Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
BMJ Open. 2024 Jan 22;14(1):e073455. doi: 10.1136/bmjopen-2023-073455.
Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF.
We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care.
Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.
The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).
心力衰竭（HF）越来越常见，与发病率、死亡率和医疗保健成本过高有关。HF 的治疗可以改变疾病进程，减少 HF 中的临床事件。然而，许多 HF 病例在出现更晚期症状时才被发现，通常需要住院治疗。预测 HF 的发生具有挑战性，统计模型在常规临床实践中的性能和可扩展性有限。可在全国电子健康记录（EHR）中实施的 HF 预测模型可以实现针对 HF 的有针对性的诊断，从而更早地识别 HF。
我们将在常规收集的初级保健 EHR 上研究一系列开发技术（包括逻辑回归和监督机器学习方法），以预测新发病例 HF 的风险，预测期为 1 年、5 年和 10 年。临床实践研究数据链接（CPRD）-GOLD 数据集将用于推导（培训和测试），CPRD-AURUM 数据集用于外部验证。这两个数据集都包含大量患者队列，在年龄、性别和种族方面代表英格兰的人口。初级保健记录在患者层面上与二级保健和死亡率数据相关联。预测模型的性能将通过区分度、校准度和临床实用性进行评估。我们将只使用初级保健中常规可获得的变量。
CPRD-GOLD 和 CPRD-AURUM 数据集的许可获得了 CPRD（编号：21_000324）。CPRD 伦理委员会批准了该研究。研究结果将作为研究论文提交给同行评议期刊发表，并在同行评议会议上展示。
该研究在 ClinicalTrials.gov 上注册（NCT05756127）。该项目的系统评价在 PROSPERO 上注册（注册号：CRD42022380892）。
