Vanni Simone, Pecci Rudi, Edlow Jonathan A, Nazerian Peiman, Santimone Rossana, Pepe Giuseppe, Moretti Marco, Pavellini Andrea, Caviglioli Cosimo, Casula Claudia, Bigiarini Sofia, Vannucchi Paolo, Grifoni Stefano
Department of Emergency Medicine, Ospedale Versilia, Azienda USL Toscana Nord Ovest, Firenze, Italy.
Audiology Clinic, Azienda Ospedaliero-Universitaria Careggi, Firenze, Italy.
Front Neurol. 2017 Nov 7;8:590. doi: 10.3389/fneur.2017.00590. eCollection 2017.
We investigated the reliability and accuracy of a bedside diagnostic algorithm for patients presenting with vertigo/unsteadiness to the emergency department.
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 ponneous and positional nystagmus, (2) the evaluation of the ystagmus irection, (3) the head mpulse test, and (4) the evaluation of equilibrium (stadin). Reliability of each step was analyzed by Fleiss' 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.
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 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.
Using the STANDING algorithm, non-sub-specialists achieved good reliability and high accuracy in excluding stroke and other threatening causes of vertigo/unsteadiness.
我们研究了一种用于急诊科眩晕/不稳患者的床旁诊断算法的可靠性和准确性。
我们纳入了一家三级医院中连续出现眩晕/不稳症状的成年患者。STANDING是我们之前描述的四步算法的首字母缩写，该算法基于眼球震颤观察和知名诊断手法，包括：（1）区分自发性和位置性眼球震颤；（2）评估眼球震颤方向；（3）头部脉冲试验；（4）平衡评估（站立）。通过Fleiss计算分析每一步的可靠性。参考标准（中枢性眩晕）是由初始影像学检查或3个月随访期间诊断出的包括中风、脱髓鞘疾病、肿瘤或其他脑部疾病在内的脑部疾病组合。
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%]。主要病因是缺血性中风（70%）。STANDING显示出良好的可靠性（总体Fleiss值为0.83），第二步一致性最高（0.95），第三步最低（0.74）。该算法的总体准确率为88%（95%CI 85 - 88%），对中枢性眩晕显示出高敏感性（95%，95%CI 83 - 99%）和特异性（87%，95%CI 85 - 87%），非常高的阴性预测值（99%，95%CI 97 - 100%），以及阳性预测值为48%（95%CI 41 - 50%）。
使用STANDING算法，非专科医生在排除中风和其他眩晕/不稳的威胁性病因方面具有良好的可靠性和高准确性。
