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开发一种风险评分系统,以预测创伤性脑损伤患者的拔管失败风险。

Development of a risk score to predict extubation failure in patients with traumatic brain injury.

机构信息

Universidade Federal da Bahia, Salvador, BA, Brazil.

Universidade Federal da Bahia, Salvador, BA, Brazil.

出版信息

J Crit Care. 2017 Dec;42:218-222. doi: 10.1016/j.jcrc.2017.07.051. Epub 2017 Jul 31.

Abstract

PURPOSE

To identify predictors and develop a risk score for the prediction of extubation failure in TBI patients.

MATERIALS AND METHODS

We prospectively evaluated 311 TBI adults receiving mechanical ventilation for >48h in the intensive care unit. Epidemiological, ventilatory, airway protective, laboratory, and hemodynamic predictors were evaluated. A multiple logistic regression model was developed to predict the extubation failure risk. A score was developed using the arithmetic sum of the points for each independent predictor, whose scores were proportional to the regression coefficient. The accuracy of the model was determined using the C statistic.

RESULTS

Extubation failure occurred in 43 patients (13.8%). Five independent predictors were identified: female sex (4 points) Glasgow Coma Scale motor score≤5 (4 points), moderate-to-large secretion volume (4 points), absent or weak cough (3 points), and mechanical ventilation≥10days (2 points). We calculated the risk score for patients and three risk categories were defined: low (0-3 points), moderate (4-7 points), high (8-17 points). The extubation failure rates in the three groups were 3.5%, 21.2%, and 42.9%, respectively.

CONCLUSION

The score developed to predict extubation failure in TBI patients can identify three risk categories and can be easily applied in the ICU.

摘要

目的

确定预测 TBI 患者拔管失败的预测因素,并建立风险评分。

材料与方法

我们前瞻性评估了 311 名在重症监护病房接受机械通气>48 小时的 TBI 成人患者。评估了流行病学、通气、气道保护、实验室和血流动力学预测因素。使用多元逻辑回归模型预测拔管失败的风险。使用每个独立预测因素的分数之和来开发评分,其分数与回归系数成正比。使用 C 统计量确定模型的准确性。

结果

43 名患者(13.8%)发生拔管失败。确定了五个独立的预测因素:女性(4 分)、格拉斯哥昏迷量表运动评分≤5(4 分)、中等至大量分泌物量(4 分)、无咳嗽或咳嗽无力(3 分)、机械通气≥10 天(2 分)。我们为患者计算了风险评分,并定义了三个风险类别:低(0-3 分)、中(4-7 分)、高(8-17 分)。三组的拔管失败率分别为 3.5%、21.2%和 42.9%。

结论

为预测 TBI 患者拔管失败而开发的评分可识别三个风险类别,并且可以在 ICU 中轻松应用。

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