Validation Study of the Claims-Based Algorithm Using the International Classification of Diseases Codes to Identify Patients With Coronavirus Disease in Japan From 2020 to 2022: The VENUS Study.

IF 2.4 4区医学 Q3 PHARMACOLOGY & PHARMACY Pharmacoepidemiology and Drug Safety Pub Date : 2024-11-01 DOI:10.1002/pds.70032

Taku Chikamochi, Chieko Ishiguro, Wataru Mimura, Megumi Maeda, Fumiko Murata, Haruhisa Fukuda

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Abstract

Purpose: We validated claims-based algorithms using the International Classification of Diseases, Tenth Revision (ICD-10) to identify patients with the first-ever coronavirus disease (COVID-19) onset between May 2020 and August 2022.

Methods: The study cohort was comprised of residents of one municipality enrolled in a public insurance program. This study used data provided by the municipality, including residents' insurer-based medical claims data linked to the Health Center Real-time Information-Sharing System (HER-SYS). The HER-SYS data included positive results from COVID-19 tests and were used as reference standards. Claims-based algorithms #1 and #2 were U07.1, B34.2, with and without suspicious diagnoses, respectively. Claims-based algorithms #3 and #4 were U07.1 with and without suspicious diagnoses, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm.

Results: The study cohort included 165 038 residents, including 13 402 residents were the reference standard. For the entire period, the sensitivity, specificity, PPV, and NPV were 55.7% (95% confidence interval: 54.8%-56.5%), 65.4% (65.2%-65.6%), 11.5% (11.3%-11.8%), and 98.9% (98.8%-99.0%) for Algorithm #1, and 67.0% (66.2%-67.8%), 88.1% (87.9%-88.3%), 31.6% (31.1%-32.2%), and 97.8% (97.7%-97.8%) for Algorithm #2, and 52.9% (52.0%-53.7%), 67.1% (66.9%-67.3%), 11.5% (11.2%-11.8%), and 98.3% (98.3%-98.4%) for Algorithm #3, 62.6% (61.8%-63.4%), 88.5% (88.3%-88.7%), 30.9% (30.3%-31.4%), and 97.3% (97.2%-97.4%) for Algorithm #4, respectively.

Conclusions: Our study showed that the validity of claims-based algorithms consisting of COVID-19-related ICD-10 codes to identify patients with first-onset COVID-19 is limited.

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利用国际疾病分类代码识别 2020 年至 2022 年日本冠状病毒病患的索赔算法验证研究：VENUS 研究。

目的：我们利用《国际疾病分类》第十版（ICD-10）验证了基于索赔的算法，以确定 2020 年 5 月至 2022 年 8 月间首次发病的冠状病毒疾病（COVID-19）患者：研究队列由某市参加公共保险计划的居民组成。本研究使用了该市提供的数据，包括与健康中心实时信息共享系统（HER-SYS）相连接的居民基于保险公司的医疗理赔数据。HER-SYS 数据包括 COVID-19 检测的阳性结果，并被用作参考标准。基于索赔的算法 #1 和 #2 分别为 U07.1、B34.2，有可疑诊断和无可疑诊断。基于索赔的算法 #3 和 #4 分别为 U07.1，有可疑诊断和无可疑诊断。计算了每种算法的灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV）：研究对象包括 165 038 名住院医师，其中 13 402 名住院医师为参照标准。在整个研究期间，1 号算法的灵敏度、特异性、PPV 和 NPV 分别为 55.7%（95% 置信区间：54.8%-56.5%）、65.4%（65.2%-65.6%）、11.5%（11.3%-11.8%）和 98.9%（98.8%-99.0%），而 1 号算法的灵敏度、特异性、PPV 和 NPV 分别为 67.0%（66.2%-67.8%）、88.1%（87.9%-88.3%）、31.6%（31.1%-32.2%）和 97.算法＃2 为 8%（97.7%-97.8%），算法＃3 为 52.9%（52.0%-53.7%）、67.1%（66.9%-67.3%）、11.5%（11.2%-11.8%）和 98.3%（98.3%-98.4%），算法＃4 为 62.6%（61.8%-63.4%）、88.5%（88.3%-88.7%）、30.9%（30.3%-31.4%）和 97.3%（97.2%-97.4%）：我们的研究表明，以索赔为基础、由 COVID-19 相关 ICD-10 代码组成的算法识别首次发病 COVID-19 患者的有效性是有限的。

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

Pharmacoepidemiology and Drug Safety 医学-药学

CiteScore

4.80

自引率

7.70%

发文量

173

审稿时长

3 months

期刊介绍： The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report. Particular areas of interest include: design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology; comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world; methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology; assessments of harm versus benefit in drug therapy; patterns of drug utilization; relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines; evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.