School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK.
King's College Hospital NHS Foundation Trust, London, UK.
Eur J Heart Fail. 2024 Feb;26(2):302-310. doi: 10.1002/ejhf.3115. Epub 2024 Jan 11.
Heart failure with preserved ejection fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all heart failure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria.
In a retrospective cohort study, we used an NLP pipeline applied to the electronic health record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorized according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥50% were further categorized based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalized more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less comorbid and experiencing fewer acute cardiovascular events.
This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.
尽管射血分数保留型心力衰竭（HFpEF）占所有心力衰竭（HF）病例的近一半，但在临床实践中仍未得到充分诊断。HFpEF 的准确和及时诊断对于患者的适当管理和治疗至关重要。在这项研究中，我们探索了自然语言处理（NLP）根据欧洲心脏病学会（ESC）诊断标准提高 HFpEF 检测和诊断能力的潜力。
在一项回顾性队列研究中，我们使用应用于电子健康记录（EHR）的 NLP 管道来识别 2010 年至 2022 年间有临床 HF 诊断的患者。我们从 EHR 中收集了人口统计学、临床、超声心动图和结局数据。根据左心室射血分数（LVEF）对患者进行分类。那些 LVEF≥50%的患者根据他们是否有临床医生诊断的 HFpEF 进一步分类，如果没有，他们是否符合 ESC 诊断标准。结果在第二个独立中心得到验证。我们确定了 8606 名 HF 患者。在连续 3727 名 HF 患者中，有 LVEF≥50%的患者中，只有 8.3%有临床医生诊断的 HFpEF，而 75.4%符合 ESC 标准但没有正式诊断的 HFpEF。确诊为 HFpEF 的患者住院频率更高；然而，尽管 ESC 标准组的合并症较少，且急性心血管事件较少，但该组的 5 年死亡率更高。
这项研究表明，未诊断的 HFpEF 患者是一个高危群体，死亡率较高。使用 NLP 方法从 EHR 数据中识别出可能的 HFpEF 患者是可行的，这些患者可能会受益于专家临床审查，并补充使用诊断算法。
