Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA
Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, USA.
BMJ Open. 2020 Dec 22;10(12):e042035. doi: 10.1136/bmjopen-2020-042035.
Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI.
Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool.
We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases.
CRD42019137274.
急性肾损伤（AKI）较为常见，与长期不良预后相关。鉴于该综合征存在异质性，预测 AKI 结局的能力可能有助于有效地利用资源和实现 AKI 个体化治疗。本系统评价旨在识别、描述和评估 AKI 住院患者结局预测相关文献中的现有模型。
将从六个数据库全面检索获得的相关文献导入 Covidence。摘要筛选和全文审查将由两名团队成员独立进行，如果出现任何分歧，将由第三名成员解决。纳入的研究为队列研究和至少包含 100 例成年住院 AKI 患者的随机对照试验。仅纳入评估多变量预测模型且报告准确性统计测量值（受试者工作特征曲线下面积或 C 统计量）和预测 AKI 缓解、AKI 进展、后续透析和死亡率的研究。数据提取将由两名团队成员独立进行，如果出现任何分歧，将由第三名成员解决。研究结果将按照系统评价和荟萃分析的首选报告项目进行报告。采用预测模型风险偏倚评估工具评估偏倚风险。
我们致力于通过在 PROSPERO 上注册我们的系统评价和未来发表来公开传播我们的研究结果。我们希望我们的综述为使用人工智能预测常见疾病结局的未来研究提供了一个平台。
PROSPERO 注册号：CRD42019137274。
