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Becerra-Muñoz VM, Gómez Sáenz JT, Escribano Subías P. The importance of data in Pulmonary Arterial Hypertension: from international registries to Machine Learning. Med Clin (Barc) 2024; 162:591-598. [PMID: 38383269 DOI: 10.1016/j.medcli.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 02/23/2024]
Abstract
Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroupś definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.
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Affiliation(s)
- Víctor Manuel Becerra-Muñoz
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Servicio de Cardiología, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, España; Hospital Universitario Virgen de la Victoria, Universidad de Málaga (UMA), Málaga, España.
| | - José Tomás Gómez Sáenz
- Centro de Salud de Nájera, La Rioja, España; Sociedad Española de Médicos de Atención Primaria (SEMERGEN), Madrid, España
| | - Pilar Escribano Subías
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Hospital Universitario 12 de Octubre, Madrid, España
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Didden E, Lu D, Hsi A, Brand M, Hedlin H, Zamanian RT. Clinical evaluation of code-based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases. Pulm Circ 2024; 14:e12333. [PMID: 38333073 PMCID: PMC10851026 DOI: 10.1002/pul2.12333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/24/2023] [Accepted: 12/21/2023] [Indexed: 02/10/2024] Open
Abstract
Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code-based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H-specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well-defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best-performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code-based PAH algorithms to their research question and demonstrate the importance of including well-defined temporal components in the algorithms.
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Affiliation(s)
- Eva‐Maria Didden
- Global Epidemiology, Rare Disease Epicenter, Actelion Pharmaceuticals LtdJanssen Pharmaceutical Company of Johnson & JohnsonAllschwilSwitzerland
| | - Di Lu
- Quantitative Sciences UnitStanford UniversityStanfordCaliforniaUSA
| | - Andrew Hsi
- Adult PH ProgramVera Moulton Wall Center UniversityStanfordCaliforniaUSA
| | - Monika Brand
- Global Epidemiology, Rare Disease Epicenter, Actelion Pharmaceuticals LtdJanssen Pharmaceutical Company of Johnson & JohnsonAllschwilSwitzerland
| | - Haley Hedlin
- Quantitative Sciences UnitStanford UniversityStanfordCaliforniaUSA
| | - Roham T. Zamanian
- Adult PH ProgramVera Moulton Wall Center UniversityStanfordCaliforniaUSA
- Division of Pulmonary, Allergy, and Critical Care MedicineStanford UniversityStanfordCaliforniaUSA
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Morland K, Gerges C, Elwing J, Visovatti SH, Weatherald J, Gillmeyer KR, Sahay S, Mathai SC, Boucly A, Williams PG, Harikrishnan S, Minty EP, Hobohm L, Jose A, Badagliacca R, Lau EMT, Jing Z, Vanderpool RR, Fauvel C, Leonidas Alves J, Strange G, Pulido T, Qian J, Li M, Mercurio V, Zelt JGE, Moles VM, Cirulis MM, Nikkho SM, Benza RL, Elliott CG. Real-world evidence to advance knowledge in pulmonary hypertension: Status, challenges, and opportunities. A consensus statement from the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative's Real-world Evidence Working Group. Pulm Circ 2023; 13:e12317. [PMID: 38144948 PMCID: PMC10739115 DOI: 10.1002/pul2.12317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/26/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023] Open
Abstract
This manuscript on real-world evidence (RWE) in pulmonary hypertension (PH) incorporates the broad experience of members of the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative Real-World Evidence Working Group. We aim to strengthen the research community's understanding of RWE in PH to facilitate clinical research advances and ultimately improve patient care. Herein, we review real-world data (RWD) sources, discuss challenges and opportunities when using RWD sources to study PH populations, and identify resources needed to support the generation of meaningful RWE for the global PH community.
