ArticleHypertension research : official journal of the Japanese Society of Hypertension2018
Combined effect of renal function and serum potassium level in sudden cardiac death in aging hypertensive subjects.
Article in Hypertension research : official journal of the Japanese Society of Hypertension, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Simple methods for estimating the maximum 24-hour urinary potassium excretion in kidney failure without replacement therapy patients.Renal failure · 2025Article
- Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018.Annals of medicine · 2023Article
- Machine Learning Models Predicting Cardiovascular and Renal Outcomes and Mortality in Patients with Hyperkalemia.Nutrients · 2022Article
- Prediction Tool to Estimate Potassium Diet in Chronic Kidney Disease Patients Developed Using a Machine Learning Tool: The UniverSel Study.Nutrients · 2022Article
- Increase of Oxidative Stress by Deficiency of The ALDH2/UCP2/Nrf2 Axis Exacerbates Cardiac Dysfunction in Chronic Kidney Disease.Reviews in cardiovascular medicine · 2022Article
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4 authors at 2 institutions in 2 countries.
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Abstract
In patients with chronic kidney disease, serum potassium level is a factor influencing sudden cardiac death (SCD). The aim of our analysis was to study the combined effect of serum potassium level and renal function on the onset of SCD in elderly hypertensive subjects. Data from the 3620 hypertensive patients aged over 70 years were extracted from three randomized clinical trials included in the INDANA database. During a mean follow up of 4.5 years, 81 patients (2.24%) died from SCD. Mean serum potassium levels and prevalence of chronic kidney disease were not different in patients who died from SCD. In addition to serum potassium and creatinine levels, 14 clinical and biological variables linked to cardiovascular diseases recorded at baseline were analyzed using a Bayesian network. The area under the receiver operating characteristic curve of the Bayesian model reached 0.91. Bayesian inference was used to simulate the combined effects of serum potassium and creatinine levels on SCD. Our analysis, using simulated data from Bayesian model, showed that the estimated probabilities of SCD was significantly increased in case of hyperkalemia (>5.0 mmol/l) and in case of hypokalemia (<3.5 mmol/l) and in case of chronic kidney disease. Combined effects of serum potassium level and renal function revealed that chronic kidney disease increased the probability of SCD whatever the serum potassium level. Our results using a Bayesian model confirm the deleterious effects of hypokalemia, hyperkalemia and chronic kidney disease on SCD in elderly hypertensive patients.
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