Major Adverse Kidney Events in the American Indian or Alaska Native Population with Diabetes

医学 人口 危险系数 糖尿病 肾脏疾病 人口学 比例危险模型 透析 队列 肾功能 置信区间 倾向得分匹配 队列研究 老年学 内科学 体质指数 人口研究 环境卫生 终末期肾病 相对风险
作者
Anna M. Zemke,Kiara Mayhand,Radica Z. Alicic,Lindsey M. Kornowske,Cami R. Jones,Kenn B. Daratha,Christina Reynolds,Susanne B. Nicholas,Panayiotis Petousis,Leonid Shpaner,Joshua J. Neumiller,Keith C. Norris,Nisha Bansal,Katherine R. Tuttle
出处
期刊:Clinical Journal of The American Society of Nephrology [Lippincott Williams & Wilkins]
标识
DOI:10.2215/cjn.0000001121
摘要

KEY POINTS: Knowledge of the risk of major adverse kidney events in the American Indian or Alaska Native population with diabetes is limited. Compared with the non-Hispanic White population, the American Indian or Alaska Native population was younger with poorer glycemic control. This population had a 20% higher risk of major adverse kidney events with social drivers of health and health care utilization as key predictors. BACKGROUND: CKD and diabetes disproportionately affect the American Indian (AI) or Alaska Native (AN) population, but understanding of major adverse kidney events (MAKE) in this population is limited. METHODS: Electronic health records from the Providence health system identified the AI or AN adult population with diabetes during 2013-2022. A 1-year window surrounding diabetes cohort entry was used to collect baseline data. Kaplan-Meier analyses assessed MAKE (≥40% eGFR decline, eGFR <15 ml/min per 1.73 m 2 , dialysis or kidney transplant, and all-cause death) with propensity score matching (1:3) of AI or AN people to reference individuals (non-Hispanic White) by demographics and clinical characteristics. Cox proportional hazards modeling estimated associations between demographic, clinical, social, and health care utilization variables and MAKE. RESULTS: The AI or AN population ( N =6103) was younger (mean±SD age 54±15 years) with higher hemoglobin A1c (mean 7.4±2.2 mg/dl) compared with the reference population ( N =354,283; age 62±14 years; hemoglobin A1c 6.9%±1.8%). During a median follow-up of 4.1 (interquartile range, 2.0-6.4) years, the AI or AN population experienced a higher frequency of MAKE (26%, n =1614) than the reference population (24%, n =85,920). With propensity score matching, MAKE survival estimates were significantly lower in the AI or AN population ( P < 0.0001). In the adjusted Cox model, increased MAKE risk was observed for the AI or AN population (versus reference population, hazard ratio [HR] 1.20, 95% confidence interval [CI], 1.15 to 1.27), higher social vulnerability index (HR, 1.05; 95% CI, 1.05 to 1.05), and hospitalization (HR, 1.98; 95% CI, 1.95 to 2.01), while more primary care visits were associated with lower MAKE risk (HR, 0.78; 95% CI, 0.77 to 0.79). CONCLUSION: The risk of MAKE was significantly increased in the AI or AN population with diabetes. Social factors and health care utilization importantly contributed to risk prediction for MAKE.

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