OBJECTIVES : Limited information exists on the extent of multiple chronic conditions (MCCs) in patients with hyperkalemia. The aims of this study were to quantify the prevalence of individual chronic condition (CC) and MCCs and examine the associations between MCCs and healthcare resource utilization (HRU) and costs.

METHODS : This retrospective cohort study was conducted using an administrative claims database from a US healthcare payer with 5M covered lives. We identified patients with hyperkalemia (ICD-10-CM: E87.5; or serum potassium >5.0 mEq/L; or NDC codes for either patiromer or sodium polystyrene sulfonate) during the study period (1/1/2016 – 6/30/2019). The earliest service/claim date with evidence of hyperkalemia was identified as index date. Qualified patients had at least 12 months of enrollment before and after index date, ≥18 years of age. CCs were assessed using all data within 12 months prior to the index date. HRU and costs were estimated using all data within 12 months after the index date. Association rule mining was applied to identify MCCs. Generalized linear models were used to examine the associations between MCCs and HRU and costs.

RESULTS : Of 22,154 patients with hyperkalemia, 94% had 3 or more CCs (average number of CCs was 7). The most common individual CCs were chronic kidney disease (CKD, 85%), hypertension (HTN, 83%), hyperlipidemia (HLD, 81%), and diabetes mellitus (DM, 47%). The most common dyad combination of CCs was CKD+HTN (71%). The most common triad combination was CKD+HTN+HLD (62%). The most common quartet combination was CKD+HTN+HLD+DM (36%). The increased number of CCs were significantly associated with increased ED visits, length of hospital stays, and total healthcare costs (all P-value <0.0001).

CONCLUSIONS : MCCs are very prevalent among patients with hyperkalemia, and MCCs are strongly associated with HRU and costs. Association rule mining provides a useful new way to study MCCs patterns.