The University of Edinburgh Centre for Clinical Brain Sciences, Muir Maxwell Epilepsy Centre, Edinburgh, UK.
The University of Edinburgh, Usher Institute, Edinburgh, UK.
Epilepsy Res. 2020 Nov;167:106462. doi: 10.1016/j.eplepsyres.2020.106462. Epub 2020 Sep 13.
We investigate the case-ascertainment accuracy for potentially active epilepsy of four administrative healthcare datasets used to identify deceased adults in Scotland.
In this diagnostic accuracy study, unique patient identifiers were used to link administrative healthcare data for adults (aged 16 years and over) who died in Scotland between 01/01/09-01/01/16. Cases were ascertained from linking mortality records, hospital admissions, antiepileptic drug (AED) prescriptions, and primary care attendances. We assessed ICD-10 codes G40 (epilepsy), G41 (status epilepticus), and R56.8 (seizures) listed as causes of death and as hospital admission reasons, various AEDs, and F25 primary care epilepsy Read codes. These epilepsy indicators were searched through 01/01/09-01/01/16, suggesting active epilepsy during a maximal period of seven years before death. They were compared to epilepsy diagnoses made from medical records reviewed by a senior epileptologist, with a second senior epileptologist independently reviewing the medical records in a 10 % sample to check for specialist interrater agreement in epilepsy diagnoses. We validated how accurately epilepsy was identified by each dataset alone and when combined, calculating positive predictive value (PPV) and sensitivity (with 95 % confidence intervals (CIs)).
159,032 deceased potential epilepsy cases were captured across the four datasets. Medical records reviewed in a random sample of 936 confirmed that epilepsy was present in 614 and absent in 322. Specialist interrater diagnostic agreement was substantial (100 medical records reviewed in duplicate, kappa = 0.72, CI 0.58-0.86). G40-41 cause of death codes had a PPV of 86 % (CI 84-89 %) and sensitivity of 73 % (CI 69-76 %). Adding R56.8 lowered PPV to 69 % (CI 65-72 %) and raised sensitivity to 87 % (CI 84-90 %). The optimal algorithm combining two datasets consisted of F25 Read codes paired with AEDs (PPV 86 % (CI 80-92 %), sensitivity 93 % (CI 88-97 %)). Also effective was pairing G40-41 and/or R56.8 cause of death codes with AEDs (PPV 91 % (CI 89-94 %), sensitivity 81 % (CI 77-84 %)). Whilst algorithms combining three datasets raised PPV to as high as 93-95 %, the associated sensitivities were low (71 % at most).
Routinely-collected Scottish data can accurately identify epilepsy in deceased adults. It may be necessary to combine the diagnostic coding used with AEDs to ensure optimal case-ascertainment. The results help inform the design of future Scottish epilepsy mortality studies recruiting from administrative data sources.
我们调查了苏格兰用于识别已故成年人的四个行政医疗保健数据集在确定潜在活跃性癫痫方面的病例检出准确性。
在这项诊断准确性研究中，使用唯一的患者标识符将在苏格兰于 2009 年 1 月 1 日至 2016 年 1 月 1 日期间死亡的 16 岁及以上成年人的行政医疗保健数据进行链接。通过链接死亡率记录、住院记录、抗癫痫药物（AED）处方和初级保健就诊来确定病例。我们评估了 ICD-10 代码 G40（癫痫）、G41（癫痫持续状态）和 R56.8（发作），这些代码列为死亡原因和住院原因，以及各种 AED 和 F25 初级保健癫痫阅读代码。这些癫痫指标在 2009 年 1 月 1 日至 2016 年 1 月 1 日期间进行搜索，表明在死亡前的最长七年期间存在活动性癫痫。它们与由高级癫痫学家审查的病历中的癫痫诊断进行了比较，由第二位高级癫痫学家独立审查了 10%样本中的病历，以检查癫痫诊断的专家间一致性。我们验证了每个数据集单独以及组合时准确识别癫痫的程度，计算了阳性预测值（PPV）和敏感性（95%置信区间（CI））。
在四个数据集中共捕获了 159032 名潜在癫痫死亡病例。在随机抽取的 936 名患者的病历中进行了复查，结果证实 614 名患者存在癫痫，322 名患者不存在癫痫。专家间的诊断一致性很强（100 份病历进行了重复审查，kappa=0.72，CI 0.58-0.86）。死因 G40-41 代码的 PPV 为 86%（CI 84-89%），敏感性为 73%（CI 69-76%）。添加 R56.8 将 PPV 降低至 69%（CI 65-72%），并将敏感性提高至 87%（CI 84-90%）。结合两种数据集的最佳算法是 F25 阅读代码与 AED 配对（PPV 86%（CI 80-92%），敏感性 93%（CI 88-97%））。同时，将 G40-41 和/或 R56.8 死因代码与 AED 配对也很有效（PPV 91%（CI 89-94%），敏感性 81%（CI 77-84%））。虽然结合三种数据集的算法将 PPV 提高到高达 93-95%，但相关敏感性很低（最高为 71%）。
苏格兰常规收集的数据可以准确识别成年死者的癫痫。可能需要结合使用诊断编码和 AED 以确保最佳病例检出。研究结果有助于为未来从行政数据源招募的苏格兰癫痫死亡率研究提供信息。
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