Approaching a nationwide registry: analyzing big data in patients with heart failure

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dc.authoridhttps://orcid.org/0000-0002-2227-6177
dc.authoridhttps://orcid.org/0000-0003-3416-5965
dc.authoridhttps://orcid.org/0000-0002-9417-7610
dc.authoridhttps://orcid.org/0000-0002-7068-1599
dc.contributor.authorÇöllüoğlu, Tuğçe
dc.contributor.authorŞahin, Anıl
dc.contributor.authorÇelik, Ahmet
dc.contributor.authorKanik, Emine Arzu
dc.date.accessioned2025-02-28T07:43:43Z
dc.date.available2025-02-28T07:43:43Z
dc.date.issued2024-05-07
dc.departmentFakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü
dc.description.abstractBackground/aim: Randomized controlled trials usually lack generabilizity to real-world context. Real-world data, enabled by the use of big data analysis, serve as a connection between the results of trials and the implementation of findings in clinical practice. Nevertheless, using big data in the healthcare has difficulties such as ensuring data quality and consistency. This article aimed to examine the challenges in accessing and utilizing healthcare big data for heart failure (HF) research, drawing from experiences in creating a nationwide HF registry in Türkiye. Materials and methods: We established a team including cardiologists, HF specialists, biostatistics experts, and data analysts. We searched certain key words related to HF, including heart failure, nationwide study, epidemiology, incidence, prevalence, outcomes, comorbidities, medical therapy, and device therapy. We followed each step of the STROBE guidelines for the preparation of a nationwide study. We obtained big data for the TRends-HF trial from the National Healthcare Data System. For the purpose of obtaining big data, we screened 85,279,553 healthcare records of Turkish citizens between January 1, 2016 and December 31, 2022. Results: We created a study cohort with the use of ICD-10 codes by cross-checking HF medication (n = 2,722,151). Concurrent comorbid conditions were determined using ICD-10 codes. All medications and procedures were screened according to ATC codes and SUT codes, respectively. Variables were placed in different columns. We employed SPSS 29.0, MedCalc, and E-PICOS statistical programs for statistical analysis. Phyton-based codes were created to analyze data that was unsuitable for interpretation by conventional statistical programs. We have no missing data for categorical variables. There was missing data for certain continuous variables. Propensity score matching analysis was employed to establish similarity among the studied groups, particularly when investigating treatment effects. Conclusion: To accurately identify patients with HF using ICD-10 codes from big data and provide precise information, it is necessary to establish additional specific criteria for HF and use different statistical programs by experts for correctly analyzing big data.
dc.identifier10.55730/1300-0144.5931
dc.identifier.citationÇöllüoğlu, T., Şahin, A., Çelik, A., & Kanik, E. A. (2024). Approaching a nationwide registry: analyzing big data in patients with heart failure. Turkish journal of medical sciences, 54(7), 1455–1460. https://doi.org/10.55730/1300-0144.5931
dc.identifier.doi10.55730/1300-0144.5931
dc.identifier.endpage1460
dc.identifier.issn1300-0144
dc.identifier.issn1303-6165
dc.identifier.issue7
dc.identifier.pmid39735481
dc.identifier.scopus2-s2.0-85213348136
dc.identifier.scopusqualityQ1
dc.identifier.startpage1455
dc.identifier.urihttps://doi.org/10.55730/1300-0144.5931
dc.identifier.urihttps://hdl.handle.net/20.500.14619/15094
dc.identifier.volume54
dc.identifier.wosWOS:001381273000005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherTUBITAK
dc.relation.ispartofTurkish Journal of Medical Sciences
dc.relation.ispartofseriesTurkish Journal of Medical Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectbig data
dc.subjectbiostatistic
dc.subjectHeart failure
dc.subjectnationwide study
dc.subjectreal-world data
dc.titleApproaching a nationwide registry: analyzing big data in patients with heart failure
dc.typeArticle
oaire.citation.issue7
oaire.citation.volume54

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