Renin-angiotensin-aldosterone system blockade after AKI with or without recovery among US veterans with diabetic kidney disease

Abstract: Significance Statement: Among patients with CKD, optimal use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers after AKI is uncertain. Despite these medications' ability to reduce risk of mortality and other adverse outcomes, there is concern that ACEi/ARB use may delay recovery of kidney function or precipitate recurrent AKI. Prior studies have provided conflicting data regarding the optimal timing of these medications after AKI and have not addressed the role of kidney recovery in determining appropriate timing. This study in US Veterans with diabetes mellitus and proteinuria demonstrated an association between ACEi/ARB use and lower mortality. This association was more pronounced with earlier post-AKI ACEi/ARB use and was not meaningfully affected by initiating ACEis/ARBs before versus after recovery from AKI.BackgroundOptimal use of angiotensin-converting enzyme inhibitors (ACEis) or angiotensin II receptor blockers (ARBs) after AKI is uncertain.MethodsUsing data derived from electronic medical records, we sought to estimate the association between ACEi/ARB use after AKI and mortality in US military Veterans with indications for such treatment (diabetes and proteinuria) while accounting for AKI recovery. We used ACEi/ARB treatment after hospitalization with AKI (defined as serum creatinine ≥50% above baseline concentration) as a time-varying exposure in Cox models. The outcome was all-cause mortality. Recovery was defined as return to ≤110% of baseline creatinine. A secondary analysis focused on ACEi/ARB use relative to AKI recovery (before versus after).ResultsAmong 54,735 Veterans with AKI, 31,146 deaths occurred over a median follow-up period of 2.3 years. Approximately 57% received an ACEi/ARB <3 months after hospitalization. In multivariate analysis with time-varying recovery, post-AKI ACEi/ARB use was associated with lower risk of mortality (adjusted hazard ratio [aHR], 0.74; 95% confidence interval [CI], 0.72 to 0.77). The association between ACEi/ARB use and mortality varied over time, with lower mortality risk associated with earlier initiation (P for interaction with time <0.001). In secondary analysis, compared with those with neither recovery nor ACEi/ARB use, risk of mortality was lower in those with recovery without ACEi/ARB use (aHR, 0.90; 95% CI, 0.87 to 0.94), those without recovery with ACEi/ARB use (aHR, 0.69; 95% CI, 0.66 to 0.72), and those with ACEi/ARB use after recovery (aHR, 0.70; 95% CI, 0.67 to 0.73).ConclusionsThis study demonstrated lower mortality associated with ACEi/ARB use in Veterans with diabetes, proteinuria, and AKI, regardless of recovery. Results favored earlier ACEi/ARB initiation. © 2023 American Society of Nephrology. All rights reserved.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.