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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. You have full access to this article via your institution. Download PDF SIMILAR CONTENT BEING

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Sudden cardiac death (SCD) refers to an unexpected death of cardiovascular origin in a patient suffering, or not, from an existing cardiac disorder [1]. In prospective trials, the incidence

of SCD varies between 50 and 100 per 100,000 individuals. Variations in the estimations may be related to the different definitions of SCD used in the trials. Elderly subjects are

particularly at risk since the incidence of SCD reaches 800 per 100,000 individuals [2]. Among the factors related to SCD, some are considered to be contributory factors and others are

considered to be triggering factors [3]. The contributory factors described are: age, male sex, African origin, long QT interval, history of ischemic heart disease, heart failure, excessive

alcohol consumption, sedentary life style and psychosocial factors (anxiety, depression, isolation) [4]. Other factors are considered to be triggers: mornings, Mondays, high temperatures and

intense physical activity. From a pathophysiological viewpoint, intense sympathetic stimulation might be the common triggering factor [5]. In the DUKE’s predictive score of SCD,

specifically established in secondary prevention in patients with documented coronary disease, seven variables that were independently linked to SCD (depressed left ventricular ejection

fraction, number of diseased coronary arteries, diabetes mellitus, hypertension, heart failure, cerebrovascular disease, and tobacco use) are used [6]. Chronic kidney disease (CKD) is a

well-known risk factor of cardiovascular mortality [7] and SCD in the community [8] and in populations with other pathologic conditions [9,10,11,12,13,14,15]. Serum potassium abnormalities

that promote cardiac arrhythmias are also considered risk factors for sudden death [16]. Serum potassium level is controlled by renal function. In patients with CKD, serum potassium level is

a factor influencing SCD even before the dialysis stage. A U-shaped curve has been described and it would appear that hypokalemia is more dangerous than hyperkalemia in renal patients

[17,18,19,20,21]. Thus, 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 subjects whose renal function is

often impaired and among whom there is a higher incidence of SCD. We performed a Bayesian inference analysis using a Bayesian model adjusted to the data extracted from the INdividual Data

ANalysis of Antihypertensive drug intervention trials (INDANA) database, which contains the individual data of hypertensive subjects included in controlled clinical trials. SUBJECTS AND

METHODS SELECTION OF PARTICIPANTS The INDANA database contains the individual data of hypertensive patients, aged 26–96 years with an average follow-up of 4.5 years [22, 23]. None of the

subject was on dialysis. Of the seven studies recorded in the INDANA database, three (SHEP; _n_ = 4736; 4.4 years of follow-up; EWPHE _n_ = 840; 4.7 years of follow-up and COOPE _n_ = 883;

4.5 years of follow-up) recorded baseline serum potassium and creatinine levels. The data of 3620 subjects aged 70 years or more were extracted. The COOPE study compared the effect of atenol

plus bendrofluazide to control, the EWPHE study compared the effects of hydrochlorothiazide plus triamterene to placebo and the SHEP study compared the effects of chlortalidone plus

atenolol or reserpine to placebo in hypertensive patients. VARIABLES OF INTEREST The covariables measured at inclusion in all studies were: age, sex, smoking status (current smoker,

non-smoker), BMI, history of myocardial infarction, history of stroke, history of hypertension, systolic and diastolic blood pressure, heart rate, blood glucose, cholesterolemia, creatinine

level, serum potassium level and plasma uric acid. Serum potassium level was divided into three classes: hypokalemia (_K_ < 3.5 mmol/l), normal serum potassium level (3.5 ≤ _K_ ≤ 5.0 

mmol/l), and hyperkalemia (_K_ ≥ 5.0 mmol/l). Renal function was considered to be impaired if creatinine levels were >137 µmol/l in men and >104 µmol/l in women. These cut-off values

were reported to estimate an inulin clearance of less than 60 ml/min/1.73 m²) [24]. As needed to compute probabilities for Bayesian models, continuous variables (age, BMI, systolic and

diastolic blood pressure, heart rate, blood glucose, cholesterolemia, and plasma uric acid) were discretized into five classes of equal frequencies (20% of the sample). In these studies, the

definitions used for sudden death (i.e., death within 1 h of the onset of severe cardiac symptoms, unrelated to other known causes for the SHEP study) were not as precise as those recently

published in the Journal of the American college of cardiology [25]. MODEL STRUCTURE A Bayesian network is a directed graph of a probabilistic structure. It consists of nodes and directed

arrows. Each node is used to represent a variable (and its modalities), and each arrow represents a dependence between variables. Each arrow points from one “parent node” to a “child node”.

