Identifying cases of spinal cord injury or disease in a primary care electronic medical record database

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. 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.

本数据集旨在从加拿大安大略省的基层医疗电子病历（Electronic Medical Records, EMR）数据库中识别脊髓损伤或疾病（Spinal Cord Injury or Disease, SCI/D）病例。研究通过人工审阅电子病历，建立了慢性脊髓损伤或疾病病例的金标准参照集，并以此评估可用于该数据库的潜在病例识别算法。
数据集采用加拿大安大略省基层医疗电子病历（Electronic Medical Records Primary Care, EMRPC）数据库。研究从该数据库的213,887名符合条件的患者中，随机抽取48,000名成年患者作为研究样本。无相关补充说明（N/A）。
候选算法通过灵敏度、特异度、阳性预测值（Positive Predictive Value, PPV）、阴性预测值（Negative Predictive Value, NPV）及F1值进行性能评估。最终共确定126例慢性脊髓损伤或疾病病例作为金标准参照集，其中57例为创伤性脊髓损伤（Traumatic Spinal Cord Injury, TSCI），67例为非创伤性脊髓损伤（Non-Traumatic Spinal Cord Injury, NTSCI）。
最优病例识别算法结合自由文本关键词检索与医师计费编码，其性能指标为：灵敏度70.6%（95%置信区间：61.9%~78.4%）、特异度98.5%（95%置信区间：97.3%~99.3%）、阳性预测值89.9%（95%置信区间：82.2%~95.0%）、阴性预测值94.7%（95%置信区间：92.8%~96.3%），F1值为79.1。
研究证实，依托完善的病例定义，通过自由文本条目从基层医疗电子病历数据库中识别慢性脊髓损伤或疾病病例具备可行性。构建脊髓损伤或疾病患者队列，可为后续开展该人群的流行病学研究与医疗服务利用分析提供支撑。