Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
BMJ Open. 2023 Oct 24;13(10):e075597. doi: 10.1136/bmjopen-2023-075597.
Visceral leishmaniasis (VL) is a neglected tropical disease responsible for many thousands of preventable deaths each year. Symptomatic patients often struggle to access effective treatment, without which death is the norm. Risk prediction tools support clinical teams and policymakers in identifying high-risk patients who could benefit from more intensive management pathways. Investigators interested in using their clinical data for prognostic research should first identify currently available models that are candidates for validation and possible updating. Addressing these needs, we aim to identify, summarise and appraise the available models predicting clinical outcomes in VL patients.
We will include studies that have developed, validated or updated prognostic models predicting future clinical outcomes in patients diagnosed with VL. Systematic reviews and meta-analyses that include eligible studies are also considered for review. Conference abstracts and educational theses are excluded. Data extraction, appraisal and reporting will follow current methodological guidelines. Ovid Embase; Ovid MEDLINE; the Web of Science Core Collection, SciELO and LILACS are searched from database inception to 1 March 2023 using terms developed for the identification of prediction models, and with no language restriction. Screening, data extraction and risk of bias assessment will be performed in duplicate with discordance resolved by a third independent reviewer. Risk of bias will be assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Tables and figures will compare and contrast key model information, including source data, participants, model development and performance measures, and risk of bias. We will consider the strengths, limitations and clinical applicability of the identified models.
Ethics approval is not required for this review. The systematic review and all accompanying data will be submitted to an open-access journal. Findings will also be disseminated through the research group's website (www.iddo.org/research-themes/visceral-leishmaniasis) and social media channels.
CRD42023417226.
内脏利什曼病(VL)是一种被忽视的热带病,每年导致数千例可预防的死亡。有症状的患者往往难以获得有效治疗,如果得不到治疗,死亡是常态。风险预测工具可帮助临床团队和政策制定者识别可能受益于更强化管理途径的高风险患者。有兴趣使用其临床数据进行预后研究的研究人员应首先确定当前可用于验证和可能更新的可用模型。为了满足这些需求,我们旨在确定、总结和评估可用于预测 VL 患者临床结局的现有模型。
我们将纳入已开发、验证或更新预测 VL 患者未来临床结局的预后模型的研究。也考虑对包含合格研究的系统评价和荟萃分析进行综述。会议摘要和教育论文将被排除在外。数据提取、评估和报告将遵循当前的方法学指南。从数据库建立到 2023 年 3 月 1 日,使用为识别预测模型而开发的术语,对 Ovid Embase;Ovid MEDLINE;Web of Science 核心合集、SciELO 和 LILACS 进行搜索,并且没有语言限制。筛选、数据提取和偏倚风险评估将由两名重复进行,如果存在分歧,将由第三名独立评审员解决。偏倚风险将使用预测模型风险偏倚评估工具(PROBAST)进行评估。表格和图形将比较和对比关键模型信息,包括源数据、参与者、模型开发和性能测量以及偏倚风险。我们将考虑所确定模型的优势、局限性和临床适用性。
本综述不需要伦理批准。系统评价和所有相关数据将提交给开放获取期刊。研究结果还将通过研究小组的网站(www.iddo.org/research-themes/visceral-leishmaniasis)和社交媒体渠道传播。
PROSPERO 注册号:CRD42023417226。