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Affiliation(s)
- Kellie Morland
- Global Medical AffairsUnited Therapeutics CorporationResearch Triangle ParkNorth CarolinaUSA
| | - Christian Gerges
- Department of Internal Medicine II, Division of CardiologyMedical University of ViennaViennaAustria
| | - Jean Elwing
- Division of Pulmonary, Critical Care, and Sleep MedicineUniversity of CincinnatiCincinnatiOhioUSA
| | - Scott H. Visovatti
- Division of Cardiovascular MedicineThe Ohio State UniversityColumbusOhioUSA
| | - Jason Weatherald
- Department of Medicine, Division of Pulmonary MedicineUniversity of AlbertaEdmontonCanada
| | - Kari R. Gillmeyer
- The Pulmonary CenterBoston University Chobian & Avedisian School of MedicineBostonMassachusettsUSA
- Center for Healthcare Organization & Implementation ResearchVA Bedford Healthcare System and VA Boston Healthcare SystemBedfordMassachusettsUSA
| | - Sandeep Sahay
- Division of Pulmonary, Critical Care & Sleep MedicineHouston Methodist HospitalHoustonTexasUSA
| | - Stephen C. Mathai
- Division of Pulmonary and Critical Care MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Athénaïs Boucly
- Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance
- Service de Pneumologie et Soins Intensifs Respiratoires, Centre de Référence de l'Hypertension Pulmonaire, Hôpital BicêtreAssistance Publique Hôpitaux de ParisLe Kremlin BicêtreFrance
- National Heart and Lung InstituteImperial CollegeLondonUK
| | - Paul G. Williams
- Center of Chest Diseases & Critical CareMilpark HospitalJohannesburgSouth Africa
| | | | - Evan P. Minty
- Department of Medicine & O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada
| | - Lukas Hobohm
- Department of CardiologyUniversity Medical Center of the Johannes Gutenberg University MainzMainzGermany
- Center for Thrombosis and Hemostasis (CTH)University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Arun Jose
- Division of Pulmonary, Critical Care, and Sleep MedicineUniversity of CincinnatiCincinnatiOhioUSA
| | - Roberto Badagliacca
- Department of Clinical, Anesthesiological and Cardiovascular Sciences, Sapienza University of RomePoliclinico Umberto IRomeItaly
| | - Edmund M. T. Lau
- Department of Respiratory Medicine, Royal Prince Alfred HospitalUniversity of SydneyCamperdownNew South WalesAustralia
- Faculty of Medicine and HealthUniversity of SydneyCamperdownNew South WalesAustralia
| | - Zhi‐Cheng Jing
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | | | - Charles Fauvel
- Service de Cardiologie, Centre de Compétence en Hypertension Pulmonaire 27/76, Centre Hospitalier Universitaire Charles Nicolle, INSERM EnVI U1096Université de RouenRouenFrance
| | - Jose Leonidas Alves
- Pulmonary Division, Heart InstituteUniversity of São Paulo Medical SchoolSão PauloBrazil
| | - Geoff Strange
- School of MedicineThe University of Notre Dame AustraliaPerthWestern AustraliaAustralia
| | - Tomas Pulido
- Ignacio Chávez National Heart InstituteMéxico CityMexico
| | - Junyan Qian
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC‐DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijingChina
| | - Mengtao Li
- Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC‐DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical ImmunologyMinistry of EducationBeijingChina
| | - Valentina Mercurio
- Department of Translational Medical SciencesFederico II UniversityNaplesItaly
| | - Jason G. E. Zelt
- Department of Medicine, Faculty of MedicineUniversity of OttawaOttawaCanada
| | - Victor M. Moles
- Division of Cardiovascular MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Meghan M. Cirulis
- Division of Pulmonary and Critical Care MedicineUniversity of UtahSalt Lake CityUtahUSA
- Department of Pulmonary and Critical Care MedicineIntermountain Medical Center MurraySalt Lake CityUtahUSA
| | | | - Raymond L. Benza
- Mount Sinai HeartIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - C. Gregory Elliott
- Division of Pulmonary and Critical Care MedicineUniversity of UtahSalt Lake CityUtahUSA
- Department of Pulmonary and Critical Care MedicineIntermountain Medical Center MurraySalt Lake CityUtahUSA
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Hyde B, Paoli CJ, Panjabi S, Bettencourt KC, Bell Lynum KS, Selej M. A claims-based, machine-learning algorithm to identify patients with pulmonary arterial hypertension. Pulm Circ 2023; 13:e12237. [PMID: 37287599 PMCID: PMC10243208 DOI: 10.1002/pul2.12237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 04/14/2023] [Accepted: 05/01/2023] [Indexed: 06/09/2023] Open
Abstract
Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine-learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de-identified data from the US-based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non-PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non-PAH at diagnosis and at 6 months prediagnosis. The PAH and non-PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non-PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non-PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH-specific screening and/or earlier specialist referral.
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Affiliation(s)
- Bethany Hyde
- Janssen Business Technology Commercial Data Insights & Data ScienceTitusvilleNew JerseyUSA
| | | | | | | | | | - Mona Selej
- Janssen R&D Data ScienceSouth San FranciscoCaliforniaUSA
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Payne C, Jacobs S, Shaboodien S, Jumaar C, Pretorius A, Kgatla T, Sanni O, Maarman G. A pulmonary arterial hypertension diagnostic algorithm for a third-world context: SYMQUICK. J Investig Med 2023; 71:542-544. [PMID: 36789665 DOI: 10.1177/10815589231155194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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