Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Department of Cardiology, Los Angeles Medical Center, Kaiser Permanente Southern California, Pasadena, CA, United States.
JMIR Cardio. 2024 Sep 30;8:e60503. doi: 10.2196/60503.
Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are incomplete.
This study aimed to develop a natural language processing (NLP) algorithm to identify patients with aortic, mitral, tricuspid, and pulmonic valve stenosis and regurgitation from transthoracic echocardiography (TTE) reports within a large integrated health care system.
We used reports from echocardiograms performed in the Kaiser Permanente Southern California (KPSC) health care system between January 1, 2011, and December 31, 2022. Related terms/phrases of aortic, mitral, tricuspid, and pulmonic stenosis and regurgitation and their severities were compiled from the literature and enriched with input from clinicians. An NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review, followed by adjudication. The developed algorithm was applied to 200 annotated echocardiography reports to assess its performance and then the study echocardiography reports.
A total of 1,225,270 TTE reports were extracted from KPSC electronic health records during the study period. In these reports, valve lesions identified included 111,300 (9.08%) aortic stenosis, 20,246 (1.65%) mitral stenosis, 397 (0.03%) tricuspid stenosis, 2585 (0.21%) pulmonic stenosis, 345,115 (28.17%) aortic regurgitation, 802,103 (65.46%) mitral regurgitation, 903,965 (73.78%) tricuspid regurgitation, and 286,903 (23.42%) pulmonic regurgitation. Among the valves, 50,507 (4.12%), 22,656 (1.85%), 1685 (0.14%), and 1767 (0.14%) were identified as prosthetic aortic valves, mitral valves, tricuspid valves, and pulmonic valves, respectively. Mild and moderate were the most common severity levels of heart valve stenosis, while trace and mild were the most common severity levels of regurgitation. Males had a higher frequency of aortic stenosis and all 4 valvular regurgitations, while females had more mitral, tricuspid, and pulmonic stenosis. Non-Hispanic Whites had the highest frequency of all 4 valvular stenosis and regurgitations. The distribution of valvular stenosis and regurgitation severity was similar across race/ethnicity groups. Frequencies of aortic stenosis, mitral stenosis, and regurgitation of all 4 heart valves increased with age. In TTE reports with stenosis detected, younger patients were more likely to have mild aortic stenosis, while older patients were more likely to have severe aortic stenosis. However, mitral stenosis was opposite (milder in older patients and more severe in younger patients). In TTE reports with regurgitation detected, younger patients had a higher frequency of severe/very severe aortic regurgitation. In comparison, older patients had higher frequencies of mild aortic regurgitation and severe mitral/tricuspid regurgitation. Validation of the NLP algorithm against the 200 annotated TTE reports showed excellent precision, recall, and F1-scores.
The proposed computerized algorithm could effectively identify heart valve stenosis and regurgitation, as well as the severity of valvular involvement, with significant implications for pharmacoepidemiological studies and outcomes research.
瓣膜性心脏病（VHD）是导致心血管发病率和死亡率的主要原因，给医疗保健系统带来了巨大的医疗和经济负担。确定 VHD 诊断的行政诊断代码并不完整。
本研究旨在开发一种自然语言处理（NLP）算法，以识别大型综合医疗保健系统中经胸超声心动图（TTE）报告中的主动脉瓣、二尖瓣、三尖瓣和肺动脉瓣狭窄和反流患者。
我们使用了 2011 年 1 月 1 日至 2022 年 12 月 31 日期间在 Kaiser Permanente Southern California（KPSC）医疗保健系统进行的超声心动图报告。主动脉瓣、二尖瓣、三尖瓣和肺动脉瓣狭窄和反流及其严重程度的相关术语/短语是从文献中编译的，并通过临床医生的输入进行了丰富。通过多次图表审查和裁决，迭代开发和精细训练了 NLP 算法。该开发的算法应用于 200 份标注的超声心动图报告，以评估其性能，然后应用于研究超声心动图报告。
在研究期间，从 KPSC 电子健康记录中提取了 1225270 份 TTE 报告。在这些报告中，确定的瓣膜病变包括 111300 例（9.08%）主动脉瓣狭窄、20246 例（1.65%）二尖瓣狭窄、397 例（0.03%）三尖瓣狭窄、2585 例（0.21%）肺动脉瓣狭窄、345115 例（28.17%）主动脉瓣反流、802103 例（65.46%）二尖瓣反流、903965 例（73.78%）三尖瓣反流和 286903 例（23.42%）肺动脉瓣反流。在这些瓣膜中，50507 例（4.12%）、22656 例（1.85%）、1685 例（0.14%）和 1767 例（0.14%）分别被鉴定为人工主动脉瓣、二尖瓣、三尖瓣和肺动脉瓣。心脏瓣膜狭窄的最常见严重程度级别是轻度和中度，而反流的最常见严重程度级别是微量和轻度。男性主动脉瓣狭窄和所有 4 种瓣膜反流的发生率较高，而女性二尖瓣、三尖瓣和肺动脉瓣狭窄的发生率较高。非西班牙裔白人的所有 4 种瓣膜狭窄和反流的发生率最高。瓣膜狭窄和反流严重程度的分布在种族/族裔群体中相似。主动脉瓣狭窄、二尖瓣狭窄和所有 4 种心脏瓣膜反流的严重程度随着年龄的增长而增加。在检测到狭窄的 TTE 报告中，年轻患者更可能患有轻度主动脉瓣狭窄，而年龄较大的患者更可能患有严重的主动脉瓣狭窄。然而，二尖瓣狭窄则相反（年龄较大的患者较轻，年龄较小的患者较重）。在检测到反流的 TTE 报告中，年轻患者主动脉瓣反流严重/非常严重的频率较高。相比之下，年龄较大的患者主动脉瓣反流轻度和二尖瓣/三尖瓣反流严重的频率较高。该 NLP 算法与 200 份标注的 TTE 报告的验证结果显示出优异的精度、召回率和 F1 评分。
该计算机算法可有效识别心脏瓣膜狭窄和反流以及瓣膜受累的严重程度，对药物流行病学研究和结果研究具有重要意义。
