Glycemic Control and Infections Among US Hemodialysis Patients With Diabetes Mellitus - PubMed
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doi: 10.1016/j.ekir.2020.04.020. eCollection 2020 Jul.
Glycemic Control and Infections Among US Hemodialysis Patients With Diabetes Mellitus
1 Department of Medicine, Division of Nephrology, Stanford University School of Medicine, Stanford, California, USA.
2 Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, USA.
3 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
4 Section of Infectious Diseases, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
5 Department of Medicine, University of California at San Francisco, San Francisco, California, USA.
6 Section of Nephrology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
PMID: 32647759
Glycemic Control and Infections Among US Hemodialysis Patients With Diabetes Mellitus
Jinnie J Rhee et al. Kidney Int Rep.
2020
doi: 10.1016/j.ekir.2020.04.020. eCollection 2020 Jul.
Authors
1 Department of Medicine, Division of Nephrology, Stanford University School of Medicine, Stanford, California, USA.
2 Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, USA.
3 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
4 Section of Infectious Diseases, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
5 Department of Medicine, University of California at San Francisco, San Francisco, California, USA.
6 Section of Nephrology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
PMID: 32647759
Abstract
Introduction: Patients with diabetes mellitus (DM) on hemodialysis (HD) may be particularly vulnerable to infections.
Methods: We used merged data from the United States Renal Data System and electronic health records data from a large US dialysis provider to retrospectively examine the association between glycemic control and infections in these patients. Adult patients with DM aged ≥18 years who initiated in-center maintenance HD treatment from 2006 to 2011 and survived >90 days were included. Quarterly mean time-averaged hemoglobin A1c (HbA1c) values were categorized into <5.5%, 5.5 to <6.5%, 6.5 to <7.5%, 7.5 to <8.5%, and ≥8.5%. We used Medicare claims to ascertain infection-related outcomes and the ESRD Death Notification to identify death from infectious cause. We used Cox proportional hazards models to estimate multivariable-adjusted hazard ratios and 95% confidence intervals (CIs) for the associations between time-averaged HbA1c categories and infectious events.
Results: In a cohort of 33,753 eligible patients, those with higher HbA1c levels had higher rates of diabetic foot infections and skin and soft tissue infections, with patients with HbA1c ≥8.5% having 23% (95% CI, 5%, 45%) and 22% (95% CI, 5%, 42%) higher rates, respectively, compared with HbA1c 5.5 to <6.5%. Patients in the lower HbA1c categories had higher rates of infection-related and all-cause mortality (P-for-trend <0.001).
Conclusion: This study highlights the need for greater attention to foot evaluation and skin and soft tissue infections among patients on HD with less than optimal diabetes control.
Keywords: diabetes mellitus; epidemiology; glycemic control; hemodialysis; infections.
© 2020 International Society of Nephrology. Published by Elsevier Inc.
Figures
Study population derived from the…
Figure 1
Study population derived from the United States Renal Data System and electronic health…
Figure 1
Study population derived from the United States Renal Data System and electronic health records of DaVita, Inc. HbA1c, hemoglobin A1c.
Figure 2
(a) Associations between hemoglobin A1c…
Figure 2
(a) Associations between hemoglobin A1c (HbA1c) categories and infection-related outcomes. Model adjusted for…
Figure 2
(a) Associations between hemoglobin A1c (HbA1c) categories and infection-related outcomes. Model adjusted for year of end-stage renal disease (ESRD) incidence; census division (a marker for location); demographic variables, such as age, sex, race/ethnicity, Medicare/Medicaid dual eligibility, and area-level geocoded socioeconomic status (SES) variables such as median rent, median household income, percentage living below the poverty line, percentage unemployed, and percentage with less than high school education; baseline body mass index (BMI) and estimated glomerular filtration rate (eGFR); preexisting comorbidities including heart failure, arrythmias, coronary artery disease, other cardiac disease, peripheral vascular disease, hypertension, chronic obstructive pulmonary disease, current tobacco use, cancer, alcohol dependence, and liver disease; central venous catheter use; baseline laboratory variables such as albumin, platelet count, white blood cell count, and ferritin, as well as time-varying laboratory variables. (b) Associations between HbA1c categories and infection-related and all-cause mortality. Model adjusted for year of ESRD incidence; census division (a marker for location); demographic variables, such as age, sex, race/ethnicity, Medicare/Medicaid dual eligibility, and area-level geocoded SES variables such as median rent, median household income, percentage living below the poverty line, percentage unemployed, and percentage with less than high school education; baseline BMI and eGFR; preexisting comorbidities including heart failure, arrythmias, coronary artery disease, other cardiac disease, peripheral vascular disease, hypertension, chronic obstructive pulmonary disease, current tobacco use, cancer, alcohol dependence, and liver disease; central venous catheter use; baseline laboratory variables such as albumin, platelet count, white blood cell count, and ferritin, as well as time-varying laboratory variables.
Figure 3
Associations between continuous hemoglobin A1c (HbA1c) and infection-related outcomes, including restricted cubic splines…
Figure 3
Associations between continuous hemoglobin A1c (HbA1c) and infection-related outcomes, including restricted cubic splines for HbA1c with knots at the 5th, 35th, 65th, and 95th percentiles. Models were from complete cases analyses and adjusted for year of end-stage renal disease incidence; census division (a marker for location); demographic variables, such as age, sex, race/ethnicity, Medicare/Medicaid dual eligibility, and area-level geocoded socioeconomic status variables such as median rent, median household income, percentage living below the poverty line, percentage unemployed, and percentage with less than high school education; baseline body mass index (BMI) and estimated glomerular filtration rate; preexisting comorbidities including heart failure, arrythmias, coronary artery disease, other cardiac disease, peripheral vascular disease, hypertension, chronic obstructive pulmonary disease, current tobacco use, cancer, alcohol dependence, and liver disease; central venous catheter use; baseline laboratory variables such as albumin, platelet count, white blood cell count, and ferritin, as well as time-varying laboratory variables; and time-varying covariate for other infections. Four separate plots were generated for each of the 4 infectious outcomes of interest, comparing the hazard for each of the outcomes between 2 hypothetical patients who are similar for all covariates at a time t but differ in their average HbA1c measurements, with a fixed referent group of HbA1c 5.5 to <6.5%.
