Participant selection algorithm.
<div><p>Background</p><p>C-reactive protein (CRP) is a biomarker of inflammation used in diagnosis of inflammatory diseases and to guide treatment decisions. Variation in within-individual measured CRP may affect its clinical utility but estimates of within-individual variati...
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2025
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| _version_ | 1849927643781136384 |
|---|---|
| author | Alex Gough (16734033) |
| author2 | Alice Sitch (396379) Tom Marshall (154542) |
| author2_role | author author |
| author_facet | Alex Gough (16734033) Alice Sitch (396379) Tom Marshall (154542) |
| author_role | author |
| dc.creator.none.fl_str_mv | Alex Gough (16734033) Alice Sitch (396379) Tom Marshall (154542) |
| dc.date.none.fl_str_mv | 2025-11-24T18:21:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0337221.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Participant_selection_algorithm_/30696362 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Biotechnology Evolutionary Biology Immunology Cancer Mental Health Infectious Diseases severe disease status guide treatment decisions comorbidity covariates extracted xlink "> 472 retrospective cohort study patient median crp median reported cv xlink "> c performed using data individual measured variation xlink "> previously reported world data limited data crp data world nature whole population variation increases short half reactive protein primary care previous studies largest study large number inflammatory diseases inflammation used individual variation inclusion criterion important implications dexter tool database using cv increased clinical utility clinical decision approximately five acute illness 606 ). |
| dc.title.none.fl_str_mv | Participant selection algorithm. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Background</p><p>C-reactive protein (CRP) is a biomarker of inflammation used in diagnosis of inflammatory diseases and to guide treatment decisions. Variation in within-individual measured CRP may affect its clinical utility but estimates of within-individual variation are based on limited data and so may not be accurate.</p><p>Methods</p><p>A retrospective cohort study was performed using data on CRP results and sociodemographic, lifestyle and comorbidity covariates extracted from the IQVIA Medical Research Database (IMRD) database using the DEXTER tool. A minimum of four measurements for each individual was the only inclusion criterion. CRP data were log-transformed for analysis. Within-individual measured variation was calculated as a coefficient of variation (CV) using a linear regression random effects model for the whole population and various subgroups.</p><p>Results</p><p>472,811 participants were included in this study, making it the largest study of variation of CRP to date by a factor of approximately five. The overall coefficient of variation for CRP was 1.604 (95% CI 1.602 to 1.606). This is much higher than the median reported CV for CRP of previous studies which was 0.41. CV increased with patient median.</p><p>Strengths and limitations</p><p>The large number of participants and the real-world nature of the results are important strengths of this study. Weaknesses included the problem of accounting for confounding by indication, and the short half-life of CRP making it hard to distinguish between acute illness and physiological variation.</p><p>Conclusions</p><p>Estimated within-individual variation in this analysis of real-world data is very high and is higher than previously reported. Variation increases with patient median CRP, that is with more severe disease status. This has important implications for the diagnosis, monitoring and clinical decision-making for inflammatory disease.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_3cd0ec34655007efed996690d0ebf301 |
| identifier_str_mv | 10.1371/journal.pone.0337221.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30696362 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Participant selection algorithm.Alex Gough (16734033)Alice Sitch (396379)Tom Marshall (154542)BiochemistryBiotechnologyEvolutionary BiologyImmunologyCancerMental HealthInfectious Diseasessevere disease statusguide treatment decisionscomorbidity covariates extractedxlink "> 472retrospective cohort studypatient median crpmedian reported cvxlink "> cperformed using dataindividual measured variationxlink ">previously reportedworld datalimited datacrp dataworld naturewhole populationvariation increasesshort halfreactive proteinprimary careprevious studieslargest studylarge numberinflammatory diseasesinflammation usedindividual variationinclusion criterionimportant implicationsdexter tooldatabase usingcv increasedclinical utilityclinical decisionapproximately fiveacute illness606 ).<div><p>Background</p><p>C-reactive protein (CRP) is a biomarker of inflammation used in diagnosis of inflammatory diseases and to guide treatment decisions. Variation in within-individual measured CRP may affect its clinical utility but estimates of within-individual variation are based on limited data and so may not be accurate.</p><p>Methods</p><p>A retrospective cohort study was performed using data on CRP results and sociodemographic, lifestyle and comorbidity covariates extracted from the IQVIA Medical Research Database (IMRD) database using the DEXTER tool. A minimum of four measurements for each individual was the only inclusion criterion. CRP data were log-transformed for analysis. Within-individual measured variation was calculated as a coefficient of variation (CV) using a linear regression random effects model for the whole population and various subgroups.</p><p>Results</p><p>472,811 participants were included in this study, making it the largest study of variation of CRP to date by a factor of approximately five. The overall coefficient of variation for CRP was 1.604 (95% CI 1.602 to 1.606). This is much higher than the median reported CV for CRP of previous studies which was 0.41. CV increased with patient median.</p><p>Strengths and limitations</p><p>The large number of participants and the real-world nature of the results are important strengths of this study. Weaknesses included the problem of accounting for confounding by indication, and the short half-life of CRP making it hard to distinguish between acute illness and physiological variation.</p><p>Conclusions</p><p>Estimated within-individual variation in this analysis of real-world data is very high and is higher than previously reported. Variation increases with patient median CRP, that is with more severe disease status. This has important implications for the diagnosis, monitoring and clinical decision-making for inflammatory disease.</p></div>2025-11-24T18:21:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0337221.g001https://figshare.com/articles/figure/Participant_selection_algorithm_/30696362CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306963622025-11-24T18:21:59Z |
| spellingShingle | Participant selection algorithm. Alex Gough (16734033) Biochemistry Biotechnology Evolutionary Biology Immunology Cancer Mental Health Infectious Diseases severe disease status guide treatment decisions comorbidity covariates extracted xlink "> 472 retrospective cohort study patient median crp median reported cv xlink "> c performed using data individual measured variation xlink "> previously reported world data limited data crp data world nature whole population variation increases short half reactive protein primary care previous studies largest study large number inflammatory diseases inflammation used individual variation inclusion criterion important implications dexter tool database using cv increased clinical utility clinical decision approximately five acute illness 606 ). |
| status_str | publishedVersion |
| title | Participant selection algorithm. |
| title_full | Participant selection algorithm. |
| title_fullStr | Participant selection algorithm. |
| title_full_unstemmed | Participant selection algorithm. |
| title_short | Participant selection algorithm. |
| title_sort | Participant selection algorithm. |
| topic | Biochemistry Biotechnology Evolutionary Biology Immunology Cancer Mental Health Infectious Diseases severe disease status guide treatment decisions comorbidity covariates extracted xlink "> 472 retrospective cohort study patient median crp median reported cv xlink "> c performed using data individual measured variation xlink "> previously reported world data limited data crp data world nature whole population variation increases short half reactive protein primary care previous studies largest study large number inflammatory diseases inflammation used individual variation inclusion criterion important implications dexter tool database using cv increased clinical utility clinical decision approximately five acute illness 606 ). |