Challenges of studying and predicting chronic kidney disease progression and its complications using routinely collected electronic healthcare records

背景：慢性肾脏疾病是一个世界性的公共卫生问题，影响了英国大约5-10%的成年人。它是一种进行性疾病，在早期无症状，但随着疾病的进展，发病率会增加，导致患者预后和生活质量差。它可以在无症状患者中通过血液和尿液检查检测到。CKD的并发症包括心血管风险增加、死亡率增加、急性肾损伤和对感染的易感性增加。此外，少数CKD患者发展为终末期肾脏疾病，需要常规透析或移植，这给公共卫生服务带来了沉重的经济负担。如果治疗成功，慢性肾脏疾病可以保持稳定多年。然而，在某些情况下，肾功能持续一段时间的快速恶化可能发生，称为进行性肾病。进行性肾脏疾病显然是一个重要的健康问题，对患者福利构成高风险，并对公共卫生服务构成负担。需要进一步的研究来提高对进行性肾脏疾病的性质、负担和后果的认识。早期识别进行性肾脏疾病的高风险患者和识别进行性CKD的亚表型将允许更好的个性化护理，并将对资源管理有用，并对政策有潜在影响。大型数据集的电子医疗记录（EHRs）可用于慢性肾病患者管理的初级保健在英国。除了捕获详细的患者特征外，通过常规测试定期捕获肾功能的纵向数据，从而可以调查单个患者的肾功能随时间的变化轨迹。然而，常规检查的频率因全科医生而异，检查往往发生在医疗团队最关注的人群中，这意味着对进展性疾病的检测是不同的。这就是所谓的测试偏见，接受测试的人往往代表病情较重的人群，而没有接受测试的人代表不参与卫生服务的病人和健康状况良好的人的混合体。此外，那些患有快速进展肾病的患者被转介到二级专科护理，这意味着这些患者将从数据中遗漏信息。目的：本研究项目将使用常规收集的电子医疗记录研究慢性肾脏疾病的进展和并发症，重点关注数据质量和适当的研究设计和方法来解决这些问题。主要目标：1)评估英国初级保健（国家慢性肾脏疾病审计数据库）常规收集的电子病历中肾功能测试的数据完整性以及数据质量问题对肾功能下降率估计准确性的影响2)对以往使用电子病历研究慢性肾脏疾病进展的研究进行全面回顾，特别是评估：报告数据完整性和/或缺失数据；用于克服数据缺失引起的问题的统计方法；3)调查全科医生（GP）执业行为的可变性（特别是患者健康记录中CKD的电子记录/编码）对已知与CKD进展相关的不良事件的影响（国家慢性肾脏疾病审计数据库）。[该分析通过研究实践水平的暴露来处理患者水平的混淆。4)开发不受肾功能检测行为影响的CKD进展预测模型，利用在CKD患者人群的电子病历中常规可用的数据。(此分析将使用尖叫数据库，这是我们公司拥有的外部数据库

Background: Chronic kidney disease is a worldwide public health problem affecting approximately 5-10% of adults in the UK. It is a progressive disease that is asymptomatic in the early stages but causes increasing morbidity as the disease progresses, leading to poor patient outcomes and quality of life. It can be detected in asymptomatic patients using blood and urine tests. Complications of CKD include increased cardiovascular risk, mortality, acute kidney injury and increased susceptibility to infection. Further, a minority of CKD patients progress to end stage renal disease requiring routine dialysis or a transplant which presents a heavy economic burden to public health services.Chronic kidney disease may remain stable over many years, if managed successfully. However, in some cases, a rapid deterioration in kidney function sustained over a period of time can occur, termed progressive kidney disease. Progressive kidney disease is clearly an important health problem, posing a high risk to patient welfare and a burden to public health services. Further research is needed to improve knowledge of the nature, burden and consequences of progressive kidney disease. Early identification of patients at high risk of progressive kidney disease and identification of sub-phenotypes of progressive CKD would allow better individualised care and would be useful for resource management with potential implications for policy.Large datasets of electronic healthcare records (EHRs) are available for chronic kidney disease patients managed in primary care in the UK. As well as capturing detailed patient characteristics, longitudinal data on kidney function are captured regularly over time through routine testing, allowing investigations in to the trajectory of kidney function over time in individual patients. However, the frequency of routine testing varies by individual general practitioners, and testing tends to occur in those the medical team is most concerned about, which means that detection of progressive disease is varied. This is what is termed testing bias where those who are tested tend to represent a sicker group of people and those who do not have a test represent a mix of sick people not engaging with the health service and people who are in good health. Moreover, those who have fast progressive kidney disease are referred to secondary specialist care, which means that these patients will be informatively missing from the data. Aims: This research project will study chronic kidney disease progression and complications using routinely collected electronic healthcare records, focussing on data quality and appropriate study designs and methods to address these issues. Key objectives:1) Evaluate data completeness for renal function tests in routinely collected EHRs in UK primary care (National chronic kidney disease audit database) and the impact of data quality issues on accuracy of estimation of rate of decline in renal function2) Conduct a comprehensive review of previous research using EHRs to study the progression of chronic kidney disease, in particular evaluating: reporting of data completeness and/or missing data; statistical methods used to overcome issues caused by missing data; and how authors interpreted study results in the presence of missing data issues 3) Investigate the impact of variability in general practitioner (GP) practice behaviour (in particular, electronic recording/coding of CKD on the patient health record) on adverse events known to be associated with CKD progression (National chronic kidney disease audit database). [This analysis deals with patient-level confounding by studying exposure at the practice level.]4) Develop predictive models for CKD progression that is not biased by renal function testing behaviours, utilising data which is routinely available in EHRs in the population of CKD patients. (This analysis will use the SCREAM ddatabase, an external database owned by our rese