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首页> 外文期刊>Frontiers in Neurology >Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm
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Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm

机译:急诊部眩晕的鉴别诊断:STANDING算法的前瞻性验证研究

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Objective We investigated the reliability and accuracy of a bedside diagnostic algorithm for patients presenting with vertigo/unsteadiness to the emergency department. Methods We enrolled consecutive adult patients presenting with vertigo/unsteadiness at a tertiary hospital. STANDING, the acronym for the four-step algorithm we have previously described, based on nystagmus observation and well-known diagnostic maneuvers includes (1) the discrimination between S pon TA neous and positional nystagmus, (2) the evaluation of the N ystagmus D irection, (3) the head I mpulse test, and (4) the evaluation of equilibrium (sta N din G ). Reliability of each step was analyzed by Fleiss’ K calculation. The reference standard (central vertigo) was a composite of brain disease including stroke, demyelinating disease, neoplasm, or other brain disease diagnosed by initial imaging or during 3-month follow-up. Results Three hundred and fifty-two patients were included. The incidence of central vertigo was 11.4% [95% confidence interval (CI) 8.2–15.2%]. The leading cause was ischemic stroke (70%). The STANDING showed a good reliability (overall Fleiss K 0.83), the second step showing the highest (0.95), and the third step the lowest (0.74) agreement. The overall accuracy of the algorithm was 88% (95% CI 85–88%), showing high sensitivity (95%, 95% CI 83–99%) and specificity (87%, 95% CI 85–87%), very high-negative predictive value (99%, 95% CI 97–100%), and a positive predictive value of 48% (95% CI 41–50%) for central vertigo. Conclusion Using the STANDING algorithm, non-sub-specialists achieved good reliability and high accuracy in excluding stroke and other threatening causes of vertigo/unsteadiness.
机译:目的我们研究了床边诊断算法在急诊中出现眩晕/不稳定症状的患者的可靠性和准确性。方法我们招募了三级医院中连续出现眩晕/不稳定症状的成年患者。 STANDING,我们先前描述的基于眼球震颤观察和众所周知的诊断操作的四步算法的首字母缩写,包括(1)S pon TA neous和位置性眼球震颤之间的区别,(2)对N眼球震颤D的评估竖起,(3)头部冲击试验,(4)平衡评估(sta N din G)。通过Fleiss的K计算分析了每个步骤的可靠性。参考标准(中枢性眩晕)是脑部疾病的综合体,包括中风,脱髓鞘疾病,肿瘤或其他通过初步影像学或在3个月的随访中诊断出的脑部疾病。结果共纳入352例患者。中枢性眩晕的发生率为11.4%[95%置信区间(CI)8.2-15.2%]。主要原因是缺血性中风(70%)。 STANDING显示出良好的可靠性(总体Fleiss K为0.83),第二步显示最高(0.95),第三步显示最低(0.74)一致性。该算法的总体准确性为88%(95%CI 85–88%),显示出很高的灵敏度(95%,95%CI 83–99%)和特异性(87%,95%CI 85–87%),非常中心性眩晕的高阴性预测值(99%,95%CI 97-100%),阳性预测值48%(95%CI 41-50%)。结论通过使用STANDING算法,非专业人士在排除中风和其他眩晕/不稳定因素的过程中获得了良好的可靠性和高精度。

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