Walsh Timothy S, Kydonaki Kalliopi, Lee Robert J, Everingham Kirsty, Antonelli Jean, Harkness Ronald T, Cole Stephen, Quasim Tara, Ruddy James, McDougall Marcia, Davidson Alan, Rutherford John, Richards Jonathan, Weir Christopher J
1Anaesthetics, Critical Care and Pain Medicine, University of Edinburgh, Edinburgh, United Kingdom.2Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom.3Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, United Kingdom.4Department of Anaesthetics, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom.5University Department of Anaesthetics, Glasgow University, Glasgow Royal Infirmary, Glasgow, United Kingdom.6Department of Anaesthetics, Monklands Hospital, National Health Service Lanarkshire, United Kingdom.7Department of Anaesthetics, Kirkcaldy Hospital, National Health Service Fife, United Kingdom.8Department of Anaesthetics, Victoria Infirmary, National Health Service GGC, Glasgow, United Kingdom.9Department of Anaesthetics, Dumfries Hospital, National Health Service Dumfries and Galloway, United Kingdom.10Department of Anaesthetics, Forth Valley Royal Hospital, National Health Service Forth Valley, United Kingdom.11Edinburgh Health Services Research Unit, Edinburgh University, Edinburgh, United Kingdom.
Crit Care Med. 2016 Mar;44(3):564-74. doi: 10.1097/CCM.0000000000001463.
To develop sedation, pain, and agitation quality measures using process control methodology and evaluate their properties in clinical practice.
A Sedation Quality Assessment Tool was developed and validated to capture data for 12-hour periods of nursing care. Domains included pain/discomfort and sedation-agitation behaviors; sedative, analgesic, and neuromuscular blocking drug administration; ventilation status; and conditions potentially justifying deep sedation. Predefined sedation-related adverse events were recorded daily. Using an iterative process, algorithms were developed to describe the proportion of care periods with poor limb relaxation, poor ventilator synchronization, unnecessary deep sedation, agitation, and an overall optimum sedation metric. Proportion charts described processes over time (2 monthly intervals) for each ICU. The numbers of patients treated between sedation-related adverse events were described with G charts. Automated algorithms generated charts for 12 months of sequential data. Mean values for each process were calculated, and variation within and between ICUs explored qualitatively.
Eight Scottish ICUs over a 12-month period.
Mechanically ventilated patients.
None.
The Sedation Quality Assessment Tool agitation-sedation domains correlated with the Richmond Sedation Agitation Scale score (Spearman ρ = 0.75) and were reliable in clinician-clinician (weighted kappa; κ = 0.66) and clinician-researcher (κ = 0.82) comparisons. The limb movement domain had fair correlation with Behavioral Pain Scale (ρ = 0.24) and was reliable in clinician-clinician (κ = 0.58) and clinician-researcher (κ = 0.45) comparisons. Ventilator synchronization correlated with Behavioral Pain Scale (ρ = 0.54), and reliability in clinician-clinician (κ = 0.29) and clinician-researcher (κ = 0.42) comparisons was fair-moderate. Eight hundred twenty-five patients were enrolled (range, 59-235 across ICUs), providing 12,385 care periods for evaluation (range 655-3,481 across ICUs). The mean proportion of care periods with each quality metric varied between ICUs: excessive sedation 12-38%; agitation 4-17%; poor relaxation 13-21%; poor ventilator synchronization 8-17%; and overall optimum sedation 45-70%. Mean adverse event intervals ranged from 1.5 to 10.3 patients treated. The quality measures appeared relatively stable during the observation period.
Process control methodology can be used to simultaneously monitor multiple aspects of pain-sedation-agitation management within ICUs. Variation within and between ICUs could be used as triggers to explore practice variation, improve quality, and monitor this over time.
采用过程控制方法制定镇静、疼痛和躁动质量指标,并在临床实践中评估其特性。
开发并验证了一种镇静质量评估工具,用于收集12小时护理期间的数据。领域包括疼痛/不适和镇静-躁动行为;镇静剂、镇痛药和神经肌肉阻滞剂的使用;通气状态;以及可能需要深度镇静的情况。每天记录预先定义的与镇静相关的不良事件。通过迭代过程,开发算法来描述肢体放松不佳、呼吸机同步性差、不必要的深度镇静、躁动以及总体最佳镇静指标的护理时间段比例。比例图描述了每个重症监护病房随时间(每2个月间隔)的过程。用G图描述了与镇静相关不良事件之间接受治疗的患者数量。自动算法生成了连续12个月数据的图表。计算每个过程的平均值,并定性探索重症监护病房内部和之间的差异。
8个苏格兰重症监护病房,为期12个月。
机械通气患者。
无。
镇静质量评估工具的躁动-镇静领域与里士满镇静躁动量表评分相关(斯皮尔曼ρ=0.75),在临床医生之间(加权kappa;κ=0.66)和临床医生与研究人员之间(κ=0.82)的比较中具有可靠性。肢体运动领域与行为疼痛量表有中等相关性(ρ=0.24),在临床医生之间(κ=0.58)和临床医生与研究人员之间(κ=0.45)的比较中具有可靠性。呼吸机同步性与行为疼痛量表相关(ρ=0.54),在临床医生之间(κ=0.29)和临床医生与研究人员之间(κ=0.42)的比较中可靠性为中等。共纳入825例患者(各重症监护病房范围为59 - 235例),提供了12385个护理时间段用于评估(各重症监护病房范围为655 - 3481个)。每个质量指标的护理时间段平均比例在各重症监护病房之间有所不同:过度镇静为12% - 38%;躁动为4% - 17%;放松不佳为13% - 21%;呼吸机同步性差为8% - 17%;总体最佳镇静为45% - 70%。平均不良事件间隔为每治疗1.5至10.3例患者出现一次。在观察期内,质量指标显得相对稳定。
过程控制方法可用于同时监测重症监护病房内疼痛-镇静-躁动管理的多个方面。重症监护病房内部和之间的差异可作为触发因素,用于探索实践差异、提高质量并长期进行监测。