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

Authors Sharma A, Alvarez PJ, Woods SD, Dai D

Received 7 July 2020

Accepted for publication 22 October 2020

Published 9 November 2020 Volume 2020:12 Pages 657—667


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Giorgio Lorenzo Colombo

Ajay Sharma,1 Paula J Alvarez,2 Steven D Woods,2 Dingwei Dai1

1Healthagen, An Affiliate of Aetna Inc., A Part of the CVS Health Family of Companies, New York, NY, USA; 2Managed Care Health Outcomes, Relypsa, Inc., a Vifor Pharma Group Company, Redwood City, CA, USA

Correspondence: Dingwei Dai
Healthagen, An Affiliate of Aetna Inc., A Part of the CVS Health Family of Companies, 100 Park Ave, New York, NY 10017, USA
Tel +1 212-457-0603

Background: Chronic kidney disease (CKD) is responsible for substantial clinical and economic burden. Drugs that inhibit the renin-angiotensin-aldosterone system inhibitors (RAASi) slow CKD progression in many common clinical scenarios. Guideline-directed medical therapy requires maximal recommended doses of RAASi, which clinicians are often reluctant to prescribe because of the associated risk of hyperkalemia (HK).
Objective: This study aims to develop and validate a model to identify individuals with CKD at elevated risk for developing HK over a 12-month period on the basis of lab, medical, and pharmacy claims.
Methods: Using claims from a large US healthcare payer, we developed a model to predict the probability of individuals identified with CKD but not HK in 2016 (baseline year [BY]) who developed HK in 2017 (prediction year [PY]). The study population was comprised of members continuously enrolled with medical and pharmacy benefits and CKD (BY). Members were excluded from the analysis if they had HK (by lab results or diagnosis code) or dialysis (BY). Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts.
Results: Of 435,512 members identified with CKD but not HK (BY), 6235 (1.43%) showed incident HK (PY). Compared with individuals without incident HK (PY), these members had a higher comorbidity burden, use of RAASi, and healthcare utilization. The AUROC and calibration analyses showed good predictive accuracy (area under the curve [AUC]=0.843 and calibration). The top 2 HK-prediction deciles identified 75.94% of members who went on to develop HK (PY).
Conclusion: Guideline-recommended doses of RAASi therapy can be limited by the risk of HK. Novel potassium binders may permit more patients at risk to benefit from these maximal RAASi doses. This predictive model successfully identified the risk of developing HK up to 1 year in advance.

Keywords: chronic kidney disease, hyperkalemia, RAAS inhibitors, potassium binder

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