Department of Internal Medicine, Universidad de Antioquia, Medellín, Colombia.
Department of Internal Medicine and Subspecialties, Hospital Pablo Tobón Uribe, Medellín, Colombia.
Lupus. 2021 Mar;30(3):421-430. doi: 10.1177/0961203320983462. Epub 2021 Jan 6.
Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions.
To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature.
A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings' archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model.
We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic.
To date, very few prognostic models have been published on the infection of SLE patients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLE patients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures. PROSPERO CRD42020171638.
拥有可靠的系统性红斑狼疮（SLE）患者预后/感染风险预测模型，将允许我们根据个体情况解决这个问题，以研究和实施可能的预防或治疗干预措施。
确定并分析医学文献中所有关于 SLE 患者预后/感染风险的预测模型。
对 PubMed、Embase 和 LILACS 数据库进行了结构化检索，检索时间截至 2020 年 5 月 9 日。此外，还对过去八年美国风湿病学会（ACR）和欧洲抗风湿病联盟（EULAR）年会档案中的摘要进行了搜索。本研究纳入了在任何临床环境中、具有任何时间范围、针对 SLE 患者开发、验证或更新的预测预后模型的研究，这些模型的预测结果是某种类型的感染。本研究对语言、日期或发表状态均没有限制。为了进行系统评价，我们遵循了 CHARMS（系统评价中预测模型研究的批判性评估和数据提取）指南建议。使用 PROBAST 工具（评估预测模型研究偏倚和适用性的工具）来评估每个模型的偏倚风险和适用性。
我们确定了四个 SLE 患者感染预后模型。大多数情况下，每个候选预测因子的事件数量非常少。此外，在构建模型时，最初是根据单变量分析进行选择的，而没有对估计系数进行收缩。这表明所提出的模型存在高度的过度拟合和乐观倾向。
迄今为止，关于 SLE 患者感染的预后模型发表的很少。这些模型非常多样化，且被评为存在高度偏倚和方法学弱点。尽管广泛认识到 SLE 患者感染的频率和严重程度，但目前仍没有可靠的预测预后模型来促进对个性化预防或治疗措施的研究和实施。PROSPERO CRD42020171638。
