Estimating the Association Between Extreme Heat and Acute Kidney Injury Using Serum Creatinine-Derived Case Definitions Restricted; Files Only

Rabin, Benjamin (Spring 2023)

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Introduction: Acute kidney injury (AKI) exacts significant morbidity for patients. In addition to well-known clinical risk factors, extreme heat exposure may contribute to the burden of community-acquired AKI. The use of administrative coded data to identify AKI cases has important limitations in estimating the heat-AKI effect. Applying the Kidney Disease: Improving Global Outcomes (KDIGO) criteria instead may improve AKI surveillance while also differentiating hospital-acquired AKI from community-acquired AKI – critical to evaluating risk related to ambient heat.

Methods: We conducted a case-crossover analysis investigating the relationship between daily temperature and AKI-related emergency department (ED) visits in Atlanta, Georgia during consecutive warm seasons between 2016 and 2019. We created six case definitions for AKI using both ICD-coded data and KDIGO-derived equations. KDIGO definitions identified AKI cases by comparing patients’ ED serum creatinine values to surrogate measures for baseline renal function. The KDIGO definitions were designed a priori to identify community-acquired AKI cases as opposed to hospital-acquired AKI.

Results: 264,415 ED visits across 4 warm seasons were included in our analysis. AKI case numbers ranged from 16,647 events using coded data to 54,320 under the most liberal KDIGO-derived definition. We found positive associations between same-day maximum temperature and ED visits for AKI across six definitions. The strongest heat-AKI effects were observed in the KDIGO definitions derived from the 2021 CKD-EPI equation.

Conclusion: Administrative coded data for AKI may underestimate the incidence of community-acquired AKI as well as its association with extreme heat. Applying the KDIGO criteria represents an alternative approach to better estimate the heat-AKI risk.

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