PIONEER Hub, University of Birmingham, Birmingham, UK.
Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
BMJ Open. 2024 Feb 13;14(2):e080678. doi: 10.1136/bmjopen-2023-080678.
Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.
Retrospective observational study of routinely collected hospital electronic health record data.
Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.
Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date.
We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.
Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2-3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.
A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.
分析常规电子健康数据是医院住院患者进行长期疾病研究和实践的重要工具。这需要准确和完整地确定广泛的诊断，而这些诊断并不总是在入院时的单一文件中记录。本研究旨在确定需要回溯多长时间的电子病历才能捕捉到长期疾病诊断。
回顾性观察性研究，对常规收集的医院电子健康记录数据进行分析。
英国伯明翰伊丽莎白女王医院（UK）链接的数据由 PIONEER 急性护理数据中心持有。
2018 年 1 月至 12 月期间首次记录为慢性阻塞性肺疾病（COPD）加重（n=560）或急性中风（n=2142）入院的患者，且在指数日期前至少有 10 年的数据。
我们分别确定了 COPD 和急性中风患者最常见的国际疾病分类第 10 版编码诊断。对于每种诊断，我们记录了在整个 10 年回溯期内至少记录过一次该诊断的患者数量，然后将其与从指数入院前 1 年至 9 年的较短回溯期进行比较。
COPD 数据集中的前 10 个最常见诊断中有 7 个在 6 年的回溯期内达到>90%的完整性。心房颤动和糖尿病在 2-3 年内的编码完整性达到>90%，但高血压和哮喘的完整性一直上升到 10 年的回溯期。对于中风，前 10 名中有 4 个在 5 年内达到 90%的完整性；心绞痛在 7 年内的编码完整性达到>90%，而之前的短暂性脑缺血发作的完整性一直上升到 10 年的回溯期。
7 年的回溯期可以捕捉到大多数，但不是全部，常见的诊断。回溯期应根据研究的疾病进行调整。
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