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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, USACorrespondence: Dingwei DaiHealthagen, An Affiliate of Aetna Inc., A Part of the CVS Health Family of Companies, 100 Park Ave, New York, NY 10017, USATel +1 212-457-0603Email Dingwei.Dai@Healthagen.comBackground: 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

Supplemental Material, sj-pdf-1-cob-10.1177_26335565221098832 for Multiple comorbid conditions and healthcare resource utilization among adult patients with hyperkalemia: A retrospective observational cohort study using association rule mining by Dingwei Dai, Ajay Sharma, Paula J Alvarez and Steven D Woods in Journal of Multimorbidity and Comorbidity

Dingwei Dai,1 Paula J Alvarez,2 Steven D Woods2 1Clinical Trial Services, Part of the CVS Health Family of Companies, Woonsocket, RI, USA; 2Managed Care Health Outcomes, Vifor Pharma, Inc., Redwood City, CA, USACorrespondence: Dingwei DaiClinical Trial Services, Part of the CVS Health Family of Companies, 101 Park Ave, 15th Floor, Mail Code F109, New York, NY, 10178-2104, USATel +1 215-775-4262Email Dingwei.Dai@cvshealth.comBackground: 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.Keywords: chronic kidney disease, kidney failure, predictive models, administrative claims data, renin-angiotensin-aldosterone system inhibitors, chronic kidney disease management