首页> 外文期刊>BMC Pediatrics >Diagnosing serious infections in acutely ill children in ambulatory care (ERNIE 2 study protocol, part A): diagnostic accuracy of a clinical decision tree and added value of a point-of-care C-reactive protein test and oxygen saturation
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Diagnosing serious infections in acutely ill children in ambulatory care (ERNIE 2 study protocol, part A): diagnostic accuracy of a clinical decision tree and added value of a point-of-care C-reactive protein test and oxygen saturation

机译:在门诊护理中诊断急性病患儿的严重感染(ERNIE 2研究方案,A部分):临床决策树的诊断准确性以及现场C反应蛋白测试和血氧饱和度的增加值

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Background Acute illness is the most common presentation of children to ambulatory care. In contrast, serious infections are rare and often present at an early stage. To avoid complications or death, early recognition and adequate referral are essential. In a recent large study children were included prospectively to construct a symptom-based decision tree with a sensitivity and negative predictive value of nearly 100%. To reduce the number of false positives, point-of-care tests might be useful, providing an immediate result at bedside. The most probable candidate is C-reactive protein, as well as a pulse oximetry. Methods This is a diagnostic accuracy study of signs, symptoms and point-of-care tests for serious infections. Acutely ill children presenting to a family physician or paediatrician will be included consecutively in Flanders, Belgium. Children testing positive on the decision tree will get a point-of-care C-reactive protein test. Children testing negative will randomly either receive a point-of-care C-reactive protein test or usual care. The outcome of interest is hospital admission more than 24?hours with a serious infection within 10?days. Aiming to include over 6500 children, we will report the diagnostic accuracy of the decision tree (+/? the point-of-care C-reactive protein test or pulse oximetry) in sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values. New diagnostic algorithms will be constructed through classification and regression tree and multiple logistic regression analysis. Discussion We aim to improve detection of serious infections, and present a practical tool for diagnostic triage of acutely ill children in primary care. We also aim to reduce the number of investigations and admissions in children with non-serious infections. Trial Registration ClinicalTrials.gov Identifier: NCT02024282
机译:背景技术急性疾病是儿童进行门诊治疗的最常见表现。相反,严重的感染很少见,而且通常在早期就出现。为了避免并发症或死亡,早期识别和适当的转诊至关重要。在最近的一项大型研究中,前瞻性地将儿童纳入了基于症状的决策树,其敏感性和阴性预测值接近100%。为了减少误报的数量,现场护理测试可能有用,可以在床旁立即得到结果。最可能的候选药物是C反应蛋白以及脉搏血氧仪。方法这是对严重感染的体征,症状和即时检验的诊断准确性研究。向家庭医生或儿科医生求助的急性病儿童将被连续收留在比利时的法兰德斯。在决策树上测试为阳性的孩子将获得即时护理C反应蛋白测试。测试结果为阴性的儿童将随机接受即时护理C反应蛋白测试或常规护理。令人感兴趣的结果是入院超过24小时,在10天之内出现严重感染。为了包括6500多名儿童,我们将报告决策树在敏感性,特异性,阳性和阴性可能性比以及阳性和阴性可能性方面的诊断准确性(+ /?即时C反应蛋白测试或脉搏血氧饱和度测定)。阴性预测值。新的诊断算法将通过分类和回归树以及多重逻辑回归分析来构建。讨论我们旨在改善对严重感染的检测,并提供一种实用的工具,可对初级保健中的急性病儿童进行诊断分类。我们还旨在减少非严重感染儿童的检查和入院次数。试验注册ClinicalTrials.gov标识符:NCT02024282

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