Risk Factors for Death Among Veterans Following Acute Kidney Injury |
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| Affiliation: | 1. Center for Access Delivery & Research and Evaluation (CADRE) Center, Iowa VA Health Care System, Iowa City;2. Department of Medicine, University of Iowa Carver College of Medicine, Iowa City;1. Department of General Internal Medicine, Tokyo Metropolitan Tama Medical Center, Tokyo, Japan;2. Department of Diagnostic and Generalist Medicine, Dokkyo Medical University Hospital, Tochigi, Japan;1. Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Tokyo, Japan;2. Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan;3. Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan;1. Department of Medicine, Mayo Clinic, Rochester, Minn;2. Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn;3. Division of Hospital Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minn;1. Hospital Regina, Novo Hamburgo, Brazil;2. Latin American Cooperative Oncology Group, Porto Alegre, RS, Brazil;3. Medical School, Unisinos, São Leopoldo, RS, Brazil;4. Thyroid Unit, Division of Endocrinology, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil |
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| Abstract: | BackgroundAcute kidney injury is prevalent among hospitalized veterans, and associated with increased risk of death following discharge. However, risk factors for death following acute kidney injury have not been well defined. We developed a mortality prediction model using Veterans Health Administration data.MethodsThis retrospective cohort study included inpatients from 2013 through 2018 with a creatinine increase of ≥0.3 mg/dL. We evaluated 45 variables for inclusion in our final model, with a primary outcome of 1-year mortality. Bootstrap sampling with replacement was used to identify variables selected in >60% of models using stepwise selection. Best sub-sets regression using Akaike information criteria was used to identify the best-fitting parsimonious model.ResultsA total of 182,683 patients were included, and 38,940 (21.3%) died within 1 year of discharge. The 10-variable model to predict mortality included age, chronic lung disease, cancer within 5 years, unexplained weight loss, dementia, congestive heart failure, hematocrit, blood urea nitrogen, bilirubin, and albumin. Notably, acute kidney injury stage, chronic kidney disease, discharge creatinine, and proteinuria were not selected for inclusion. C-statistics in the primary validation cohorts were 0.77 for the final parsimonious model, compared with 0.52 for acute kidney injury stage alone.ConclusionWe identified risk factors for long-term mortality following acute kidney injury. Our 10-variable model did not include traditional renal variables, suggesting that non-kidney factors contribute to the risk of death more than measures of kidney disease in this population, a finding that may have implications for post-acute kidney injury care. |
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