Validating ICD-10 Diagnosis Codes for Guillain-Barré Syndrome in Taiwan's National Health Insurance Claims Database.

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Clinical Epidemiology Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.2147/CLEP.S485953

Cheng-Yang Hsieh, Po-Ting Chen, Shih-Chieh Shao, Swu-Jane Lin, Shu-Chen Liao, Edward Chia-Cheng Lai

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Abstract

Purpose: To validate the International Classification of Diseases, 10th Revision (ICD-10) codes for Guillain-Barré syndrome (GBS) in Taiwan's insurance claims database.

Methods: We identified adult patients hospitalized at any Chang Gung Memorial Foundation branch hospital between January 1st, 2017, and December 31st, 2022, with ICD-10 code G61.0 in any of the five discharge diagnosis positions, indicating possible Guillain-Barré syndrome. We then validated the possible GBS diagnosis using data from electronic medical records of the identified patients, based on the diagnostic criteria established by the National Institute of Neurological Disorders and Stroke. We determined the positive predictive values (PPV) of various operational definitions, including the position (primary or other) where the code was recorded in the discharge diagnosis, nerve conduction study (NCS) claims, and / or specific GBS treatments.

Results: The final validation cohort of 484 patients with ICD-10 code for GBS in the discharge diagnosis was found to include 368 true GBS patients. Identifying inpatients using only the ICD-10 code for GBS in any of the five positions for discharge diagnosis yielded a PPV of 76.0%. With more restrictive definitions (primary diagnosis only, or requiring additional claims for NCS and/or treatments), the PPV tended to increase, but with fewer true GBS patients identified. Using ICD-10 GBS code in the primary diagnosis plus NCS and treatment claims yielded the highest PPV (98.3%); however, 140 (38.0%) of the true GBS patients were missed using this definition. In contrast, using the ICD-10 GBS code in any position, plus claims for NCS, achieved a relatively good PPV (85.8%) with minimal loss of true GBS patients (13, ie, 3.5%).

Conclusion: In Taiwan's NHI claims data, identifying true GBS patients using only the ICD-10 code yielded a PPV of 76.0%; however, adding claims for diagnostic procedure and GBS treatment increased the PPV to 98.3%.

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验证台湾国民健康保险理赔数据库中有关吉兰-巴雷综合征的 ICD-10 诊断代码。

目的：验证台湾保险理赔数据库中吉兰-巴雷综合征（GBS）的国际疾病分类第十版（ICD-10）代码：我们确定了 2017 年 1 月 1 日至 2022 年 12 月 31 日期间在任何一家长庚纪念基金会分院住院的成人患者，这些患者在五个出院诊断位置中的任何一个位置都有 ICD-10 代码 G61.0，表示可能患有格林-巴利综合征。然后，我们根据美国国立神经疾病和中风研究所制定的诊断标准，利用已确定患者的电子病历数据验证了可能的吉兰-巴雷综合征诊断。我们确定了各种操作定义的阳性预测值（PPV），包括出院诊断中记录代码的位置（主要或其他）、神经传导研究（NCS）索赔以及/或特定的 GBS 治疗方法：在出院诊断中带有 GBS ICD-10 编码的 484 名患者中，最终验证队列包括 368 名真正的 GBS 患者。仅使用出院诊断中五个位置中任何一个位置的 ICD-10 编码识别住院患者，PPV 为 76.0%。随着定义的限制性增加（仅限于主要诊断，或需要额外的 NCS 和/或治疗索赔），PPV 有增加的趋势，但真正的 GBS 患者却减少了。在主要诊断加上 NCS 和治疗索赔中使用 ICD-10 GBS 代码的 PPV 值最高（98.3%）；但是，使用该定义遗漏了 140 例（38.0%）真正的 GBS 患者。相比之下，在任何位置使用 ICD-10 GBS 代码，再加上 NCS 索赔，则可获得相对较好的 PPV（85.8%），而真正的 GBS 患者的损失却很小（13 例，即 3.5%）：结论：在台湾的国民健康保险理赔数据中，仅使用 ICD-10 编码识别真正的 GBS 患者的 PPV 为 76.0%；但如果加上诊断程序和 GBS 治疗的理赔，PPV 则可提高到 98.3%。

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.