Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases
Identifieur interne : 000733 ( Main/Exploration ); précédent : 000732; suivant : 000734Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases
Auteurs : Urmila Chandran ; Jenna Reps ; Paul E. Stang ; Patrick B. RyanSource :
- PLoS ONE [ 1932-6203 ] ; 2019.
Abstract
Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.
Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan® databases. The model was further validated in the Optum database across a range of patient cohorts.
In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).
A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.
Url:
DOI: 10.1371/journal.pone.0226255
PubMed: 31851711
PubMed Central: 6919633
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases</title><author><name sortKey="Chandran, Urmila" sort="Chandran, Urmila" uniqKey="Chandran U" first="Urmila" last="Chandran">Urmila Chandran</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Reps, Jenna" sort="Reps, Jenna" uniqKey="Reps J" first="Jenna" last="Reps">Jenna Reps</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Stang, Paul E" sort="Stang, Paul E" uniqKey="Stang P" first="Paul E." last="Stang">Paul E. Stang</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Ryan, Patrick B" sort="Ryan, Patrick B" uniqKey="Ryan P" first="Patrick B." last="Ryan">Patrick B. Ryan</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31851711</idno><idno type="pmc">6919633</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919633</idno><idno type="RBID">PMC:6919633</idno><idno type="doi">10.1371/journal.pone.0226255</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000779</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000779</idno><idno type="wicri:Area/Pmc/Curation">000779</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000779</idno><idno type="wicri:Area/Pmc/Checkpoint">000678</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000678</idno><idno type="wicri:Area/Ncbi/Merge">000D41</idno><idno type="wicri:Area/Ncbi/Curation">000D41</idno><idno type="wicri:Area/Ncbi/Checkpoint">000D41</idno><idno type="wicri:Area/Main/Merge">000733</idno><idno type="wicri:Area/Main/Curation">000733</idno><idno type="wicri:Area/Main/Exploration">000733</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases</title><author><name sortKey="Chandran, Urmila" sort="Chandran, Urmila" uniqKey="Chandran U" first="Urmila" last="Chandran">Urmila Chandran</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Reps, Jenna" sort="Reps, Jenna" uniqKey="Reps J" first="Jenna" last="Reps">Jenna Reps</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Stang, Paul E" sort="Stang, Paul E" uniqKey="Stang P" first="Paul E." last="Stang">Paul E. Stang</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author><author><name sortKey="Ryan, Patrick B" sort="Ryan, Patrick B" uniqKey="Ryan P" first="Patrick B." last="Ryan">Patrick B. Ryan</name><affiliation><nlm:aff id="aff001"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title><p>Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.</p></sec><sec id="sec002"><title>Methods</title><p>Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics<sup>®</sup> Extended DataMart (Optum) and additionally validated in three external IBM MarketScan<sup>®</sup> databases. The model was further validated in the Optum database across a range of patient cohorts.</p></sec><sec id="sec003"><title>Results</title><p>In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).</p></sec><sec id="sec004"><title>Conclusions</title><p>A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Bernatsky, S" uniqKey="Bernatsky S">S Bernatsky</name></author><author><name sortKey="Lix, L" uniqKey="Lix L">L Lix</name></author><author><name sortKey="O Donnell, S" uniqKey="O Donnell S">S O'Donnell</name></author><author><name sortKey="Lacaille, D" uniqKey="Lacaille D">D Lacaille</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Albrecht, K" uniqKey="Albrecht K">K Albrecht</name></author><author><name sortKey="Zink, A" uniqKey="Zink A">A Zink</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Avina Zubieta, Ja" uniqKey="Avina Zubieta J">JA Avina-Zubieta</name></author><author><name sortKey="Abrahamowicz, M" uniqKey="Abrahamowicz M">M Abrahamowicz</name></author><author><name sortKey="Cho, Hk" 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Patrick B" uniqKey="Ryan P" first="Patrick B." last="Ryan">Patrick B. 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