Hypertension in Dialysis Patients: Diagnostic Approaches and Evaluation of Epidemiology

Georgianos, Panagiotis I.; Vaios, Vasilios; Sgouropoulou, Vasiliki; Eleftheriadis, Theodoros; Tsalikakis, Dimitrios G.; Liakopoulos, Vassilios

doi:10.3390/diagnostics12122961

Cited by 4 publications

(4 citation statements)

References 56 publications

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“…Guidelines for treatment and control of hypertension by the American Heart Association have increasingly leveraged lifestyle recommendations in addition to pharmaceutical‐based therapy 1,3,10,11 . Therefore, using patient diets as a potential risk factor for hypertension may enhance personalized medical care 2,12,13 …”

Section: Introductionmentioning

confidence: 99%

“…1,3,10,11 Therefore, using patient diets as a potential risk factor for hypertension may enhance personalized medical care. 2,12,13 Machine learning algorithms, which generate predictions from data without explicit direction from the user, can be effectively employed to leverage the ever-growing data present in hospital systems to augment diagnosis, improve outcomes, and reduce costs. [14][15][16][17][18] These algorithms can identify hidden patterns within large datasets that may otherwise be missed.…”

Section: Introductionmentioning

confidence: 99%

“… 1 , 3 , 10 , 11 Therefore, using patient diets as a potential risk factor for hypertension may enhance personalized medical care. 2 , 12 , 13 …”

Section: Introductionmentioning

confidence: 99%

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Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Huang,

Huang

2023

J of Clinical Hypertension

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Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long‐term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine‐learning techniques could accurately predict hypertension risk using nutritional information. A cross‐sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine‐learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine‐learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Huang,

Huang

2023

J of Clinical Hypertension

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“…Hypertension is an important cause of cardiovascular morbidity and mortality in both the general population and in patients with end-stage kidney disease (ESKD) undergoing long-term peritoneal dialysis (PD) [ 1 , 2 ]. In the general population, the trajectories for blood pressure (BP) with ageing differ between men and women [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Gender-Related Differences in the Levels of Ambulatory BP and Intensity of Antihypertensive Treatment in Patients Undergoing Peritoneal Dialysis

Kontogiorgos

Georgianos

Vaios

et al. 2023

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Prior studies have shown that among patients with chronic kidney disease not yet on dialysis, the faster progression of kidney injury in men than in women is, at least partly, explained by sex differences in ambulatory blood pressure (BP) control. The present study aimed to investigate potential differences in the levels of ambulatory BP and intensity of antihypertensive treatment between men and women with end-stage kidney disease undergoing long-term peritoneal dialysis (PD). In a case-control design, 48 male PD patients were matched for age and heart failure status with 48 female patients in a 1:1 ratio. Ambulatory BP monitoring was performed with an oscillometric device, the Mobil-O-Graph (IEM, Stolberg, Germany). The BP-lowering medications actually taken by the patients were prospectively recorded. No gender-related differences were observed in 24 h systolic BP (129.0 ± 17.9 vs. 128.5 ± 17.6 mmHg, p = 0.890). In contrast, 24 h diastolic BP was higher in men than in women (81.5 ± 12.1 vs. 76.8 ± 10.3 mmHg, p = 0.042). As compared with women, men were being treated with a higher average number of antihypertensive medications daily (2.4 ± 1.1 vs. 1.9 ± 1.1, p = 0.019) and were more commonly receiving calcium-channel-blockers (70.8% vs. 43.8%, p = 0.007) and β-blockers (85.4% vs. 66.7%, p = 0.031). In conclusion, the present study shows that among PD patients, the levels of ambulatory BP and intensity of antihypertensive treatment are higher in men than in women. Longitudinal studies are needed to explore whether these gender-related differences in the severity of hypertension are associated with worse cardiovascular outcomes for male patients undergoing PD.

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The association of home blood pressure with all‐cause mortality in hemodialysis patients: A prospective observational study

Kontogiorgos,

Georgianos,

Tsikliras

et al. 2024

Ther Apher Dial

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IntroductionPrior observational studies conducted in the hemodialysis population have suggested a reverse association between dialysis‐unit blood pressure (BP) and mortality. The present study aimed to investigate the prognostic association of home versus dialysis‐unit BP with all‐cause mortality in hemodialysis patients.MethodsAt baseline, 146 patients receiving maintenance hemodialysis underwent assessment of their BP with the following methods: (i) 2‐week averaged routine predialysis and postdialysis BP measurements; (ii) home BP monitoring for 1 week that included duplicate morning and evening BP measurements with the use of validated devices.ResultsOver a median follow‐up period of 38 months (interquartile range [IQR]: 22–54), 44 patients (31.1%) died. In Kaplan–Meier curves, predialysis and postdialysis systolic BP (SBP) was not associated with all‐cause mortality, while home SBP appeared to be of prognostic significance (log rank p = 0.029). After stratifying patients into quartiles, all‐cause mortality was lowest when home SBP was ranging from 128.1 to 136.8 mmHg (quartile 2). In univariate Cox regression analysis, using quartile 2 as a referent category, the risk of all‐cause mortality was 3.32‐fold higher in quartile 1, 1.53‐fold higher in quartile 3 and 3.25‐fold higher in quartile 4. The risk‐association remained unchanged after adjustment for several confounding factors (adjusted hazard ratio: 4.79, 1.79, 3.63 for quartiles 1, 3, and 4 of home systolic BP, respectively).ConclusionOur findings suggest that among hemodialysis patients, 1‐week averaged home SBP is independently associated with all‐cause mortality. In sharp contrast, SBP recorded either before or after dialysis over 2 weeks is not prognostically informative.

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Hypertension in Dialysis Patients: Diagnostic Approaches and Evaluation of Epidemiology

Cited by 4 publications

References 56 publications

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Gender-Related Differences in the Levels of Ambulatory BP and Intensity of Antihypertensive Treatment in Patients Undergoing Peritoneal Dialysis

The association of home blood pressure with all‐cause mortality in hemodialysis patients: A prospective observational study

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