Diringer M N, Edwards D F
Department of Neurology, Washington University School of Medicine, St Louis, Mo., USA.
Arch Neurol. 1997 May;54(5):606-11. doi: 10.1001/archneur.1997.00550170078017.
The accurate prediction of functional outcome requires the development of multivariate models. To enhance their contribution to such models, the predictive power of each component must be optimized.
To improve the predictive power of coma scales as the first step in building more sophisticated multivariate models to predict specific levels of functional outcome.
Prospective descriptive study.
Neurology and neurosurgery intensive care unit (NNICU) in a tertiary care academic center.
Eighty-four patients with acute traumatic brain injury, intracerebral hemorrhage, subarachnoid hemorrhage, or ischemic stroke.
None.
The Glasgow Coma Scale (GCS) and Innsbruck Coma Scale (ICS) were administered within 24 hours of admission to the NNICU and then at 48-hour intervals until discharge of the patient from the NNICU. The assessments were performed by 3 occupational therapy graduate students working under the supervision of the medical director of the NNICU. The functional outcome at 3 months after discharge from the hospital was assessed by telephone by the same nurse using the following categories: (1) dead, (2) receiving nursing home or custodial care, (3) home with help, or (4) independent. Cronbach's alpha estimates of reliability for each scale were computed using all scores obtained during the study. The analyses indicated that the verbal response item of the GCS and the oral automatisms item of the ICS were less reliable in this patient population. The scales were modified by deleting those items, and predictive validity for the original and modified scales was computed using a discriminant function of the admission scores.
Before modification, both scales were best at predicting independence (GCS and ICS, 71% correct) and mortality (GCS, 60% correct; ICS, 56% correct). The modifications produced a modest improvement in the ability of both scales to better predict levels of outcome (modified GCS: home with help, 33% correct, independent, 71% correct; modified ICS: home with help, 0% correct, independent, 74% correct).
By deleting items with low reliability from the ICS and the GCS we achieved improved reliability and predictive validity. The improvement in predictive power, however, was inadequate to accurately predict functional outcome. Combining clinical scales with other demographic, physiological, functional, and radiographic data will be needed to achieve useful predictions of functional outcome.
准确预测功能预后需要开发多变量模型。为了增强各组成部分对这类模型的贡献,必须优化每个组成部分的预测能力。
作为构建更复杂的多变量模型以预测特定功能预后水平的第一步,提高昏迷量表的预测能力。
前瞻性描述性研究。
一家三级医疗学术中心的神经内科和神经外科重症监护病房(NNICU)。
84例急性创伤性脑损伤、脑出血、蛛网膜下腔出血或缺血性中风患者。
无。
在患者入住NNICU后24小时内以及随后每隔48小时直至患者从NNICU出院时,应用格拉斯哥昏迷量表(GCS)和因斯布鲁克昏迷量表(ICS)进行评估。评估由3名在NNICU医疗主任监督下工作的职业治疗研究生进行。出院3个月后的功能预后由同一名护士通过电话评估,分为以下几类:(1)死亡;(2)接受养老院或监护护理;(3)在家需帮助;(4)独立生活。使用研究期间获得的所有分数计算每个量表的克朗巴哈α信度估计值。分析表明,GCS的言语反应项目和ICS的口腔自动运动项目在该患者群体中可靠性较低。通过删除这些项目对量表进行了修改,并使用入院分数的判别函数计算原始量表和修改后量表的预测效度。
修改前,两个量表在预测独立生活(GCS和ICS,正确率71%)和死亡率(GCS,正确率60%;ICS,正确率56%)方面表现最佳。修改后,两个量表更好地预测预后水平的能力有适度提高(修改后的GCS:在家需帮助,正确率33%,独立生活:正确率71%;修改后的ICS:在家需帮助,正确率0%,独立生活:正确率74%)。
通过从ICS和GCS中删除可靠性较低的项目,我们提高了信度和预测效度。然而,预测能力的提高仍不足以准确预测功能预后。为了实现对功能预后的有效预测,需要将临床量表与其他人口统计学、生理学、功能和影像学数据相结合。