Generalized Propensity Score Methods to Assess CKD-Associated Physiologic Factors and Risk of Kidney Failure in the Chronic Renal Insufficiency Cohort (CRIC) Study.

Autor:	Scialla JJ; Division of Nephrology, Department of Medicine, University of Virginia School of Medicine.; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA., Mallawaarachchi I; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA., Illenberger N; Division of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, New York, NY., Brookhart MA; Department of Population Health Sciences, Duke University School of Medicine, Durham, NC., Isakova T; Division of Nephrology and Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL., Mitra N; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA., Ma JZ; Division of Nephrology, Department of Medicine, University of Virginia School of Medicine.; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA.
Jazyk:	angličtina
Zdroj:	American journal of epidemiology [Am J Epidemiol] 2024 Aug 28. Date of Electronic Publication: 2024 Aug 28.
DOI:	10.1093/aje/kwae326
Abstrakt:	Epidemiologic studies have identified many biochemical risk factors for chronic kidney disease (CKD) progression that are correlates of kidney function, termed here 'CKD-associated physiologic factors'. Uncertainty remains if these factors are risk factors or risk markers accounting for aspects of kidney function not otherwise captured. We aimed to use flexible machine learning, a dynamic covariate history including kidney function informative markers, and generalized propensity score (GPS) weighting, to better control confounding for such exposures. We studied 3,052 adults with CKD in the Chronic Renal Insufficiency Cohort Study. We established a 2-year run-in period and assembled 90 variables that characterize variability and trends of selected CKD-associated physiologic factors and confounders. Using SuperLearner, we created a GPS for each CKD-associated physiologic factor and performed GPS-weighted Cox regressions. For context, we also evaluated results from traditional multivariable Cox proportional hazards models as in prior studies. Similar to traditional approaches, bicarbonate, calcium, potassium, hemoglobin, and PTH were each associated with risk of kidney failure using GPS weighting. The GPS approach detected non-linear associations in many factors, some of which were not detected with traditional models. We conclude that many associations between CKD-associated physiologic factors and kidney outcomes remain strong after GPS weighting. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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