Generating and evaluating a propensity model using textual features from electronic medical records
Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructu...
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| Vydáno v: | PLOS ONE Ročník 14; číslo 3; s. e0212999 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science (PLoS)
04.03.2019
Public Library of Science |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding.
In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users.
The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex.
PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. |
|---|---|
| AbstractList | Background Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. Methods In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users. Results The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex. Conclusions PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users. The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex. PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. Background Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. Methods In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users. Results The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18–1.36). Matching only on age resulted in an HR of 0.36 (0.11–1.16), and of 0.35 (0.11–1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13–1.38), which reduced to 0.35 (0.96–1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13–1.39), which dropped to 0.31 (0.09–1.08) after adjustment for age and sex. Conclusions PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users. The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex. PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding.BACKGROUNDPropensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding.In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users.METHODSIn a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated Primary Care Information (IPCI) database, we identified all patients who experienced an upper gastrointestinal bleeding (UGIB). We used a large-scale regularized regression to fit two PS models using all structured and unstructured information in the EHR. We calculated hazard ratios (HRs) to estimate the risk of UGIB among selective cyclo-oxygenase-2 (COX-2) inhibitor users compared to nonselective NSAID (nsNSAID) users.The crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex.RESULTSThe crude hazard ratio of UGIB for COX-2 inhibitors compared to nsNSAIDs was 0.50 (95% confidence interval 0.18-1.36). Matching only on age resulted in an HR of 0.36 (0.11-1.16), and of 0.35 (0.11-1.11) when further adjusted for sex. Matching on PS only, the first model yielded an HR of 0.42 (0.13-1.38), which reduced to 0.35 (0.96-1.25) when adjusted for age and sex. The second model resulted in an HR of 0.42 (0.13-1.39), which dropped to 0.31 (0.09-1.08) after adjustment for age and sex.PS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates.CONCLUSIONSPS models can be created using unstructured information in EHRs. An incremental benefit was observed by matching on PS over traditional matching and adjustment for covariates. |
| Audience | Academic |
| Author | Jan A. Kors Martijn J. Schuemie Gwen M. C. Masclee Zubair Afzal Miriam C. J. M. Sturkenboom |
| AuthorAffiliation | University of Oxford, UNITED KINGDOM 2 Janssen Research and Development LLC, Titusville, NJ, United States of America 1 Department of Medical Informatics, Erasmus University Medical Center, CA Rotterdam, Netherlands |
| AuthorAffiliation_xml | – name: University of Oxford, UNITED KINGDOM – name: 2 Janssen Research and Development LLC, Titusville, NJ, United States of America – name: 1 Department of Medical Informatics, Erasmus University Medical Center, CA Rotterdam, Netherlands |
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| BackLink | https://cir.nii.ac.jp/crid/1871991017542719872$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/30830923$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_pds_5500 crossref_primary_10_1016_j_hlpt_2024_100844 crossref_primary_10_1080_00031305_2024_2368794 crossref_primary_10_1093_ije_dyac026 crossref_primary_10_1002_cpt_2826 crossref_primary_10_1016_j_jbi_2025_104882 |
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| Copyright | COPYRIGHT 2019 Public Library of Science 2019 Afzal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 Afzal et al 2019 Afzal et al |
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| DOI | 10.1371/journal.pone.0212999 |
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| Title | Generating and evaluating a propensity model using textual features from electronic medical records |
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