Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction

IF 3.7 2区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

American heart journal Pub Date : 2024-09-07 DOI:10.1016/j.ahj.2024.09.001

Matthew T. Mefford PhD , Andrew P. Ambrosy MD , Rong Wei MS , Chengyi Zheng PhD , Rishi V. Parikh MPH , Teresa N. Harrison SM , Ming-Sum Lee MD , Alan S. Go MD , Kristi Reynolds PhD

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引用次数: 0

Abstract

Background

Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impact of incorporating unstructured data to identify WHFE, describing age-, sex-, race and ethnicity-, and left ventricular ejection fraction (LVEF)-specific rates.

Methods

Adult members of Kaiser Permanente Southern California (KPSC) with a HF diagnosis between 2014 and 2018 were followed through 2019 to identify hospitalized WHFE. The main outcome was hospitalizations with a principal or secondary HF discharge diagnosis meeting rule-based Natural Language Processing (NLP) criteria for WHFE. In comparison, we examined hospitalizations with a principal discharge diagnosis of HF. Age-, sex-, and race and ethnicity-adjusted rates per 100 person-years (PY) were calculated among age, sex, race and ethnicity (non-Hispanic (NH) Asian/Pacific Islander [API], Hispanic, NH Black, NH White) and LVEF subgroups.

Results

Among 44,863 adults with HF, 10,560 (23.5%) had an NLP-defined, hospitalized WHFE. Adjusted rates (per 100 PY) of WHFE using NLP were higher compared to rates based only on HF principal discharge diagnosis codes (12.7 and 9.3, respectively), and this followed similar patterns among subgroups, with the highest rates among adults ≥75 years (16.3 and 11.2), men (13.2 and 9.7), and NH Black (16.9 and 14.3) and Hispanic adults (15.3 and 11.4), and adults with reduced LVEF (16.2 and 14.0). Using NLP disproportionately increased the perceived burden of WHFE among API and adults with mid-range and preserved LVEF.

Conclusion

Rule-based NLP improved the capture of hospitalized WHFE above principal discharge diagnosis codes alone. Applying standardized consensus definitions to EHR data may improve understanding of the burden of WHFE and promote optimal care overall and in specific sociodemographic groups.

查看原文本刊更多论文

通过基于规则的自然语言处理，研究不同年龄、性别、种族和民族以及左心室射血分数对心力衰竭恶化住院情况的影响。

背景：之前关于心衰恶化事件（WHFE）特征的研究仅限于使用住院治疗的结构化医疗数据，而且很少探讨社会人口学差异。本研究考察了纳入非结构化数据对识别心衰事件的影响，描述了年龄、性别、种族和民族以及左心室射血分数（LVEF）特异性比率。主要结果是主要或次要 HF 出院诊断符合基于规则的自然语言处理 (NLP) WHFE 标准的住院情况。相比之下，我们检查了主要出院诊断为 HF 的住院情况。在年龄、性别、种族和民族（非西班牙裔（NH）亚太岛民 [API]、西班牙裔、NH 黑人、NH 白人）以及 LVEF 亚组中，我们计算了经年龄、性别、种族和民族调整的每百人年（PY）发病率。结果在 44,863 名成人高频患者中，10,560 人（23.5%）有 NLP 定义的 WHFE 住院病例。与仅基于心房颤动主要出院诊断代码的 WHFE 率（分别为 12.7 和 9.3）相比，使用 NLP 的调整后 WHFE 率（每 100 PY）更高，而且亚组之间的模式相似，年龄≥75 岁的成人（16.3 和 11.2）、男性（13.2 和 9.7）、新罕布什尔州黑人（16.9 和 14.3）和西班牙裔成人（15.3 和 11.4）以及 LVEF 降低的成人（16.2 和 14.0）的 WHFE 率最高。结论基于规则的 NLP 提高了对住院 WHFE 的捕获率，超过了单纯的主要出院诊断代码。将标准化的共识定义应用于电子病历数据可提高对 WHFE 负担的认识，促进整体和特定社会人口群体的最佳护理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

American heart journal 医学-心血管系统

CiteScore

8.20

自引率

2.10%

发文量

214

审稿时长

38 days

期刊介绍： The American Heart Journal will consider for publication suitable articles on topics pertaining to the broad discipline of cardiovascular disease. Our goal is to provide the reader primary investigation, scholarly review, and opinion concerning the practice of cardiovascular medicine. We especially encourage submission of 3 types of reports that are not frequently seen in cardiovascular journals: negative clinical studies, reports on study designs, and studies involving the organization of medical care. The Journal does not accept individual case reports or original articles involving bench laboratory or animal research.