Edwards Jodi D, Koehoorn Mieke, Boyd Lara A, Sobolev Boris, Levy Adrian R
1School of Population and Public Health,University of British Columbia,Vancouver,British Columbia,Canada.
2Department of Physical Therapy,University of British Columbia,Vancouver,British Columbia,Canada.
Can J Neurol Sci. 2017 Jul;44(4):397-403. doi: 10.1017/cjn.2016.454.
Hospitalization data underestimate the occurrence of transient ischemic attack (TIA). As TIA is frequently diagnosed in primary care, methodologies for the accurate ascertainment of a TIA from physician claims data are required for surveillance and health systems planning in this population. The present study evaluated the diagnostic accuracy of multiple algorithms for TIA from a longitudinal population-based physician billing database.
Population-based administrative data from the province of British Columbia were used to identify the base population (1992-2007; N=102,492). Using discharge records for hospital admissions for acute ischemic stroke with a recent (<90 days) TIA as the reference standard, we performed receiver-operating characteristic analyses to calculate sensitivity, specificity, positive and negative predictive values and overall accuracy, and to compare area under the curve for each physician billing algorithm. To evaluate the impact of different case definitions on population-based TIA burden, we also estimated the annual TIA occurrence associated with each algorithm.
Physician billing algorithms showed low to moderate sensitivity, with the algorithm for two consecutive physician visits within 90 days showing the highest sensitivity at 37.7% (CI 95%=37.4-38.1). All algorithms demonstrated high specificity and moderate to high overall accuracy, resulting in low positive predictive values (≤5%), low discriminability (0.53-0.57) and high false positive rates (1 - specificity). Population-based estimates of TIA occurrence were comparable to prior studies and declined over time.
Physician billing data have insufficient sensitivity to identify TIAs but may be used in combination with hospital discharge data to improve the accuracy of estimating the population-based occurrence of TIAs.
住院数据低估了短暂性脑缺血发作（TIA）的发生率。由于TIA常在初级保健中被诊断出来，因此在对该人群进行监测和卫生系统规划时，需要从医生索赔数据中准确确定TIA的方法。本研究从一个基于人群的纵向医生计费数据库中评估了多种TIA算法的诊断准确性。
使用来自不列颠哥伦比亚省的基于人群的行政数据来确定基础人群（1992 - 2007年；N = 102,492）。以近期（<90天）有TIA的急性缺血性卒中住院出院记录作为参考标准，我们进行了接受者操作特征分析，以计算敏感性、特异性、阳性和阴性预测值以及总体准确性，并比较每种医生计费算法的曲线下面积。为了评估不同病例定义对基于人群的TIA负担的影响，我们还估计了与每种算法相关的年度TIA发生率。
医生计费算法显示出低至中等的敏感性，90天内两次连续医生就诊的算法敏感性最高，为37.7%（95%CI = 37.4 - 38.1）。所有算法都表现出高特异性和中等至高的总体准确性，导致阳性预测值较低（≤5%）、鉴别能力较低（0.53 - 0.57）和假阳性率较高（1 - 特异性）。基于人群的TIA发生率估计与先前研究相当，且随时间下降。
医生计费数据识别TIA的敏感性不足，但可与医院出院数据结合使用，以提高基于人群的TIA发生率估计的准确性。
