Observational studyEndocrine2024
Hyperthyroidism and cardiovascular disease: an association study using big data analytics.
Observational study in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Thyroid functional state-dependent dysbiosis of gut microbiota in Hashimoto's thyroiditis: a cross-sectional metagenomic profiling study.Thyroid research · 2026Article
- Aescin and rutin mitigate hyperthyroidism-induced multisystem dysfunction via antioxidant and anti-inflammatory mechanisms in a rat model.Scientific reports · 2026Article
- Cardiac electrical remodeling in Graves' disease: insights from global ventricular repolarization indices.Frontiers in endocrinology · 2026Observational
- Hyperthyroidism with reference to elevated serum FT3 and FT4 levels in patients with ischemic stroke.Biomedical reports · 2025Article
- Therapeutic landscape of metabolic dysfunction-associated steatohepatitis (MASH).Nature reviews. Drug discovery · 2025Review
- Article
- Unexpected Grave's-induced acute myocardial infarction in a young female, a literature review based on a case report.International journal of emergency medicine · 2024Article
- Molecular Study of Sudden Cardiac Death.International journal of molecular sciences · 2024Article
- Lipoprotein alterations in endocrine disorders - a review of the recent developments in the field.Frontiers in endocrinology · 2024Review
- Primary Hyperparathyroidism and Cardiovascular Disease: An Association Study Using Clinical Natural Language Processing Systems and Big Data Analytics.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe cardiovascular (CV) system is profoundly affected by thyroid hormones. Both hypo- and hyperthyroidism can increase the risk of severe CV complications.
objectiveTo assess the association of hyperthyroidism with major CV risk factors (CVRFs) and CV diseases (CVDs) using a big data methodology with the Savana Manager platform. MATERIAL AND
methodsThis was an observational and retrospective study. The data were obtained from the electronic medical records of the University Hospital Puerta de Hierro Majadahonda (Spain). Artificial intelligence techniques were used to extract the information from the electronic health records and Savana Manager 3.0 software was used for analysis.
resultsOf a total of 540,939 patients studied (53.62% females; mean age 42.2 ± 8.7 years), 5504 patients (1.02%; 69.9% women) had a diagnosis of hyperthyroidism. Patients with this diagnosis had a significantly (p < 0.0001) higher frequency of CVRFs than that found in non-hyperthyroid subjects. The higher frequency of CVRFs in patients with hyperthyroidism was observed in both women and men and in patients younger and older than 65 years of age. The total frequency of CVDs was also significantly (p < 0.0001) higher in patients diagnosed with hyperthyroidism than that found in patients without this diagnosis. The highest odds ratio values obtained were 6.40 (4.27-9.61) for embolic stroke followed by 5.99 (5.62-6.38) for atrial fibrillation. The frequency of all CVDs evaluated in patients with a diagnosis of hyperthyroidism was significantly higher in both women and men, as well as in those younger and older than 65 years, compared to subjects without this diagnosis. A multivariate regression analysis showed that hyperthyroidism was significantly and independently associated with all the CVDs evaluated except for embolic stroke.
conclusionThe data from this hospital cohort suggest that there is a significant association between the diagnosis of hyperthyroidism and the main CVRFs and CVDs in our population, regardless of the age and gender of the patients. Our study, in addition to confirming this association, provides useful information for understanding the applicability of artificial intelligence techniques to "real-world data and information".
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