Rehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada.
Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.
J Spinal Cord Med. 2021;44(sup1):S28-S39. doi: 10.1080/10790268.2021.1971357.
To identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR).
A reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database.
Electronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada.
A sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database.
N/A.
MAIN OUTCOME MEASURE(S): Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score.
126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9-78.4), 98.5% specificity (97.3-99.3), 89.9% PPV (82.2-95.0), 94.7% NPV (92.8-96.3), and an F-score of 79.1.
Identifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.
在安大略省初级保健电子病历(EMR)数据库中识别脊髓损伤或疾病(SCI/D)病例。
通过对 EMR 进行手动审查,建立了慢性 SCI/D 病例的参考标准;该参考标准用于评估同一数据库中潜在的病例识别算法。
电子病历初级保健(EMRPC)数据库,安大略省,加拿大。
从 EMRPC 数据库中 213887 名合格患者中随机抽取 48000 名成年患者作为样本。
无。
使用敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)和 F 分数评估候选算法。
确定了 126 例慢性 SCI/D 病例,形成参考标准。其中,57 例为创伤性脊髓损伤(TSCI)病例,67 例为非创伤性脊髓损伤(NTSCI)病例。最佳病例识别算法使用自由文本关键字搜索和医生计费代码,具有 70.6%的敏感性(61.9-78.4)、98.5%的特异性(97.3-99.3)、89.9%的阳性预测值(82.2-95.0)、94.7%的阴性预测值(92.8-96.3)和 79.1 的 F 分数。
使用自由文本条目从初级保健 EMR 数据库中识别慢性 SCI/D 病例是可行的,依赖于全面的病例定义。确定一组 SCI/D 患者将允许对这些患者的流行病学和卫生服务利用情况进行未来研究。