优化床边护士记录和训练有素的研究者在ICU谵妄评估之间的一致性。

IF 6 1区 医学 Q1 CRITICAL CARE MEDICINE
Kelly M Toth, Zahra Aghababa, Jason N Kennedy, Chukwudi Onyemekwu, Niall T Prendergast, Christopher A Franz, Michael E Reznik, Brian Jiang, Brett Curtis, Faraaz Shah, Georgios D Kitsios, Bryan J McVerry, Timothy D Girard
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引用次数: 0

摘要

目的:谵妄在重症监护病房是常见且有害的。重症监护谵妄筛查清单(ICDSC)和ICU混淆评估方法(CAM-ICU)是推荐用于谵妄识别的有效工具。然而,床边护士记录的谵妄评估在ICU的准确性是不一致的,限制了临床研究的实用性。我们试图评估和优化床边护士记录和训练有素的研究者谵妄评估之间的一致性。设计、环境和患者:在宾夕法尼亚州西南部卫生系统的大型学术医院重症监护病房中,每天由床边护士(使用ICDSC)和训练有素的研究人员(使用CAM-ICU)评估患有急性呼吸衰竭或败血症的危重成人谵妄。使用匹配的护士对研究人员谵妄评估,我们使用有效的截止值对谵妄状态进行分类,并使用Cohen's kappa评估一致性。我们推导并比较了使用ICDSC文件、机械通气状态和入院序贯器官衰竭评估来预测非昏迷患者谵妄的逻辑回归模型,并使用研究者CAM-ICU评估作为参考标准。我们使用十倍交叉验证在内部验证模型。干预措施:没有。测量和主要结果:从279例患者的1535个匹配评估样本中,床边护士使用已建立的ICDSC谵妄/正常临界值(ICDSC≥4)评估和训练有素的研究人员使用CAM-ICU评估(Cohen’s kappa = 0.42)之间存在中度一致。结合ICDSC单项成分和临床资料建立的logistic回归模型预测CAM-ICU为阳性研究,判别性好(曲线下面积= 0.87),交叉验证效果良好(F1评分= 0.72)。在敏感性分析中,ICDSC信息更有限的模型表现出良好的区分能力(F1 = 0.60-0.70),而经过验证的截止模型表现最差。结论:由床边护士ICDSC结果和临床变量提供的谵妄模型提高了ICU中检测到的谵妄的准确性,可以用于未来的实用研究,利用大型临床数据集来推进对谵妄机制、轨迹和结果的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Agreement Between Bedside Nurse-Documented and Trained Researcher Delirium Assessments in the ICU.

Objectives: Delirium is common and harmful in the ICU. The Intensive Care Delirium Screening Checklist (ICDSC) and Confusion Assessment Method for the ICU (CAM-ICU) are validated tools recommended for delirium identification. However, the accuracy of bedside nurse-documented delirium assessments in the ICU is inconsistent, limiting utility in clinical research. We sought to evaluate and optimize agreement between bedside nurse-documented and trained researcher delirium assessments.

Design, setting, and patients: Critically ill adults with acute respiratory failure or sepsis in ICUs in large academic hospitals in a southwestern Pennsylvania health system were assessed daily for delirium by bedside nurses (using the ICDSC) and trained researchers (using the CAM-ICU). Using matched nurse-to-researcher delirium assessments, we categorized delirium status using validated cutoffs and evaluated agreement using Cohen's kappa. We derived and compared logistic regression models that used ICDSC documentation, mechanical ventilation status, and admission Sequential Organ Failure Assessment to predict delirium in noncomatose patients, using researcher CAM-ICU assessments as the reference standard. We internally validated models using ten-fold cross-validation.

Interventions: None.

Measurements and main results: From a sample of 1535 matched assessments of 279 patients, there was moderate agreement between bedside nurse assessments using the established ICDSC delirium/normal cutoff (ICDSC ≥ 4) and trained researcher assessments using the CAM-ICU (Cohen's kappa = 0.42). A logistic regression model informed by individual ICDSC components and clinical data predicted a positive research CAM-ICU with good discrimination (area under the curve = 0.87) and performed well in cross-validation (F1 score = 0.72). In sensitivity analyses, models with more limited ICDSC information demonstrated fair to good discriminatory ability (F1 = 0.60-0.70), with the validated cutoff model having the lowest performance.

Conclusions: A delirium model informed by bedside nurse ICDSC findings and clinical variables improves accuracy of delirium detected in the ICU and can be used in future pragmatic research that leverages large clinical datasets to advance understanding of delirium mechanisms, trajectories, and outcomes.

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来源期刊
Critical Care Medicine
Critical Care Medicine 医学-危重病医学
CiteScore
16.30
自引率
5.70%
发文量
728
审稿时长
2 months
期刊介绍: Critical Care Medicine is the premier peer-reviewed, scientific publication in critical care medicine. Directed to those specialists who treat patients in the ICU and CCU, including chest physicians, surgeons, pediatricians, pharmacists/pharmacologists, anesthesiologists, critical care nurses, and other healthcare professionals, Critical Care Medicine covers all aspects of acute and emergency care for the critically ill or injured patient. Each issue presents critical care practitioners with clinical breakthroughs that lead to better patient care, the latest news on promising research, and advances in equipment and techniques.
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