Ruppert Matthew M, Lipori Jessica, Patel Sandip, Ingersent Elizabeth, Cupka Julie, Ozrazgat-Baslanti Tezcan, Loftus Tyler, Rashidi Parisa, Bihorac Azra
Department of Medicine, University of Florida, Gainesville, FL.
Precision and Intelligent Systems in Medicine (Prisma), Division of Nephrology, Hypertension, & Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.
Crit Care Explor. 2020 Dec 16;2(12):e0296. doi: 10.1097/CCE.0000000000000296. eCollection 2020 Dec.
Summarize performance and development of ICU delirium-prediction models published within the past 5 years.
Systematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.
Eligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.
Screened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.
Eighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62-0.94), specificity (0.50-0.97), and sensitivity (0.45-0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.
Although most ICU delirium-prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium-prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.
总结过去5年发表的重症监护病房（ICU）谵妄预测模型的性能和发展情况。
2019年4月使用PubMed、Embase、Cochrane Central、Web of Science以及护理与健康相关文献累积索引进行了系统的电子检索，以识别同行评审研究。
符合条件的研究是过去5年以英文发表的，专门针对成人ICU人群谵妄预测模型的开发、验证或重新校准。
由三名研究人员独立提取筛选出的文献引用，重叠率为42%，使用预测模型研究系统评价的关键评估和数据提取清单来验证一致性。
纳入了18项研究，其中有23个不同的预测模型。通过受试者操作特征曲线下面积（0.62 - 0.94）、特异性（0.50 - 0.97）和敏感性（0.45 - 0.96）评估，模型性能差异很大。大多数模型使用从单个时间点或时间段收集的数据来预测住院或ICU入院期间任何时间点谵妄的发生，并且缺乏为临床医生提供实用、可操作预测的机制。
尽管大多数ICU谵妄预测模型具有相对较好的性能，但它们在临床实践中的适用性有限。大多数模型是静态的，基于单个时间点收集的数据进行预测，没有考虑ICU入院期间病情的波动。需要进一步开展研究，以创建与临床相关的动态谵妄预测模型，该模型能够随着时间适应个体患者生理变化，并为临床医生提供可操作的预测。
