Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
BMJ Open. 2024 Sep 25;14(9):e086589. doi: 10.1136/bmjopen-2024-086589.
This study was undertaken to identify potential predictors of atrial fibrillation after cardiac surgery (AFACS) through a modified Delphi process and expert consensus. These will supplement predictors identified through a systematic review and cohort study to inform the development of two AFACS prediction models as part of the PARADISE project (NCT05255224). Atrial fibrillation is a common complication after cardiac surgery. It is associated with worse postoperative outcomes. Reliable prediction of AFACS would enable risk stratification and targeted prevention. Systematic identification of candidate predictors is important to improve validity of AFACS prediction tools.
This study is a Delphi consensus exercise.
This study was undertaken through remote participation.
The participants are an international multidisciplinary panel of experts selected through national research networks.
This is a two-stage consensus exercise consisting of generating a long list of variables, followed by refinement by voting and retaining variables selected by at least 40% of panel members.
The panel comprised 15 experts who participated in both stages, comprising cardiac intensive care physicians (n=3), cardiac anaesthetists (n=2), cardiac surgeons (n=1), cardiologists (n=4), cardiac pharmacists (n=1), critical care nurses (n=1), cardiac nurses (n=1) and patient representatives (n=2). Our Delphi process highlighted candidate AFACS predictors, including both patient factors and those related to the surgical intervention. We generated a final list of 72 candidate predictors. The final list comprised 3 demographic, 29 comorbidity, 4 vital sign, 13 intraoperative, 10 postoperative investigation and 13 postoperative intervention predictors.
A Delphi consensus exercise has the potential to highlight predictors beyond the scope of existing literature. This method proved effective in identifying a range of candidate AFACS predictors. Our findings will inform the development of future AFACS prediction tools as part of the larger PARADISE project.
NCT05255224.
通过改良 Delphi 流程和专家共识，确定心脏手术后心房颤动（AFACS）的潜在预测因素。这些预测因素将补充通过系统评价和队列研究确定的预测因素，为 PARADISE 项目（NCT05255224）中两个 AFACS 预测模型的开发提供信息。心房颤动是心脏手术后的常见并发症。它与术后不良结局相关。可靠预测 AFACS 可以进行风险分层和有针对性的预防。系统识别候选预测因素对于提高 AFACS 预测工具的有效性很重要。
这是一项 Delphi 共识研究。
本研究通过远程参与进行。
参与者是通过国家研究网络选定的国际多学科专家小组。
这是一个两阶段的共识研究，包括生成一个变量的长列表，然后通过投票进行细化，并保留至少 40%的小组成员选择的变量。
专家组由 15 名专家组成，他们参加了两个阶段，包括心脏重症监护医生（n=3）、心脏麻醉师（n=2）、心脏外科医生（n=1）、心脏病专家（n=4）、心脏药剂师（n=1）、重症监护护士（n=1）、心脏护士（n=1）和患者代表（n=2）。我们的 Delphi 流程突出了 AFACS 预测因素，包括患者因素和与手术干预相关的因素。我们生成了最终的 72 个候选预测因素列表。最终列表包括 3 个人口统计学因素、29 个合并症、4 个生命体征、13 个术中、10 个术后检查和 13 个术后干预预测因素。
Delphi 共识研究有可能突出现有文献范围之外的预测因素。这种方法在确定一系列 AFACS 候选预测因素方面非常有效。我们的研究结果将为 PARADISE 项目的未来 AFACS 预测工具的开发提供信息。
NCT05255224。
