Austin Williams, William Zach Webster, Chao Cai, Alexander Milgrom, Majdi Al-Hasan, P Brandon Bookstaver
{"title":"评估元基因组下一代测序检测对感染宿主病原体鉴定的诊断效用:一项回顾性队列研究。","authors":"Austin Williams, William Zach Webster, Chao Cai, Alexander Milgrom, Majdi Al-Hasan, P Brandon Bookstaver","doi":"10.1177/20499361241232854","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Metagenomic next-generation sequencing (mNGS) testing identifies thousands of potential pathogens in a single blood test, though data on its real-world diagnostic utility are lacking.</p><p><strong>Objectives: </strong>Determine the diagnostic utility of mNGS testing in practice and factors associated with high clinical utility.</p><p><strong>Design: </strong>Retrospective cohort study of mNGS tests ordered from June 2018 through May 2020 at a community teaching hospital.</p><p><strong>Methods: </strong>Tests were included if ordered for diagnostic purposes in patients with probable or high clinical suspicion of infection. Exclusions included patient expiration, hospice care, or transfer outside of the institution. Utility criteria were established a priori by the research team. Two investigators independently reviewed each test and categorized it to either high or low diagnostic utility. Reviewer discordance was referred to a third investigator. The stepwise multiple regression method was used to identify clinical factors associated with high diagnostic utility.</p><p><strong>Results: </strong>Among 96 individual tests from 82 unique patients, 80 tests met the inclusion criteria for analysis. At least one potential pathogen was identified in 58% of tests. Among 112 pathogens identified, there were 74 bacteria, 25 viruses, 12 fungi, and 1 protozoon. In all, 46 tests (57.5%) were determined to be of high diagnostic utility. Positive mNGS tests were identified in 36 (78.3%) and 11 (32.4%) of high and low diagnostic utility tests, respectively (<i>p</i> < 0.001). Antimicrobials were changed after receiving test results in 31 (67.4%) of high utility tests and 4 (11.8%) of low utility tests (<i>p</i> < 0.0001). In the multiple regression model, a positive test [odds ratio (OR) = 10.9; 95% confidence interval (CI), 3.2-44.4] and consultation with the company medical director (OR = 3.6; 95% CI, 1.1-13.7) remained significantly associated with high diagnostic utility.</p><p><strong>Conclusion: </strong>mNGS testing resulted in high clinical utility in most cases. Positive mNGS tests were associated with high diagnostic utility. Consultation with the Karius<sup>®</sup> medical director is recommended to maximize utility.</p>","PeriodicalId":46154,"journal":{"name":"Therapeutic Advances in Infectious Disease","volume":"11 ","pages":"20499361241232854"},"PeriodicalIF":3.8000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10893884/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluation of the diagnostic utility of metagenomic next-generation sequencing testing for pathogen identification in infected hosts: a retrospective cohort study.\",\"authors\":\"Austin Williams, William Zach Webster, Chao Cai, Alexander Milgrom, Majdi Al-Hasan, P Brandon Bookstaver\",\"doi\":\"10.1177/20499361241232854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Metagenomic next-generation sequencing (mNGS) testing identifies thousands of potential pathogens in a single blood test, though data on its real-world diagnostic utility are lacking.</p><p><strong>Objectives: </strong>Determine the diagnostic utility of mNGS testing in practice and factors associated with high clinical utility.</p><p><strong>Design: </strong>Retrospective cohort study of mNGS tests ordered from June 2018 through May 2020 at a community teaching hospital.</p><p><strong>Methods: </strong>Tests were included if ordered for diagnostic purposes in patients with probable or high clinical suspicion of infection. Exclusions included patient expiration, hospice care, or transfer outside of the institution. Utility criteria were established a priori by the research team. Two investigators independently reviewed each test and categorized it to either high or low diagnostic utility. Reviewer discordance was referred to a third investigator. The stepwise multiple regression method was used to identify clinical factors associated with high diagnostic utility.</p><p><strong>Results: </strong>Among 96 individual tests from 82 unique patients, 80 tests met the inclusion criteria for analysis. At least one potential pathogen was identified in 58% of tests. Among 112 pathogens identified, there were 74 bacteria, 25 viruses, 12 fungi, and 1 protozoon. In all, 46 tests (57.5%) were determined to be of high diagnostic utility. Positive mNGS tests were identified in 36 (78.3%) and 11 (32.4%) of high and low diagnostic utility tests, respectively (<i>p</i> < 0.001). Antimicrobials were changed after receiving test results in 31 (67.4%) of high utility tests and 4 (11.8%) of low utility tests (<i>p</i> < 0.0001). In the multiple regression model, a positive test [odds ratio (OR) = 10.9; 95% confidence interval (CI), 3.2-44.4] and consultation with the company medical director (OR = 3.6; 95% CI, 1.1-13.7) remained significantly associated with high diagnostic utility.</p><p><strong>Conclusion: </strong>mNGS testing resulted in high clinical utility in most cases. Positive mNGS tests were associated with high diagnostic utility. Consultation with the Karius<sup>®</sup> medical director is recommended to maximize utility.</p>\",\"PeriodicalId\":46154,\"journal\":{\"name\":\"Therapeutic Advances in Infectious Disease\",\"volume\":\"11 \",\"pages\":\"20499361241232854\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10893884/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Therapeutic Advances in Infectious Disease\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/20499361241232854\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Therapeutic Advances in Infectious Disease","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/20499361241232854","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Evaluation of the diagnostic utility of metagenomic next-generation sequencing testing for pathogen identification in infected hosts: a retrospective cohort study.
Background: Metagenomic next-generation sequencing (mNGS) testing identifies thousands of potential pathogens in a single blood test, though data on its real-world diagnostic utility are lacking.
Objectives: Determine the diagnostic utility of mNGS testing in practice and factors associated with high clinical utility.
Design: Retrospective cohort study of mNGS tests ordered from June 2018 through May 2020 at a community teaching hospital.
Methods: Tests were included if ordered for diagnostic purposes in patients with probable or high clinical suspicion of infection. Exclusions included patient expiration, hospice care, or transfer outside of the institution. Utility criteria were established a priori by the research team. Two investigators independently reviewed each test and categorized it to either high or low diagnostic utility. Reviewer discordance was referred to a third investigator. The stepwise multiple regression method was used to identify clinical factors associated with high diagnostic utility.
Results: Among 96 individual tests from 82 unique patients, 80 tests met the inclusion criteria for analysis. At least one potential pathogen was identified in 58% of tests. Among 112 pathogens identified, there were 74 bacteria, 25 viruses, 12 fungi, and 1 protozoon. In all, 46 tests (57.5%) were determined to be of high diagnostic utility. Positive mNGS tests were identified in 36 (78.3%) and 11 (32.4%) of high and low diagnostic utility tests, respectively (p < 0.001). Antimicrobials were changed after receiving test results in 31 (67.4%) of high utility tests and 4 (11.8%) of low utility tests (p < 0.0001). In the multiple regression model, a positive test [odds ratio (OR) = 10.9; 95% confidence interval (CI), 3.2-44.4] and consultation with the company medical director (OR = 3.6; 95% CI, 1.1-13.7) remained significantly associated with high diagnostic utility.
Conclusion: mNGS testing resulted in high clinical utility in most cases. Positive mNGS tests were associated with high diagnostic utility. Consultation with the Karius® medical director is recommended to maximize utility.