ArticleTurkish journal of medical sciences2024
Approaching a nationwide registry: analyzing big data in patients with heart failure.
Article in Turkish journal of medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.