从电子健康记录数据中发展偏侧特异性可计算表型，以治疗为保证的糖尿病黄斑水肿为例

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2025-04-16 DOI:10.1016/j.xops.2025.100797

Kaili Ding MBBS, MS , Tracy Z. Lang BS , Roberta McKean-Cowdin PhD , Hossein Ameri MD, PhD , Narsing A. Rao MD , Brian C. Toy MD

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引用次数: 0

摘要

目的开发一种利用结构化和非结构化电子健康记录（EHR）数据的通用算法，以在纵向眼水平更准确地识别侧边特异性治疗需要的疾病。设计一项回顾性治疗保证糖尿病黄斑水肿（TW-DME）队列研究。研究对象：2013年至2023年，从健康安全网系统和加利福尼亚州洛杉矶的一所大学医院确定的糖尿病视网膜病变（DR）患者，采用诊断和程序代码。方法基于以下5类数据：1级医师操作文件、2级收费代码（专业和设施）、3级用药单、4级交叉操作代码和5级与操作相关的诊断代码，我们调查了偏侧特异性TW-DME状态的完整性和准确性。横向数据完整性评估每个类别，独立和分层的层次顺序。数据的准确性通过人工图表审查验证患者的子集。主要结果测量：确定横截面和纵向TW-DME状态的算法性能。结果2013 - 2023年，7784例DR患者就诊68465次，其中4809例（61.8%）患者确诊为TW-DME。值得注意的是，67.9%的健康安全网患者就诊时未诊断出侧边性。提出的算法将治疗保证的DR队列的侧性完整性提高到93.6%的安全网和99.0%的大学站点。通过图表验证表明，纵向眼水平的阳性预测值（安全网47.0%-93.2%，大学85.3%-98.8%）、阴性预测值（安全网23.2%-33.3%，大学46.9%-72.6%）、敏感性（安全网35.9%-76.0%，大学79.2%-96.0%）、特异性（安全网60.4%-76.6%，大学38.8%-90.4%）、一致性（安全网38.5%-76.1%，大学74.8%-95.4%）和F1评分（安全网40.7%-83.7%，大学82.1%-97.4%）均有所增加。结论采用结构化和非结构化数据的sour算法为更准确地从电子病历中识别和提取横向数据提供了一种通用且可重复的方法。这种方法适用于具有不同文档和编码实践的站点。该算法的应用可以提高临床数据的实用性，这些数据作为常规护理的一部分，可用于未来眼科疾病患病率、后遗症、治疗模式和成本的调查。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。

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Developing Laterality-Specific Computable Phenotypes from Electronic Health Record Data, Employing Treatment-Warranted Diabetic Macular Edema as a Use Case

Purpose

To develop a general algorithm employing structured and unstructured electronic health record (EHR) data to identify laterality-specific treatment-warranted disease more accurately at the longitudinal eye level.

Design

A retrospective treatment-warranted diabetic macular edema (TW-DME) cohort study.

Subjects

Patients with diabetic retinopathy (DR) identified from a health safety net system and a university hospital in Los Angeles, California, employing diagnosis and procedure codes from 2013 to 2023.

Methods

We investigated the completeness and accuracy of laterality-specific TW-DME status based on the following 5 categories of data: Tier 1-Physician Procedure Documentation, Tier 2-Charge Codes (Professional and Facility), Tier 3-Medication Orders, Tier 4-Crosswalked Procedure Codes, and Tier 5-Diagnosis Code associated with Procedure. Laterality data completeness was evaluated for each category, independently and in a tiered hierarchical order. Data accuracy was verified by manual chart review for a subset of validation patients.

Main Outcome Measures

Algorithm performance in ascertaining cross-sectional and longitudinal TW-DME status.

Results

From 2013 to 2023, 7784 patients with DR had 68 465 visits, with 4809 (61.8%) patients identified as having TW-DME. Notably, 67.9% of health safety net patients had visits with missing diagnosis laterality. The proposed algorithm improved laterality completeness in the treatment-warranted DR cohort to 93.6% for the safety net and 99.0% for the university sites. Validation by chart review demonstrated an increase in positive predictive value (safety net 47.0%–93.2%, university 85.3%–98.8%), negative predictive value (safety net 23.2%–33.3%, university 46.9%–72.6%), sensitivity (safety net 35.9%–76.0%, university 79.2%–96.0%), specificity (safety net 60.4%–76.6%, university 38.8%–90.4%), agreement (safety net 38.5%–76.1%, university 74.8%–95.4%), and F1 score (safety net 40.7%–83.7%, university 82.1%–97.4%) at the longitudinal eye level.

Conclusions

Our algorithm employing structured and unstructured data lays out a general and reproducible approach to more accurately identify and extract laterality-specific data from EHRs. This method was valid across sites with disparate documentation and coding practices. Application of this algorithm could improve the utility of clinical data generated as part of routine care for future investigations of ocular disease prevalence, sequelae, treatment patterns, and costs.