The probabilistic dependencies between variables are calculated using the conditional probability as defined by Bayes’ theorem. The tree augmented naïve (TAN) algorithm was used to build the

structure of the Bayesian network. The TAN algorithm uses three rules: (i) each variable is independently linked to the target variable (i.e., CV event); (ii) each variable is also linked

to a unique parent node; (iii) among all possible structures, the structure that maximizes the global mutual information between variables is selected. Thus, conditional probabilities tables

are estimated using the expectation maximization algorithm. The expectation maximization algorithm is an efficient iterative procedure to compute the maximum likelihood estimation in the

presence of missing or hidden data. Network learning was performed on all data files. The software used to create the model was Netica® 5.19; Norsys corporation®. STATISTICS Mean values

between groups (SCD and No SCD) were compared using an analysis of variance (ANOVA) and percentages using _χ_2 tests. The goodness of fit of the model was assessed using the area under the

receiver operating characteristic (ROC) curve. The mutual information of two variables measures their degree of dependence. This mutual information is null when the variables are

independent, and grows when dependence increases. Bayesian inference was performed by superimposing 100% likelihood for each of the three classes of serum potassium level and each of the two

classes of renal function. The probabilities of SCD were obtained for each of the six possible combinations of serum potassium level and renal function. Comparisons of probabilities were

carried out using a _χ_2 test. Interaction of the study treatment on SCD was also analyzed using the Bayesian network. Percentages were compared sing a _χ_2 test. Statistics were performed

using the MedCalc® software version 11.5.1.0. RESULTS During the 4.5 years of follow-up, 81 patients (2.24%) died of SCD. Incidence rate of SCD were similar between cohorts (2.23% for SHEP,

2.0% for EWPHE, and 2.52% for COOPE). The main characteristics of the patients who died from SCD are described in Table 1. Mean serum potassium and prevalence of CKD were not different in

patients who died from SCD (Table 1). The graph in Fig. 1 represents the structure of the model obtained by the TAN algorithm. Figure 2 shows the simplified model obtained using the INDANA

database showing the distribution of modalities of the variables of interest (inputs: serum potassium level and renal function, and output: SCD). The area under the ROC curve of the Bayesian

model for describing SCD was 0.91. In the population, the five variables that were the most linked to SCD are, in descending order: age, history of myocardial infarction, history of

diabetes mellitus, serum potassium level and blood glucose (Table 2). Analyses with simulated data revealed that renal failure increases the probability of SCD (2.83% vs. 2.07%; _p_ < 

0.05), that hypokalemia increases the probability of SCD (4.93% vs. 2.16%; _p_ < 0.001) and that hyperkalemia increases the probability of SCD (2.66% vs. 2.16%; _p_ < 0.05). Combined

effects of serum potassium levels and renal function were simulated as shown in Fig. 3. Compared to the probability of SCD with normal serum potassium and normal renal function,

probabilities of SCD were significantly higher in case of hypokalemia and normal renal function (4.54% vs. 2.00%; _p_ < 0.001), hypokalemia and CKD (6.25% vs. 2.00%; _p_ < 0.001),

normal potassium level and CKD (2.73% vs. 2.00%; _p_ < 0.05), and in case of hyperkalemia and CKD (3.35% vs. 2.00%; _p_ < 0.001). Results are presented in Fig. 4. Study treatment did

not influence the incidence of SCD that was 2.10% in the treatment group and 2.37% (NS) in the control group. In the COOPE, EWPHE and SHEP study incidences of SCD were 2.53% vs. 2.52%, 2.17%

vs. 1.82% and 2.03% vs. 2.44% in the treatment and control groups, respectively. DISCUSSION For a long time, serum potassium level has been known to have an effect on cardiac rhythm

disorders and is considered a risk factor of SCD. Serum potassium level is controlled by renal function which is often impaired in elderly subjects. CKD is also a well-known risk factor of

SCD in the community [8] and in populations with coronary artery disease [11,12,13], heart failure [9, 10, 12], end-stage renal disease [14] and in elderly [15]. The originality of our study

is to have analyzed the combined effects of renal function and serum potassium in elderly hypertensive subjects. Our analysis showed that in hypertensive subjects aged over 70 years, serum

potassium level did have an effect on the risk of SCD. Hypokalemia seems to be more harmful than hyperkalemia. CKD tended to increase the risk of SCD irrespective of serum potassium levels

