A Model to Predict Risk of Hyperkalemia in Patients with Chronic Kidney Disease Using a Large Administrative Claims Database

Early Clinical Experience of Finerenone in People with Chronic Kidney Disease and Type 2 Diabetes in Japan-A Multi-Cohort Study from the FOUNTAIN (FinerenOne mUltidatabase NeTwork for Evidence generAtIoN) Platform

Background: In the phase 3 clinical trials FIGARO-DKD and FIDELIO-DKD, finerenone reduced the risk of cardiovascular and kidney events among people with chronic kidney disease (CKD) and type 2 diabetes (T2D). Evidence regarding finerenone use in real-world settings is limited. Methods: A retrospective cohort study (NCT06278207) using two Japanese nationwide hospital-based databases provided by Medical Data Vision (MDV) and Real World Data Co., Ltd. (RWD Co., Kyoto Japan), converted to the OMOP common data model, was conducted. Persons with CKD and T2D initiating finerenone from 1 July 2021, to 30 August 2023, were included. Baseline characteristics were described. The occurrence of hyperkalemia after finerenone initiation was assessed. Results: 1029 new users of finerenone were included (967 from MDV and 62 from RWD Co.). Mean age was 69.5 and 72.4 years with 27.3% and 27.4% being female in the MDV and RWD Co. databases, respectively. Hypertension (92 and 95%), hyperlipidemia (59 and 71%), and congestive heart failure (60 and 66%) were commonly observed comorbidities. At baseline, 80% of persons were prescribed angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers. Sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists were prescribed in 72% and 30% of the study population, respectively. The incidence proportions of hyperkalemia were 2.16 and 2.70 per 100 persons in the MDV and RWD Co. databases, respectively. There were no hospitalizations associated with hyperkalemia observed in either of the two datasets. Conclusions: For the first time, we report the largest current evidence on the clinical use of finerenone in real-world settings early after the drug authorization in Japan. This early evidence from clinical practice suggests that finerenone is used across comorbidities and comedications.

Impact of treatment of hyperkalaemia on quality of life: design of a prospective observational cohort study of long-term management of hyperkalaemia in patients with chronic kidney disease or chronic heart failure in Japan

INTRODUCTION

Hyperkalaemia (HK) is a frequent complication in patients with chronic kidney disease (CKD) and/or chronic heart failure (CHF). HK must be managed, both to protect patients from its direct clinical adverse outcomes and to enable treatment with disease-modifying therapies including renin-angiotensin-aldosterone system inhibitors. However, the experiences of patients undergoing treatment of HK are not clearly understood. Optimising treatment decisions and improving long-term patient management requires a better understanding of patients' quality of life (QOL). Thus, the aims of this research are: (1) to describe treatment patterns and the impact of treatment on a patient's QOL, (2) to study the relationships between treatment patterns and the impact of treatment on a patient's QOL and (3) to study the relationships between the control of serum potassium (S-K) and the impact of treatment on a patient's QOL, in patients with HK.

METHODS AND ANALYSIS

This is a prospective cohort study with 6 months of follow-up in 30-40 outpatient nephrology and cardiology clinics in Japan. The participants will be 350 patients with CKD or CHF who received their first potassium binders (PB) prescription to treat HK within the previous 6 months. Medical records will be used to obtain information on S-K, on treatment of HK with PBs and with diet, and on the patients' characteristics. To assess the impact of treatment on a patient's QOL, questionnaires will be used to obtain generic health-related QOL, CKD-specific and CHF-specific QOL, and PB-specific QOL. Multivariable regression models will be used to quantify how treatment patterns and S-K control are related to the impact of treatment on a patient's QOL.

ETHICS AND DISSEMINATION

Institutional review boards at all participating facilities review the study protocol. Patient consent will be obtained. The results will be published in international journals.

TRIAL REGISTRATION NUMBER

NCT05297409.

Predicting hyperkalemia in patients with advanced chronic kidney disease using the XGBoost model

BACKGROUND

Hyperkalemia is a common complication of chronic kidney disease (CKD). Hyperkalemia is associated with mortality, CKD progression, hospitalization, and high healthcare costs in patients with CKD. We developed a machine learning model to predict hyperkalemia in patients with advanced CKD at an outpatient clinic.

