{"title":"提高诊断精度的战略途径:优化HBV和HCV病毒载量监测试剂盒的面板大小","authors":"Gauri Misra, Mahima Gupta, Richa Singhal, Sidra Qaisar, Priyanshi Singh, Anupkumar R. Anvikar","doi":"10.1002/jmv.70437","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>To effectively manage and treat infections caused by the hepatitis B (HBV) and hepatitis C virus (HCV), performing viral load monitoring using high-quality diagnostic kits is essential. First-time quality control evaluation, “performance evaluation” (PE), is critical to ensure its reliability. This depends on the quality and sample size used for quality control evaluation. This is the first study that attempts to optimize the sample size requirement for doing the PE of HBV and HCV viral load monitoring kits. The leftover plasma samples were characterized using closed systems. Various random subsets consisting <i>n</i> = 100, 80, 60, 40, 20, and 10 samples with low and high viral load ranges were tested to assess the accuracy, sensitivity, and specificity of the molecular diagnostic kits. Molecularly characterized panels (reference panel) and subsets exhibited reproducibility and strong concordance as compared with international standard. All the subsets of HBV and HCV correlated well with Pearson correlation coefficient (<i>r</i>) > 0.997 and demonstrated good agreement with an Intra-class correlation coefficient > 0.900 for all. The subset of <i>n</i> = 80, 60, and 40 for HBV and <i>n</i> = 10,20, and 40 for HCV, exhibited comparable proficiency. The tested subsets demonstrated high correlation and agreement, indicating that a reduced panel size can provide reliable PE of HBV and HCV viral load monitoring kits. Further studies, subjected to the availability of samples involving larger datasets and a broader range of testing conditions can streamline and strengthen the optimal panel size. This study will serve as a benchmark in clinical and diagnostic settings for testing of in vitro diagnostics, thus, strengthening the regulatory and quality control processes.</p></div>","PeriodicalId":16354,"journal":{"name":"Journal of Medical Virology","volume":"97 6","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strategic Approach to Enhancing Diagnostic Precision: Optimizing Panel Size for HBV and HCV Viral Load Monitoring Kits\",\"authors\":\"Gauri Misra, Mahima Gupta, Richa Singhal, Sidra Qaisar, Priyanshi Singh, Anupkumar R. Anvikar\",\"doi\":\"10.1002/jmv.70437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>To effectively manage and treat infections caused by the hepatitis B (HBV) and hepatitis C virus (HCV), performing viral load monitoring using high-quality diagnostic kits is essential. First-time quality control evaluation, “performance evaluation” (PE), is critical to ensure its reliability. This depends on the quality and sample size used for quality control evaluation. This is the first study that attempts to optimize the sample size requirement for doing the PE of HBV and HCV viral load monitoring kits. The leftover plasma samples were characterized using closed systems. Various random subsets consisting <i>n</i> = 100, 80, 60, 40, 20, and 10 samples with low and high viral load ranges were tested to assess the accuracy, sensitivity, and specificity of the molecular diagnostic kits. Molecularly characterized panels (reference panel) and subsets exhibited reproducibility and strong concordance as compared with international standard. All the subsets of HBV and HCV correlated well with Pearson correlation coefficient (<i>r</i>) > 0.997 and demonstrated good agreement with an Intra-class correlation coefficient > 0.900 for all. The subset of <i>n</i> = 80, 60, and 40 for HBV and <i>n</i> = 10,20, and 40 for HCV, exhibited comparable proficiency. The tested subsets demonstrated high correlation and agreement, indicating that a reduced panel size can provide reliable PE of HBV and HCV viral load monitoring kits. Further studies, subjected to the availability of samples involving larger datasets and a broader range of testing conditions can streamline and strengthen the optimal panel size. This study will serve as a benchmark in clinical and diagnostic settings for testing of in vitro diagnostics, thus, strengthening the regulatory and quality control processes.</p></div>\",\"PeriodicalId\":16354,\"journal\":{\"name\":\"Journal of Medical Virology\",\"volume\":\"97 6\",\"pages\":\"\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Virology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jmv.70437\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"VIROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Virology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jmv.70437","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"VIROLOGY","Score":null,"Total":0}
Strategic Approach to Enhancing Diagnostic Precision: Optimizing Panel Size for HBV and HCV Viral Load Monitoring Kits
To effectively manage and treat infections caused by the hepatitis B (HBV) and hepatitis C virus (HCV), performing viral load monitoring using high-quality diagnostic kits is essential. First-time quality control evaluation, “performance evaluation” (PE), is critical to ensure its reliability. This depends on the quality and sample size used for quality control evaluation. This is the first study that attempts to optimize the sample size requirement for doing the PE of HBV and HCV viral load monitoring kits. The leftover plasma samples were characterized using closed systems. Various random subsets consisting n = 100, 80, 60, 40, 20, and 10 samples with low and high viral load ranges were tested to assess the accuracy, sensitivity, and specificity of the molecular diagnostic kits. Molecularly characterized panels (reference panel) and subsets exhibited reproducibility and strong concordance as compared with international standard. All the subsets of HBV and HCV correlated well with Pearson correlation coefficient (r) > 0.997 and demonstrated good agreement with an Intra-class correlation coefficient > 0.900 for all. The subset of n = 80, 60, and 40 for HBV and n = 10,20, and 40 for HCV, exhibited comparable proficiency. The tested subsets demonstrated high correlation and agreement, indicating that a reduced panel size can provide reliable PE of HBV and HCV viral load monitoring kits. Further studies, subjected to the availability of samples involving larger datasets and a broader range of testing conditions can streamline and strengthen the optimal panel size. This study will serve as a benchmark in clinical and diagnostic settings for testing of in vitro diagnostics, thus, strengthening the regulatory and quality control processes.
期刊介绍:
The Journal of Medical Virology focuses on publishing original scientific papers on both basic and applied research related to viruses that affect humans. The journal publishes reports covering a wide range of topics, including the characterization, diagnosis, epidemiology, immunology, and pathogenesis of human virus infections. It also includes studies on virus morphology, genetics, replication, and interactions with host cells.
The intended readership of the journal includes virologists, microbiologists, immunologists, infectious disease specialists, diagnostic laboratory technologists, epidemiologists, hematologists, and cell biologists.
The Journal of Medical Virology is indexed and abstracted in various databases, including Abstracts in Anthropology (Sage), CABI, AgBiotech News & Information, National Agricultural Library, Biological Abstracts, Embase, Global Health, Web of Science, Veterinary Bulletin, and others.