Relationship Between Patterns in Antihypertensive Drugs Medication and Mortality in Incident Dialysis Patients: A Multicenter Prospective Cohort Study

Carotid Endarterectomy Should Not Be Recommended to End-Stage Kidney Disease Patients with Asymptomatic Carotid Artery Disease

BACKGROUND

Carotid endarterectomy (CEA) for asymptomatic carotid artery disease is advised for patients with low perioperative stroke risk and life expectancy of 3-5 years. We sought to explore the role of risk stratification and postoperative medical management in identifying appropriate asymptomatic candidates for CEA in the end-stage kidney disease (ESKD) population.

METHODS

We identified ESKD patients on dialysis from the United States Renal Data System that underwent CEA (2008-2014) for asymptomatic carotid artery disease. We used the Liu comorbidity index as well as a novel risk prediction model based on Cox proportional hazards model to stratify patients. The primary outcome evaluated was 3-year survival, and Kaplan-Meier methods were used to generate survival estimates. We further conducted a subanalysis of patients with Medicare part D data to determine postoperative usage of the following medications: statins, antiplatelets, and antihypertensives. We evaluated the association of medication utilization and 3-year survival using Kaplan-Meier methods and Cox proportional hazards modeling.

RESULTS

We analyzed 1,813 patients meeting inclusion criteria. The population was predominantly older (mean age 70.2 ± 9.1), White (84.8%), and had a high prevalence of cardiovascular comorbidities, such as hypertension (90.7%), diabetes (62.5%), and congestive heart failure (35.4%). Among the entire cohort, 23.0% had a Liu comorbidity index ≤8, 35.0% had index 9-12, and 42.0% had index >12. Increasing Liu comorbidity index was associated with worse survival (P < 0.01); however, even the group with Liu index ≤8 had poor 3-year survival of 58.8% (53.9-63.4). The Cox proportional hazards model identified variables for inclusion in the risk model such as age >80 (adjusted hazard ratio [aHR] = 2.49, 95% confidence interval [CI] [1.87-3.33], P < 0.001), congestive heart failure (aHR = 1.31, 95% CI [1.14-1.51], P < 0.001), and Liu comorbidity index >12(aHR = 1.89, 95% CI [1.56-2.28], P < 0.001). The risk score generated ranged from 0 to 6.5, and patients were divided into 3 groups: score ≤2 (43.4%), 2-4 (41.2%), and >4 (15.4%). Increasing risk score was associated with worse survival (P < 0.01) but even the "low-risk" group had 3-year survival of 58.5% (54.9-61.9). Subanalysis of the 1,249 (68.8% of total) patients with part D data found that statins and calcium channel blocker use was associated with improved survival, although observed rates for patients on drug were still low.

CONCLUSIONS

The overall long-term survival of ESKD patients undergoing CEA for asymptomatic carotid artery disease is low. Risk stratification and analysis of postoperative medical management did not identify a subgroup of patients with adequate 3-year survival. Hence, the preventive benefits of CEA are not realized in these patients.

Influence of continuity of care on self-management ability and quality of life in outpatient maintenance hemodialysis patients

INTRODUCTION

The objective of this study was to evaluate the effect of continuity of care on self-management ability and quality of life (QOL) in patients undergoing maintenance hemodialysis (MHD).

METHODS

One hundred patients were randomly assigned to the observation group and the control group. In the observation group, patients received a 12-month continuity of care. In the control group, patients were given with routine nursing. Evaluate the patients' self-management ability and QOL between two groups 1 week before discharge and 6 and 12 months outpatient MHD.

RESULTS

Observation group had higher Hemodialysis Self-Management Instrument (HD-SMI) scores and Kidney Disease Quality of Life-Short Form (KDQOL-SF™) scores than control group at 6 and 12 months outpatient MHD. But patients in observation group had a much lower systolic blood pressure than those in control group at 12 months outpatient MHD.

CONCLUSIONS

Our study suggested that continuity of care in the form of online education, telephone visit, and outpatient visit could improve self-management ability and QOL of patients undergoing MHD.

Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients

Some variables including age, comorbidity of diabetes, and so on at dialysis initiation are associated with patient prognosis. Cardiovascular (CV) events are a major cause of death, and adequate models that predict prognosis in dialysis patients are warranted. Therefore, we created models using some variables at dialysis initiation. We used a database of 1,520 consecutive dialysis patients (median age, 70 years; 492 women [32.4%]) from a multicenter prospective cohort study. We established the primary endpoint as a composite of the incidence of first CV events or all-cause death. A multivariable Cox proportional hazard regression model was used to construct a model. We considered a complex and a simple model. We used area under the receiver operating characteristic curve (AUROC) to assess and compare the predictive performances of the prediction models and evaluated the improvement in discrimination using the complex model versus the simple model using net reclassification improvement (NRI). We then assessed integrated discrimination improvement (IDI) to evaluate improvements in average sensitivity and specificity. Of 392 deaths, 152 were CV-related. Totally, 506 CV events occurred during the follow-up period (median 1,285 days). Finally, 692 patients reached the primary endpoint. Baseline data were set at dialysis initiation. AUROC for the primary endpoint was 0.737 (95% confidence interval [CI], 0.712–0.761) in the simple model and 0.765 (95% CI, 0.741–0.788) in the complex model. There were significant intergroup differences in NRI (0.44; 95% CI, 0.34–0.53; p < 0.001) and IDI (0.02; 95% CI, 0.02–0.03; p < 0.001). We prepared a Shiny R application for each model to automatically calculate the predicted occurrence probability (https://statacademy.shinyapps.io/App_inaguma_20190717/). The complex model made more accurate predictions than the simple model. However, the intergroup difference was not significant. Hence, the simple model was more useful than the complex model. The tool was useful in a real-world clinical setting because it required only routinely available variables. Moreover, we emphasized that the tool could predict the incidence of CV events or all-cause mortality for individual patients. In the future, we must confirm its external validity in other prospective cohorts.