Martin Skarzynski, Erin M McAuley, Ezekiel J Maier, Anthony C Fries, Jameson D Voss, Richard R Chapleau
{"title":"基于严重急性呼吸系统综合征冠状病毒2型基因组的严重性预测与较低的qPCR值和较高的病毒载量相对应","authors":"Martin Skarzynski, Erin M McAuley, Ezekiel J Maier, Anthony C Fries, Jameson D Voss, Richard R Chapleau","doi":"10.1155/2022/6499217","DOIUrl":null,"url":null,"abstract":"<p><p>The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be \"severe\" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the \"mild\" category (severity probability <0.5) had an average Ct of 20.4 (<i>P</i>=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (<i>r</i> = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (<i>n</i> = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (<i>n</i> = 350) (<i>P</i>=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.</p>","PeriodicalId":44052,"journal":{"name":"Global Health Epidemiology and Genomics","volume":"2022 1","pages":"6499217"},"PeriodicalIF":1.1000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9173902/pdf/","citationCount":"0","resultStr":"{\"title\":\"SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.\",\"authors\":\"Martin Skarzynski, Erin M McAuley, Ezekiel J Maier, Anthony C Fries, Jameson D Voss, Richard R Chapleau\",\"doi\":\"10.1155/2022/6499217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be \\\"severe\\\" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the \\\"mild\\\" category (severity probability <0.5) had an average Ct of 20.4 (<i>P</i>=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (<i>r</i> = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (<i>n</i> = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (<i>n</i> = 350) (<i>P</i>=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.</p>\",\"PeriodicalId\":44052,\"journal\":{\"name\":\"Global Health Epidemiology and Genomics\",\"volume\":\"2022 1\",\"pages\":\"6499217\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9173902/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Health Epidemiology and Genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/6499217\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Health Epidemiology and Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/6499217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.
The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.