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新型自动化呼吸机失同步检测算法与呼吸机失同步、潮气量输送和镇静的关系。

The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm.

机构信息

Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO.

Department of Biomedical Information, University of Columbia, New York, NY.

出版信息

Crit Care Med. 2018 Feb;46(2):e151-e157. doi: 10.1097/CCM.0000000000002849.

Abstract

OBJECTIVE

Ventilator dyssynchrony is potentially harmful to patients with or at risk for the acute respiratory distress syndrome. Automated detection of ventilator dyssynchrony from ventilator waveforms has been difficult. It is unclear if certain types of ventilator dyssynchrony deliver large tidal volumes and whether levels of sedation alter the frequency of ventilator dyssynchrony.

DESIGN

A prospective observational study.

SETTING

A university medical ICU.

PATIENTS

Patients with or at risk for acute respiratory distress syndrome.

INTERVENTIONS

Continuous pressure-time, flow-time, and volume-time data were directly obtained from the ventilator. The level of sedation and the use of neuromuscular blockade was extracted from the medical record. Machine learning algorithms that incorporate clinical insight were developed and trained to detect four previously described and clinically relevant forms of ventilator dyssynchrony. The association between normalized tidal volume and ventilator dyssynchrony and the association between sedation and the frequency of ventilator dyssynchrony were determined.

MEASUREMENTS AND MAIN RESULTS

A total of 4.26 million breaths were recorded from 62 ventilated patients. Our algorithm detected three types of ventilator dyssynchrony with an area under the receiver operator curve of greater than 0.89. Ventilator dyssynchrony occurred in 34.4% (95% CI, 34.41-34.49%) of breaths. When compared with synchronous breaths, double-triggered and flow-limited breaths were more likely to deliver tidal volumes greater than 10 mL/kg (40% and 11% compared with 0.2%; p < 0.001 for both comparisons). Deep sedation reduced but did not eliminate the frequency of all ventilator dyssynchrony breaths (p < 0.05). Ventilator dyssynchrony was eliminated with neuromuscular blockade (p < 0.001).

CONCLUSION

We developed a computerized algorithm that accurately detects three types of ventilator dyssynchrony. Double-triggered and flow-limited breaths are associated with the frequent delivery of tidal volumes of greater than 10 mL/kg. Although ventilator dyssynchrony is reduced by deep sedation, potentially deleterious tidal volumes may still be delivered. However, neuromuscular blockade effectively eliminates ventilator dyssynchrony.

摘要

目的

呼吸机不同步可能对急性呼吸窘迫综合征患者或有发生急性呼吸窘迫综合征风险的患者有害。从呼吸机波形中自动检测呼吸机不同步一直很困难。目前尚不清楚某些类型的呼吸机不同步是否输送大潮气量,镇静水平是否改变呼吸机不同步的频率。

设计

前瞻性观察研究。

地点

大学医疗重症监护病房。

患者

急性呼吸窘迫综合征患者或有发生急性呼吸窘迫综合征风险的患者。

干预措施

直接从呼吸机获取连续压力-时间、流量-时间和容量-时间数据。镇静水平和使用神经肌肉阻滞剂从病历中提取。开发并训练了包含临床见解的机器学习算法,以检测四种先前描述和临床上相关的呼吸机不同步形式。确定了归一化潮气量与呼吸机不同步之间的关系,以及镇静与呼吸机不同步频率之间的关系。

测量和主要结果

从 62 名接受通气治疗的患者中记录了总计 426 万次呼吸。我们的算法以大于 0.89 的接收器工作特征曲线下面积检测到三种类型的呼吸机不同步。呼吸机不同步发生在 34.4%(95%CI,34.41-34.49%)的呼吸中。与同步呼吸相比,双触发和流量限制呼吸更有可能输送大于 10ml/kg 的潮气量(40%和 11%,而 0.2%;两者比较均 p < 0.001)。深度镇静虽然减少但并未消除所有呼吸机不同步呼吸的频率(p < 0.05)。神经肌肉阻滞剂消除了呼吸机不同步(p < 0.001)。

结论

我们开发了一种计算机算法,可准确检测三种类型的呼吸机不同步。双触发和流量限制呼吸与经常输送大于 10ml/kg 的潮气量有关。尽管深度镇静可减少呼吸机不同步,但仍可能输送潜在有害的潮气量。然而,神经肌肉阻滞剂可有效消除呼吸机不同步。

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