基于国际疾病分类代码的算法验证研究：从 2020 年到 2022 年，用于识别日本冠状病毒病患者：VENUS 研究。

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.

机构信息

Section of Clinical Epidemiology, Department of Data Science, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.

Department of Health Care Administration and Management, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan.

出版信息

Pharmacoepidemiol Drug Saf. 2024 Nov;33(11):e70032. doi: 10.1002/pds.70032.

DOI:10.1002/pds.70032

PMID:39449609

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.

摘要

目的

我们使用基于国际疾病分类第十版（ICD-10）的病例报告算法来确定 2020 年 5 月至 2022 年 8 月期间首次出现的新型冠状病毒病（COVID-19）患者。

方法

研究队列由参加公共保险计划的一个市的居民组成。本研究使用了该市提供的数据，包括居民基于保险的医疗报销数据，这些数据与健康中心实时信息共享系统（HER-SYS）相关联。HER-SYS 数据包括 COVID-19 检测的阳性结果，并作为参考标准。病例报告算法 #1 和 #2 分别为 U07.1 和 B34.2，分别伴有和不伴有可疑诊断。病例报告算法 #3 和 #4 分别为 U07.1，伴有和不伴有可疑诊断。计算了每种算法的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。

结果

研究队列包括 165038 名居民，其中 13402 名居民为参考标准。在整个时期，算法 #1 的敏感性、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%），算法 #2 的敏感性、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 97.8%（97.7%-97.8%），算法 #3 的敏感性、特异性、PPV 和 NPV 分别为 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 的敏感性、特异性、PPV 和 NPV 分别为 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%）。