Radwa Ewaisha, Tifani L Flieth, Karl M Ness, Alicia Algeciras-Schimnich, Joshua A Bornhorst
{"title":"用于 phi 多分析化验的计算指数控制方法的性能特征与算法分析。","authors":"Radwa Ewaisha, Tifani L Flieth, Karl M Ness, Alicia Algeciras-Schimnich, Joshua A Bornhorst","doi":"10.1093/jalm/jfae110","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself.</p><p><strong>Methods: </strong>Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined.</p><p><strong>Results: </strong>Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements.</p><p><strong>Conclusions: </strong>An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.</p>","PeriodicalId":46361,"journal":{"name":"Journal of Applied Laboratory Medicine","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Characteristics of a Calculated Index Control Method for the phi Multianalyte Assay with Algorithmic Analysis.\",\"authors\":\"Radwa Ewaisha, Tifani L Flieth, Karl M Ness, Alicia Algeciras-Schimnich, Joshua A Bornhorst\",\"doi\":\"10.1093/jalm/jfae110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself.</p><p><strong>Methods: </strong>Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined.</p><p><strong>Results: </strong>Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements.</p><p><strong>Conclusions: </strong>An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.</p>\",\"PeriodicalId\":46361,\"journal\":{\"name\":\"Journal of Applied Laboratory Medicine\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Laboratory Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/jalm/jfae110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Laboratory Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jalm/jfae110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Performance Characteristics of a Calculated Index Control Method for the phi Multianalyte Assay with Algorithmic Analysis.
Background: Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself.
Methods: Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined.
Results: Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements.
Conclusions: An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.