Validation of Diagnostic Codes to Identify Glaucoma in Taiwan’s Claims Data: A Multi-Institutional Study

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2024-04-03 DOI:10.2147/clep.s443872

Pei-Ting Lu, Tsung-Hsien Tsai, Chi-Chun Lai, Lan-Hsin Chuang, Shih-Chieh Shao

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引用次数: 0

Abstract

Background: Healthcare databases play a crucial role in improving our understanding of glaucoma epidemiology, which is the leading cause of irreversible blindness globally. However, the accuracy of diagnostic codes used in these databases to detect glaucoma is still uncertain.
Aim: To assess the accuracy of ICD-9-CM and ICD-10-CM codes in identifying patients with glaucoma, including two distinct subtypes, primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG).
Methods: We analyzed electronic medical records data from a 2% random sample of patients who newly underwent visual field examination in Taiwan’s largest multi-institutional healthcare system from 2011 to 2020. The diagnosis of glaucoma was confirmed by two ophthalmologists, based on the glaucoma diagnostic criteria. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for ICD-9-CM codes 365.1X and 365.2X, and ICD-10-CM codes H4010X, H4011X, H4012X, H4020X, H4021X, H4022X, H4023X and H4024X for glaucoma were calculated.
Results: We randomly selected 821 patients (mean age: 56.9 years old; female: 50.5%) from the original cohort of 41,050 newly receiving visual field examination in the study. Among 464 cases with an ICD-9-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 86.5, 96.5, 91.9, and 90.9%, respectively. Among 357 cases with an ICD-10-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 87.0, 92.8, 92.2 and 87.9%, respectively. The accuracy of diagnostic codes to identify POAG and PACG remained consistent.
Conclusion: The diagnostic codes were highly reliable for identifying cases of glaucoma in Taiwan’s routine healthcare practice. These results provide confidence when using ICD-9-CM and ICD-10-CM codes to define glaucoma cases in healthcare database research in Taiwan.

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台湾索赔数据中识别青光眼的诊断代码验证：多机构研究

背景：青光眼是导致全球不可逆失明的主要原因，而医疗数据库在提高我们对青光眼流行病学的认识方面发挥着至关重要的作用。目的：评估 ICD-9-CM 和 ICD-10-CM 编码在识别青光眼患者（包括原发性开角型青光眼（POAG）和原发性闭角型青光眼（PACG）这两种不同亚型）方面的准确性：我们分析了 2011 年至 2020 年期间在台湾最大的多机构医疗系统中新接受视野检查的 2% 随机抽样患者的电子病历数据。青光眼的诊断由两名眼科医生根据青光眼诊断标准进行确认。计算了青光眼的 ICD-9-CM 编码 365.1X 和 365.2X，以及 ICD-10-CM 编码 H4010X、H4011X、H4012X、H4020X、H4021X、H4022X、H4023X 和 H4024X 的阳性预测值（PPV）、阴性预测值（NPV）、敏感性和特异性：我们从新接受视野检查的 41,050 名患者中随机抽取了 821 名患者（平均年龄：56.9 岁；女性：50.5%）。在 464 例带有 ICD-9-CM 青光眼代码的病例中，青光眼的敏感性、特异性、PPV 和 NPV 分别为 86.5%、96.5%、91.9% 和 90.9%。在 357 个有 ICD-10-CM 青光眼代码的病例中，青光眼的敏感性、特异性、PPV 和 NPV 分别为 87.0%、92.8%、92.2% 和 87.9%。诊断代码识别 POAG 和 PACG 的准确性保持一致：结论：在台湾的常规医疗实践中，诊断代码在识别青光眼病例方面具有很高的可靠性。这些结果为在台湾医疗数据库研究中使用ICD-9-CM和ICD-10-CM代码定义青光眼病例提供了信心。

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

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.