. Knowing patients' risk profile early will guide clinicians to take rapid preventive measures for limiting infections, promote antibiotic stewardship that may aid in alleviating the current antibiotic resistance crisis, shorten hospital length of stay and burden on health care resources, reduce health care costs, and significantly improve patients' outcomes. In addition, the biomarkers' identity and molecular functions may contribute to developing novel preventive interventions.BACKGROUNDDespite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a common yet compelling challenge during the course of recovery. The damage to the skin layer, which is the outermost barrier against pathogens, and the metabolic and immune alterations following thermal injuries render patients particularly susceptible to BSIs (1–6). Furthermore, the extensive use of invasive devices and the need for wound manipulations add an extra layer of complexity to these patients' predisposition to BSIs (1, 2). Severe thermal injuries complicated by secondary BSIs are correlated with a dramatic rise in patient morbidity and mortality rates. At the same time, BSIs in thermally injured patients inevitably lead to prolonged hospital stays, increased utilization of resources, and ultimately higher health care costs (7–10).