METHODS

This retrospective study included 1,965 advanced CKD patients between January 1, 2010, and December 31, 2020 in Taiwan. We randomly divided all patients into the training (75%) and testing (25%) datasets. The primary outcome was to predict hyperkalemia (K⁺ > 5.5 mEq/L) in the next clinic vist. Two nephrologists were enrolled in a human-machine competition. The area under the receiver operating characteristic curves (AUCs), sensitivity, specificity, and accuracy were used to evaluate the performance of XGBoost and conventional logistic regression models with that of these physicians.

RESULTS

In a human-machine competition of hyperkalemia prediction, the AUC, PPV, and accuracy of the XGBoost model were 0.867 (95% confidence interval: 0.840-0.894), 0.700, and 0.933, which was significantly better than that of our clinicians. There were four variables that were chosen as high-ranking variables in XGBoost and logistic regression models, including hemoglobin, the serum potassium level in the previous visit, angiotensin receptor blocker use, and calcium polystyrene sulfonate use.

CONCLUSIONS

The XGBoost model provided better predictive performance for hyperkalemia than physicians at the outpatient clinic.

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.

A Nomogram to Identify Hyperkalemia Risk in Patients with Advanced CKD

Background

Hyperkalemia is a common and life-threatening complication of CKD. We aimed to develop and validate a nomogram that could identify the risk of hyperkalemia (≥5.5 mmol/L) in patients with CKD.

Methods

A retrospective cohort study was performed in adult patients with predialysis advanced CKD (stages ≥3) in 2020-2021 for the outcome of hyperkalemia within 6 months. The training set was used to identify risk factors of hyperkalemia. Then a nomogram was developed by multivariable logistic regression analysis. C-statistics, calibration curves, and decision curve analysis (DCA) were used, and the model was validated in the internal and two external validation sets.

Results

In total, 847 patients with advanced CKD were included. In 6 months, 28% of patients had hyperkalemia (234 out of 847). Independent risk factors were: age ≥75 years, higher CKD stages, previous event of serum potassium ≥5.0 mmol/L within 3 months, and comorbidities with heart failure, diabetes, or metabolic acidosis. Then the nomogram on the basis of the risk factors adding the use of renin-angiotensin-aldosterone system inhibitors was constructed. The C-statistic of the model was 0.76 (95% CI, 0.70 to 0.78), and was stable in both the internal validation set (0.73; 95% CI, 0.63 to 0.82) and external validation sets (0.88; 95% CI, 0.84 to 0.95 and 0.82; 95% CI, 0.72 to 0.92). Calibration curves and DCA analysis both found good performances of the nomogram.

Conclusion

A feasible nomogram and online calculator were developed and validated to evaluate the risk of hyperkalemia within 6 months in patients with advanced CKD. Patients with CKD and a high risk of hyperkalemia may benefit from intensive monitoring and early triage.

A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database

Background

To create an appropriate chronic kidney disease (CKD) management program, we developed a predictive model to identify patients in a large administrative claims database with CKD stages 3 or 4 who were at high risk for progression to kidney failure.

Methods

The predictive model was developed and validated utilizing a subset of patients with CKD stages 3 or 4 derived from a large Aetna claims database. The study spanned 36 months, comprised of a 12-month (2015) baseline period and a 24-month (2016-2017) prediction period. All patients were ≥18 years of age and continuously enrolled for 36 months. Multivariate logistic regression was used to develop models. Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts.

Results

Of the 74,114 patients identified as having CKD stages 3 or 4 during the baseline period, 2476 (3.3%) had incident kidney failure during the prediction period. The predictive model included the effect of numerous variables, including age, gender, CKD stage, hypertension (HTN), diabetes mellitus (DM), congestive heart failure, peripheral vascular disease, anemia, hyperkalemia (HK), prospective episode risk group score, and poor adherence to renin-angiotensin-aldosterone system inhibitors. The strongest predictors of progression to kidney failure were CKD stage (4 vs 3), HTN, DM, and HK. The ROC and calibration analyses in the validation sample demonstrated good predictive accuracy (AUROC=0.844) and calibration. The top two prediction deciles identified 70.8% of patients who progressed to kidney failure during the prediction period.

Conclusion

This novel predictive model had good accuracy for identifying, from a large national database, patients with CKD who were at high risk of progressing to kidney failure within 2 years. Early identification using this model could potentially lead to improved health outcomes and reduced healthcare expenditures in this at-risk population.