(Fig. 4). These results were obtained by Bayesian inference analysis of data extracted from the INDANA database [23, 24]. The INDANA contains data obtained from well-performed clinical

trials. Nevertheless, only three trials in the INDANA database recorded data for baseline serum potassium and creatinine levels. Given the age of the data in the INDANA database, we did not

calculate glomerular filtration rate (GFR) using a formula (Cockcroft, MDRD or CKD-EPI) to detect the presence of any CKD. CKD (GFR < 60 ml/min/1.73 m²) was estimated however using the

creatinine level thresholds proposed at the time and validated using inulin renal clearances as gold standard [25]. The data being relatively old, we only used baseline data that we have

linked to SCD thus eliminating the effect of treatment (which has never been found connected to SCD in the INDANA database). The goodness of fit of the Bayesian model was confirmed by the

area under the ROC curve of 0.91. We used the TAN Bayes algorithm maximizing the area under the ROC curve and confirming its relevance for modeling biomedical data [26, 27]. In comparison

with a naive architecture where all the variables are considered to be independent of each other when predicting SCD, the TAN algorithm establishes relationships between variables (Fig. 1),

which is much more relevant for biomedical statistics. Our non-parametric approach using Bayesian inference is justified by the complexity of the relationships between variable modalities

and the target variable: SCD. Unlike other statistical methods, Bayesian networks make no hypothesis as to the nature of the relationships between variables. Moreover, in clinical practice,

the measurement of information is “noisy”, even in the best of trials, like those found in the INDANA database, even more so when the observed phenomenon is rare. Indeed, the incidence of

SCD in the population of hypertensive subjects over 70 years of age in the INDANA database is low and is only 2.24% in our study. The definition of SCD in the used studies (death within 1 h

of the onset of severe cardiac symptoms, unrelated to other known causes) that is different from the recently recommended one [25] is a limitation to the interpretation of our results. The

principal limitation of our study is the absence of external validation of our model. However the results of our analysis found the same main risk factors of SCD as described in the

literature. The computation of mutual information on individual data is in accordance with the descriptive statistical analysis (Table 1). Even for subjects over 70 years, age remains the

principal risk factor of SCD. History of myocardial infarction and of type 2 diabetes are the two other principal risk factors of SCD, as well as serum potassium level. In patients with CKD,

serum potassium level is also a risk factor of SCD before the dialysis stage [20]. We found that serum potassium level affects the risk of SCD in elderly hypertensive subjects, and that

hypokalemia tends to be more harmful than hyperkalemia. In the INADANA database, hypokalemia is rarely associated with renal falure and may be due to iatrogenesis (diuretics or potassium

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references AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Hospices Civils de Lyon-Université C Bernard Lyon 1, Lyon, France Jean-Pierre Fauvel * Service de pharmacologie clinique et essais

thérapeutiques Hospices Civils de Lyon, UMR5558 - Evaluation et Modélisation des Effets des Médicaments, Lyon, France Francois Gueyffier * Studies Coordinating Centre, Research Unit

Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium Lutgarde Thijs * Pharmacie, Hospices Civils de Lyon-

EMR3738 Ciblage thérapeutique en oncologie, Université C Bernard Lyon 1, Lyon, France Michel Ducher Authors * Jean-Pierre Fauvel View author publications You can also search for this author

inPubMed Google Scholar * Francois Gueyffier View author publications You can also search for this author inPubMed Google Scholar * Lutgarde Thijs View author publications You can also

search for this author inPubMed Google Scholar * Michel Ducher View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to

Jean-Pierre Fauvel. ETHICS DECLARATIONS CONFLICT OF INTEREST The authors declare that they have no conflict of interest. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE

CITE THIS ARTICLE Fauvel, JP., Gueyffier, F., Thijs, L. _et al._ Combined effect of renal function and serum potassium level in sudden cardiac death in aging hypertensive subjects.

_Hypertens Res_ 41, 469–474 (2018). https://doi.org/10.1038/s41440-018-0035-3 Download citation * Received: 12 April 2017 * Revised: 15 November 2017 * Accepted: 27 November 2017 *

Published: 09 April 2018 * Issue Date: June 2018 * DOI: https://doi.org/10.1038/s41440-018-0035-3 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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