Development and validation of a code-based algorithm using in-hospital medical records to identify patients with pulmonary arterial hypertension in a French healthcare database
Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be u...
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| Published in | ERJ open research Vol. 10; no. 4; pp. 109 - 2024 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
European Respiratory Society
01.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2312-0541 2312-0541 |
| DOI | 10.1183/23120541.00109-2024 |
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| Abstract | Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database "Système National des Données de Santé (SNDS)".
We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients.
In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries.
Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database. |
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| AbstractList | Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database "Système National des Données de Santé (SNDS)".
We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients.
In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries.
Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database. Introduction Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database “Système National des Données de Santé (SNDS)”. Methods We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients. Results In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries. Conclusion Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database. Development and validation of an algorithm allowing identification of PAH patients in a French healthcare database https://bit.ly/3VXGt74 Introduction Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database (SNDS). Methods We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm (Gillmeyer et al . algorithm), using in hospital ICD-10 codes, right heart catheterisation procedure and PAH specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Secondly, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients. Results In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 01/01/2010 and 30/06/2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6% respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries. Conclusion Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities, that can be used in future studies using the French healthcare database. Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database "Système National des Données de Santé (SNDS)".IntroductionPulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database "Système National des Données de Santé (SNDS)".We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients.MethodsWe developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients.In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries.ResultsIn the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries.Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database.ConclusionOverall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database. |
| Author | Chaumais, Marie-Camille Khouri, Charles Hlavaty, Alex Bouvaist, Hélène Humbert, Marc Bernardeau, Claire Montani, David Cracowski, Jean-Luc Jambon-Barbara, Clément |
| AuthorAffiliation | 4 INSERM UMR_S 999, Hôpital Marie Lannelongue, Le Plessis Robinson, France 1 Pharmacovigilance Unit, Grenoble Alpes University Hospital, Grenoble, France 7 Faculty of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France 9 Grenoble Alpes University Hospital, Clinical Pharmacology Department INSERM CIC1406, Grenoble, France 2 Univ. Grenoble Alpes, HP2 Laboratory, Inserm U1300, Grenoble, France 5 Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Pharmacy, Hôpital Bicêtre, Le Kremlin-Bicêtre, France 8 AP-HP, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital Bicêtre, DMU 5 Thorinno, Le Kremlin-Bicêtre, France 6 Faculty of Pharmacy, Université Paris-Saclay, Saclay, France 3 Cardiology Unit, Grenoble Alpes University Hospital, Grenoble, France |
| AuthorAffiliation_xml | – name: 9 Grenoble Alpes University Hospital, Clinical Pharmacology Department INSERM CIC1406, Grenoble, France – name: 2 Univ. Grenoble Alpes, HP2 Laboratory, Inserm U1300, Grenoble, France – name: 7 Faculty of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France – name: 3 Cardiology Unit, Grenoble Alpes University Hospital, Grenoble, France – name: 6 Faculty of Pharmacy, Université Paris-Saclay, Saclay, France – name: 1 Pharmacovigilance Unit, Grenoble Alpes University Hospital, Grenoble, France – name: 4 INSERM UMR_S 999, Hôpital Marie Lannelongue, Le Plessis Robinson, France – name: 5 Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Pharmacy, Hôpital Bicêtre, Le Kremlin-Bicêtre, France – name: 8 AP-HP, Department of Respiratory and Intensive Care Medicine, Pulmonary Hypertension National Referral Centre, Hôpital Bicêtre, DMU 5 Thorinno, Le Kremlin-Bicêtre, France |
| Author_xml | – sequence: 1 givenname: Clément orcidid: 0000-0002-6619-4130 surname: Jambon-Barbara fullname: Jambon-Barbara, Clément – sequence: 2 givenname: Alex surname: Hlavaty fullname: Hlavaty, Alex – sequence: 3 givenname: Claire surname: Bernardeau fullname: Bernardeau, Claire – sequence: 4 givenname: Hélène surname: Bouvaist fullname: Bouvaist, Hélène – sequence: 5 givenname: Marie-Camille orcidid: 0000-0002-1217-8442 surname: Chaumais fullname: Chaumais, Marie-Camille – sequence: 6 givenname: Marc orcidid: 0000-0003-0703-2892 surname: Humbert fullname: Humbert, Marc – sequence: 7 givenname: David orcidid: 0000-0002-9358-6922 surname: Montani fullname: Montani, David – sequence: 8 givenname: Jean-Luc surname: Cracowski fullname: Cracowski, Jean-Luc – sequence: 9 givenname: Charles orcidid: 0000-0002-8427-8573 surname: Khouri fullname: Khouri, Charles |
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| Cites_doi | 10.1183/13993003.01913-2018 10.1164/rccm.202112-2761OC 10.1002/pds.2321 10.1136/ard-2022-222824 10.1183/16000617.0050-2019 10.1016/j.respe.2017.05.004 10.1016/j.jacc.2013.10.036 10.1038/nrcardio.2017.84 10.1016/j.chest.2018.11.004 10.1016/j.chest.2020.12.010 10.1016/j.ijcard.2022.12.016 10.1002/pul2.12333 10.1177/2045894020977300 10.1093/bjd/ljad248 10.1183/13993003.00879-2022 10.1164/rccm.201709-1821ST 10.2307/2529310 10.1093/eurheartj/ehac237 10.1002/pds.4233 10.1016/j.lanepe.2021.100158 10.1164/rccm.200510-1668OC 10.1161/JAHA.120.016648 10.1016/S0167-5273(11)70492-2 10.1177/2045894020961713 10.1016/j.therap.2023.01.009 10.1513/AnnalsATS.201810-672CME |
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| Snippet | Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy... Introduction Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and... Development and validation of an algorithm allowing identification of PAH patients in a French healthcare database https://bit.ly/3VXGt74 |
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| Title | Development and validation of a code-based algorithm using in-hospital medical records to identify patients with pulmonary arterial hypertension in a French healthcare database |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39135662 https://www.proquest.com/docview/3092367458 https://hal.science/hal-04618768 https://pubmed.ncbi.nlm.nih.gov/PMC11317892 https://doi.org/10.1183/23120541.00109-2024 https://doaj.org/article/4877d888c5614c3880dc4031d422a2e0 |
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