Department of Internal Medicine, Vila Nova de Gaia Hospital Cente, Rua Conceição Fernandes, 4434-502, Vila Nova de Gaia, Portugal.
Stroke Unit, Vila Nova de Gaia Hospital Center, Rua Conceição Fernandes, 4434-502, Vila Nova de Gaia, Portugal.
BMC Med Res Methodol. 2018 Nov 20;18(1):145. doi: 10.1186/s12874-018-0613-8.
Prognostic tools for intracerebral hemorrhage (ICH) patients are potentially useful for ascertaining prognosis and recommended in guidelines to facilitate streamline assessment and communication between providers. In this systematic review with meta-analysis we identified and characterized all existing prognostic tools for this population, performed a methodological evaluation of the conducting and reporting of such studies and compared different methods of prognostic tool derivation in terms of discrimination for mortality and functional outcome prediction.
PubMed, ISI, Scopus and CENTRAL were searched up to 15th September 2016, with additional studies identified using reference check. Two reviewers independently extracted data regarding the population studied, process of tool derivation, included predictors and discrimination (c statistic) using a predesignated spreadsheet based in the CHARMS checklist. Disagreements were solved by consensus. C statistics were pooled using robust variance estimation and meta-regression was applied for group comparisons using random effect models.
Fifty nine studies were retrieved, including 48,133 patients and reporting on the derivation of 72 prognostic tools. Data on discrimination (c statistic) was available for 53 tools, 38 focusing on mortality and 15 focusing on functional outcome. Discrimination was high for both outcomes, with a pooled c statistic of 0.88 for mortality and 0.87 for functional outcome. Forty three tools were regression based and nine tools were derived using machine learning algorithms, with no differences found between the two methods in terms of discrimination (p = 0.490). Several methodological issues however were identified, relating to handling of missing data, low number of events per variable, insufficient length of follow-up, absence of blinding, infrequent use of internal validation, and underreporting of important model performance measures.
Prognostic tools for ICH discriminated well for mortality and functional outcome in derivation studies but methodological issues require confirmation of these findings in validation studies. Logistic regression based risk scores are particularly promising given their good performance and ease of application.
脑出血(ICH)患者的预后工具对于确定预后非常有用,并且在指南中被推荐以促进提供者之间的简化评估和沟通。在本系统评价和荟萃分析中,我们确定并描述了所有针对该人群的现有预后工具,对这些研究的进行和报告方法进行了方法学评估,并比较了不同的预后工具推导方法在死亡率和功能结局预测方面的区分能力。
我们检索了 PubMed、ISI、Scopus 和 CENTRAL,截至 2016 年 9 月 15 日,并使用参考文献检查确定了其他研究。两名审查员独立提取了有关研究人群、工具推导过程、纳入预测因素和区分度(c 统计量)的数据,使用基于 CHARMS 清单的预设计电子表格。意见分歧通过共识解决。使用稳健方差估计法对 c 统计量进行汇总,并使用随机效应模型对组间比较进行了元回归分析。
共检索到 59 项研究,纳入 48133 例患者,报告了 72 种预后工具的推导。有 53 种工具的区分度(c 统计量)数据可用,其中 38 种关注死亡率,15 种关注功能结局。两种结局的区分度均较高,死亡率的合并 c 统计量为 0.88,功能结局为 0.87。43 种工具为回归模型,9 种工具为机器学习算法推导,两种方法在区分度方面无差异(p=0.490)。然而,我们确定了一些方法学问题,涉及到缺失数据的处理、每个变量的事件数较少、随访时间不足、缺乏盲法、内部验证不频繁、重要模型性能指标报告不足等。
ICH 的预后工具在推导研究中对死亡率和功能结局具有良好的区分能力,但方法学问题需要在验证研究中确认这些发现。基于逻辑回归的风险评分特别有前途,因为它们具有良好的性能和易于应用。