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

被引:6
作者
Sharma, Ajay [1 ]
Alvarez, Paula J. [2 ]
Woods, Steven D. [2 ]
Dai, Dingwei [1 ]
机构
[1] Healthagen, 100 Pk Ave, New York, NY 10017 USA
[2] Relypsa Inc, Managed Care Hlth Outcomes, Redwood City, CA USA
来源
CLINICOECONOMICS AND OUTCOMES RESEARCH | 2020年 / 12卷
关键词
chronic kidney disease; hyperkalemia; RAAS inhibitors; potassium binder; SODIUM ZIRCONIUM CYCLOSILICATE; HEART-FAILURE; POTASSIUM; PATIROMER; COST; NEPHROPATHY; RAMIPRIL; SOCIETY; CARE;
D O I
10.2147/CEOR.S267063
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:657 / 667
页数:11
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