Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
BMJ Glob Health. 2021 Jan;6(1). doi: 10.1136/bmjgh-2020-003451.
Early identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.
We systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.
Of 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56-11.13), hypoxia (2.10-8.11), altered consciousness (1.24-14.02), and markers of acidosis (1.36-7.71) and poor peripheral perfusion (1.78-17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49-0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.
Few studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.
CRD42019140542.
早期识别有严重发热性疾病风险的儿童可以优化转诊、入院和治疗决策,特别是在资源有限的环境中。我们旨在确定预测临床和实验室因素,以预测从社区就诊的发热儿童进展为严重疾病。
我们系统地检索了 1999 年 5 月 31 日至 2020 年 4 月 30 日期间 MEDLINE、Web of Science 和 Embase 中检索到的文献,并补充了参考文献列表的手工检索和咨询专家技术咨询小组。评估从社区就诊发热疾病的儿童中预后因素或临床预测模型的研究符合条件。主要结局是在登记后 30 天内确定的任何客观疾病严重程度测量值。我们计算了未调整的比值比 (LR) 来比较预后因素,并使用接收者操作特征曲线下的面积 (AUROC) 来比较临床预测模型。使用预测模型风险偏倚评估工具和预后研究质量工具评估了研究的偏倚风险和适用性。
在 5949 篇文章中,有 18 项研究纳入了 24530 名儿童的 200 个预后因素和 25 个临床预测模型,共评估了 200 个预后因素和 25 个临床预测模型。由于研究之间存在异质性,因此无法进行正式的荟萃分析。营养不良(阳性 LR 范围 1.56-11.13)、缺氧(2.10-8.11)、意识改变(1.24-14.02)、酸中毒(1.36-7.71)和外周灌注不良(1.78-17.38)是最常见的严重疾病预测因素。临床预测模型的性能差异很大(AUROC 范围 0.49-0.97)。适用性方面存在问题,大多数研究的偏倚风险较高。
很少有研究解决这个重要的公共卫生问题。我们从广泛的地理背景中确定了预后因素,这些因素可以帮助临床医生评估有进展为严重疾病风险的发热儿童。需要多中心研究,包括门诊患者,以探索普遍性并开发数据驱动的工具,以支持社区层面的患者优先排序和分诊。
CRD42019140542。