Managing comorbidities in chronic kidney disease reduces utilization and costs.

BACKGROUND: Effective management of comorbid diabetes and hypertension in patients with chronic kidney disease (CKD) is important for optimal outcomes. However, little is known about this relationship from a health plan perspective. The objective of this study was to evaluate the association of effective management of comorbid diabetes and/or hypertension with healthcare resource utilization (HCRU) in patients with chronic kidney disease (CKD). METHODS: This retrospective cohort study used the Humana Research Database to identify patients with CKD Stage ≥ 3a in 2017. Eligible patients were enrolled in a Medicare Advantage Prescription Drug plan for ≥ 12 months before and after the index date (first observed evidence of CKD). Patients with end-stage renal disease, kidney transplant, or hospice election preindex were excluded. Recommended comorbid disease management included hemoglobin A1c monitoring; adherence to glucose-lowering, cardiovascular, and angiotensin-converting enzyme inhibitors/angiotensin receptor blocker medications; and nephrologist/primary care provider (PCP) visits. HCRU was evaluated for 12 months postindex. RESULTS: The final cohort of 241,628 patients was 55% female and 77% White, with an average age of 75 years. Approximately 90% of patients had Stage 3 CKD. Half had both diabetes and hypertension, and most of the remaining half had hypertension without diabetes. Patients meeting the criteria for good disease management, compared with patients not meeting those criteria, were less likely to experience an inpatient hospitalization, by as much as 40% depending on the criterion and the comorbidities present, or an emergency department visit, by as much as 30%. Total monthly healthcare costs were as much as 17% lower. CONCLUSIONS: Management of comorbid diabetes and hypertension in patients with CKD was associated with lower HCRU and costs. Care coordination programs targeting patients with CKD must give careful attention to glucose and blood pressure control. TRIAL REGISTRATION: Not applicable.

Transition-to-dialysis planning, health care use, and mortality in end-stage renal disease.

OBJECTIVES: To evaluate the association of dialysis transition planning factors (eg, nephrologist care, vascular access placement, place of dialysis) with inpatient (IP) stays, emergency department (ED) visits, and mortality. STUDY DESIGN: Retrospective cohort study. METHODS: We used the Humana Research Database to identify 7026 patients with a diagnosis of end-stage renal disease (ESRD) in 2017 who were enrolled in a Medicare Advantage Prescription Drug plan with at least 12 months preindex enrollment, with first ESRD evidence as the index date. Patients with kidney transplant, hospice election, or dialysis preindex were excluded. Transition-to-dialysis planning was defined as optimal (vascular access placed), suboptimal (nephrologist care, but no vascular access), or unplanned (first dialysis in IP stay or ED visit). RESULTS: The cohort was 41% female and 66% White, with a mean age of 70 years. Optimally planned, suboptimally planned, and unplanned transition to dialysis occurred for 15%, 34%, and 44% of the cohort, respectively. Among patients with preindex chronic kidney disease (CKD) stages 3a and 3b, 64% and 55%, respectively, had an unplanned dialysis transition. For patients with preindex CKD stages 4 and 5, 68% and 84%, respectively, had a planned transition. In adjusted models, patients with a suboptimally or optimally planned transition were 57% to 72% less likely to die, 20% to 37% less likely to experience an IP stay, and 80% to 100% more likely to experience an ED visit than patients with an unplanned dialysis transition. CONCLUSIONS: A planned transition to dialysis was associated with reduced odds of IP stays and lower mortality.

Predictive Risk Models to Identify Patients at High-Risk for Severe Clinical Outcomes With Chronic Kidney Disease and Type 2 Diabetes.

INTRODUCTION/OBJECTIVE: Predictive risk models identifying patients at high risk for specific outcomes may provide valuable insights to providers and payers regarding points of intervention and modifiable factors. The goal of our study was to build predictive risk models to identify patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) at high risk for progression to end stage kidney disease (ESKD), mortality, and hospitalization for cardiovascular disease (CVD), cerebrovascular disease (CeVD), and heart failure (HF). METHODS: This was a retrospective observational cohort study utilizing administrative claims data in patients with CKD (stage 3-4) and T2D aged 65 to 89 years enrolled in a Medicare Advantage Drug Prescription plan offered by Humana Inc. between 1/1/2012 and 12/31/2017. Patients were enrolled ≥1 year pre-index and followed for outcomes, including hospitalization for CVD, CeVD and HF, ESKD, and mortality, 2 years post-index. Pre-index characteristics comprising demographic, comorbidities, laboratory values, and treatment (T2D and cardiovascular) were evaluated and included in the models. LASSO technique was used to identify predictors to be retained in the final models followed by logistic regression to generate parameter estimates and model performance statistics. Inverse probability censoring weighting was used to account for varying follow-up time. RESULTS: We identified 169 876 patients for inclusion. Declining estimated glomerular filtration rate (eGFR) increased the risk of hospitalization for CVD (38.6%-61.8%) and HF (2-3 times) for patients with eGFR 15 to 29 mL/min/1.73 m2 compared to patients with eGFR 50 to 59 mL/min/1.73 m2. Patients with urine albumin-to-creatinine ratio (UACR) ≥300 mg/g had greater chance for hospitalization for CVD (2.0 times) and HF (4.9 times), progression to ESKD (2.9 times) and all-cause mortality (2.4 times) than patients with UACR <30 mg/g. Elevated hemoglobin A1c (≥8%) increased the chances for hospitalization for CVD (21.3%), CeVD (45.4%), and death (20.6%). Among comorbidities, history of HF increased the risk for ESKD, mortality, and hospitalization for CVD, CeVD, and HF. CONCLUSIONS: The predictive models developed in this study could potentially be used as decision support tools for physicians and payers, and the risk scores from these models can be applied to future outcomes studies focused on patients with T2D and CKD.

Glomerular filtration rate change and outcomes in type 2 diabetes.

OBJECTIVES: To assess the relationship between relative estimated glomerular filtration rate (eGFR) change and outcomes in patients with type 2 diabetes (T2D). STUDY DESIGN: This retrospective cohort study utilized administrative claims (Humana Research Database) for patients with T2D aged 65 to 89 years, enrolled in a Medicare Advantage plan, with an initial eGFR of 25 to 89 mL/min/1.73m2 in 2008 to 2017, and a second eGFR measurement within 3 to 24 months after the identification date. METHODS: The primary exposure was relative decline in eGFR of 40% or more in a 2-year period. Outcomes included end-stage kidney disease (ESKD) or kidney failure, a composite cardiovascular (CV) outcome, and all-cause mortality assessed with multivariable adjusted survival models. Days out of the home and all-cause total costs were assessed using multivariable adjusted generalized linear models. RESULTS: A total of 288,170 patients were included. The adjusted HR for ESKD or kidney failure was 4.38 (95% CI, 3.99-4.81) in patients with 40% or greater decline versus those with a decline of less than 40%. The adjusted HR was 1.67 (95% CI, 1.53-1.82) for the composite CV outcome and 1.98 (95% CI, 1.87-2.10) for all-cause mortality. Patients with a 40% or greater relative decline had 2.23 times higher all-cause total per patient per month costs ($1910 difference) and 1.82 times higher odds of 7 or more days out of the home versus those with less than 40% relative eGFR decline. CONCLUSIONS: Our results indicate that a relative eGFR decline of 40% or greater is associated with an increased risk of ESKD or kidney failure, CV outcomes and all-cause mortality, and increased health care resource utilization and costs.