Centre for Reviews and Dissemination, Alcuin College, University of York, York, YO10 5DD, UK.
Syst Rev. 2012 Feb 9;1:8. doi: 10.1186/2046-4053-1-8.
A common and potentially life-threatening complication of the treatment of childhood cancer is infection, which frequently presents as fever with neutropenia. The standard management of such episodes is the extensive use of intravenous antibiotics, and though it produces excellent survival rates of over 95%, it greatly inconveniences the three-fourths of patients who do not require such aggressive treatment. There have been a number of studies which have aimed to develop risk prediction models to stratify treatment. Individual participant data (IPD) meta-analysis in therapeutic studies has been developed to improve the precision and reliability of answers to questions of treatment effect and recently have been suggested to be used to answer questions regarding prognosis and diagnosis to gain greater power from the frequently small individual studies.
In the IPD protocol, we will collect and synthesise IPD from multiple studies and examine the outcomes of episodes of febrile neutropenia as a consequence of their treatment for malignant disease. We will develop and evaluate a risk stratification model using hierarchical regression models to stratify patients by their risk of experiencing adverse outcomes during an episode. We will also explore specific practical and methodological issues regarding adaptation of established techniques of IPD meta-analysis of interventions for use in synthesising evidence derived from IPD from multiple studies for use in predictive modelling contexts.
Our aim in using this model is to define a group of individuals at low risk for febrile neutropenia who might be treated with reduced intensity or duration of antibiotic therapy and so reduce the inconvenience and cost of these episodes, as well as to define a group of patients at very high risk of complications who could be subject to more intensive therapies. The project will also help develop methods of IPD predictive modelling for use in future studies of risk prediction.
儿童癌症治疗过程中常见且可能危及生命的并发症是感染，通常表现为发热伴中性粒细胞减少症。此类发作的标准治疗方法是广泛使用静脉内抗生素，虽然这能产生超过 95%的出色存活率，但却给四分之三不需要如此激进治疗的患者带来了极大的不便。已经有许多旨在开发风险预测模型来分层治疗的研究。治疗研究中的个体参与者数据（IPD）荟萃分析已被开发出来，以提高对治疗效果问题的回答的准确性和可靠性，最近有人建议将其用于回答预后和诊断问题，以从经常规模较小的个体研究中获得更大的权力。
在 IPD 方案中，我们将从多个研究中收集和综合 IPD，并检查因恶性疾病治疗而导致的发热性中性粒细胞减少症发作的结果。我们将使用层次回归模型开发和评估一种风险分层模型，以根据患者在发作期间经历不良结局的风险对其进行分层。我们还将探讨关于适应干预措施的 IPD 荟萃分析的既定技术的具体实践和方法问题，以便在预测模型背景下综合来自多个研究的 IPD 得出的证据。
我们使用该模型的目的是定义一组发热性中性粒细胞减少症风险较低的个体，他们可以接受减少抗生素治疗的强度或持续时间，从而减少这些发作的不便和成本，同时定义一组并发症风险非常高的患者，他们可能需要接受更强化的治疗。该项目还将有助于开发用于未来风险预测研究的 IPD 预测建模方法。
