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Hyperthyroidism and cardiovascular disease: an association study using big data analytics
Pedro Iglesias1,2, María Benavent3, Guillermo López3
1Department of Endocrinology and Nutrition, University Hospital Puerta de Hierro Majadahonda, Instituto de Investigación Sanitaria Puerta de Hierro Segovia de Arana, Majadahonda, Madrid, Spain. piglo65@gmail.com.
Insights
Hyperthyroidism significantly increases the risk of cardiovascular diseases (CVDs) and cardiovascular risk factors (CVRFs) in patients, regardless of age or gender. This study highlights the strong link between hyperthyroidism and major cardiovascular complications.
Area of Science:
- Endocrinology and Cardiology
- Big Data Analytics in Healthcare
Background:
- Thyroid hormones profoundly impact the cardiovascular (CV) system.
- Both hypothyroidism and hyperthyroidism elevate the risk of severe CV complications.
Purpose of the Study:
- To assess the association between hyperthyroidism and major cardiovascular risk factors (CVRFs) and cardiovascular diseases (CVDs).
- Utilize a big data methodology with the Savana Manager platform for comprehensive analysis.
Main Methods:
- Observational, retrospective study design.
- Data sourced from electronic medical records of University Hospital Puerta de Hierro Majadahonda, Spain.
- Employed artificial intelligence and Savana Manager 3.0 software for data extraction and analysis.
Main Results:
- Hyperthyroidism (1.02% of 540,939 patients) was significantly associated with higher frequencies of CVRFs and CVDs (p < 0.0001).
- Elevated CVRFs and CVDs observed in hyperthyroid patients across all age and gender groups.
- Highest odds ratios for embolic stroke (6.40) and atrial fibrillation (5.99); hyperthyroidism independently associated with most CVDs except embolic stroke.
Conclusions:
- Significant association confirmed between hyperthyroidism, CVRFs, and CVDs in the studied population.
- Findings hold true regardless of patient age and gender.
- Demonstrates the utility of artificial intelligence in analyzing real-world health data.
Background:
The cardiovascular (CV) system is profoundly affected by thyroid hormones. Both hypo- and hyperthyroidism can increase the risk of severe CV complications.
Objective:
To 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 Methods:
This 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.
Results:
Of 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.
Conclusion:
The 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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