Prevalence, recurrence and seasonal variation of hyperkalemia among patients on hemodialysis

Hyperkalemia burden and treatment patterns in Chinese patients on hemodialysis: final analysis of a prospective multicenter cohort study (PRECEDE-K)

OBJECTIVES

Patients with end-stage renal disease (ESRD) on hemodialysis (HD) are at risk for hyperkalemia (HK), associated with cardiac arrhythmia and sudden death. Data on the burden of HK and management techniques among HD patients in China are still scarce. This study assessed the treatment modalities, recurrence, and prevalence of HK in Chinese HD patients.

METHODS

In this prospective cohort study conducted from May 2021 to July 2022, patients aged ≥18 years who had ESRD and were on HD were enrolled from 15 centers in China (up to 6 months).

RESULTS

Overall, 600 patients were enrolled. At the baseline visit, mean (± standard deviation) urea reduction ratio was 68.0% ± 9.70 and Kt/V was 1.45 ± 0.496. Over 6 months, 453 (75.5%) patients experienced HK, of whom 356 (78.6%) recurred. Within 1, 2, 3, 4, 5, and 6 months, 203 (44.8%), 262 (57.8%), 300 (66.2%), 326 (72.0%), 347 (76.6%), and 356 (78.6%) patients had at least one HK recurrence event, respectively. The proportions of patients with ≥1, 2, 3, 4, 5, or 6 HK recurrence events were 356 (78.6%), 306 (67.5%), 250 (55.2%), 208 (45.9%), 161 (35.5%), and 110 (24.3%), respectively. Among the 453 patients who experienced HK, only 24 (5.3%) were treated with potassium binders: seven (1.5%) with sodium polystyrene sulfonate, 13 (2.9%) with calcium polystyrene sulfonate, and six (1.3%) with sodium zirconium cyclosilicate.

CONCLUSION

Since HK is a chronic illness, long-term care is necessary. Patients on HD should have effective potassium management on non-dialysis days, yet our real-world population rarely used potassium binders.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT04799067.

Seasonal variation of serum potassium in hemodialysis patients: myth or reality? A narrative review of literature

Research has shown that patients undergoing hemodialysis experience seasonal variations in their serum potassium levels. There was inconsistent seasonal fluctuation in serum potassium levels among the hemodialysis population across different locations. In the form of narrative review for the first time, the article discusses the seasonal changes of serum potassium in this population and its potential reasons, this article demonstrates that it is primarily attributable to seasonal dietary potassium intake. However, existing studies have not quantified seasonal dietary potassium intake, so the results are still speculative. Furthermore, future research ought to further expound upon the clinical implications of seasonal variations in serum potassium levels among dialysis patients, as well as other influencing mechanisms such as the pathophysiological causes of these seasonal changes, particularly those pertaining to dietary, geographical, and regional factors. These findings contribute to a more thorough interpretation of laboratory results in hemodialysis patients and provide important guidance for their individualized dietary management.

Evaluating the Effectiveness of a Generative Pretrained Transformer-Based Dietary Recommendation System in Managing Potassium Intake for Hemodialysis Patients

OBJECTIVE

Despite adequate dialysis, the prevalence of hyperkalemia in Chinese hemodialysis (HD) patients remains elevated. This study aims to evaluate the effectiveness of a dietary recommendation system driven by generative pretrained transformers (GPTs) in managing potassium levels in HD patients.

METHODS

We implemented a bespoke dietary guidance tool utilizing GPT technology. Patients undergoing HD at our center were enrolled in the study from October 2023 to November 2023. The intervention comprised of two distinct phases. Initially, patients were provided with conventional dietary education focused on potassium management in HD. Subsequently, in the second phase, they were introduced to a novel GPT-based dietary guidance tool. This artificial intelligence (AI)-powered tool offered real-time insights into the potassium content of various foods and personalized dietary suggestions. The effectiveness of the AI tool was evaluated by assessing the precision of its dietary recommendations. Additionally, we compared predialysis serum potassium levels and the proportion of patients with hyperkalemia among patients before and after the implementation of the GPT-based dietary guidance system.

RESULTS

In our analysis of 324 food photographs uploaded by 88 HD patients, the GPTs system evaluated potassium content with an overall accuracy of 65%. Notably, the accuracy was higher for high-potassium foods at 85%, while it stood at 48% for low-potassium foods. Furthermore, the study examined the effect of GPT-based dietary advice on patients' serum potassium levels, revealing a significant reduction in those adhering to GPTs recommendations compared to recipients of traditional dietary guidance (4.57 ± 0.76 mmol/L vs. 4.84 ± 0.94 mmol/L, P = .004). Importantly, compared to traditional dietary education, dietary education based on the GPTs tool reduced the proportion of hyperkalemia in HD patients from 39.8% to 25% (P = .036).

CONCLUSION

These results underscore the promising role of AI in improving dietary management for HD patients. Nonetheless, the study also points out the need for enhanced accuracy in identifying low potassium foods. It paves the way for future research, suggesting the incorporation of extensive nutritional databases and the assessment of long-term outcomes. This could potentially lead to more refined and effective dietary management strategies in HD care.

A nomogram to predict hyperkalemia in patients with hemodialysis: a retrospective cohort study

BACKGROUND

Hyperkalemia increases the risk of mortality and cardiovascular-related hospitalizations in patients with hemodialysis. Predictors of hyperkalemia are yet to be identified. We aimed at developing a nomogram able to predict hyperkalemia in patients with hemodialysis.

METHODS

We retrospectively screened patients with end-stage renal disease (ESRD) who had regularly received hemodialysis between Jan 1, 2017, and Aug 31, 2021, at Lishui municipal central hospital in China. The outcome for the nomogram was hyperkalemia, defined as serum potassium [K⁺] ≥ 5.5 mmol/L. Data were collected from hemodialysis management system. Least Absolute Shrinkage Selection Operator (LASSO) analysis selected predictors preliminarily. A prediction model was constructed by multivariate logistic regression and presented as a nomogram. The performance of nomogram was measured by the receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA). This model was validated internally by calculating the performance on a validation cohort.

RESULTS

A total of 401 patients were enrolled in this study. 159 (39.65%) patients were hyperkalemia. All participants were divided into development (n = 256) and validation (n = 145) cohorts randomly. Predictors in this nomogram were the number of hemodialysis session, blood urea nitrogen (BUN), serum sodium, serum calcium, serum phosphorus, and diabetes. The ROC curve of the training set was 0.82 (95%CI 0.77, 0.88). Similar ROC curve was achieved at validation set 0.81 (0.74, 0.88). The calibration curve demonstrated that the prediction outcome was correlated with the observed outcome.

CONCLUSION

This nomogram helps clinicians in predicting the risk of PEW and managing serum potassium in the patients with hemodialysis.