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Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI

Large observational databases linking kidney function and other routine patient health data are increasingly being used to study acute kidney injury (AKI). Routine health care data show an apparent rise in the incidence of population AKI and an increase in acute dialysis. Studies also report an exce...

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Detalles Bibliográficos
Autores principales: Sawhney, Simon, Fraser, Simon D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: W.B. Saunders 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648688/
https://www.ncbi.nlm.nih.gov/pubmed/28778358
http://dx.doi.org/10.1053/j.ackd.2017.05.001
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author Sawhney, Simon
Fraser, Simon D.
author_facet Sawhney, Simon
Fraser, Simon D.
author_sort Sawhney, Simon
collection PubMed
description Large observational databases linking kidney function and other routine patient health data are increasingly being used to study acute kidney injury (AKI). Routine health care data show an apparent rise in the incidence of population AKI and an increase in acute dialysis. Studies also report an excess in mortality and adverse renal outcomes after AKI, although with variation depending on AKI severity, baseline, definition of renal recovery, and the time point during follow-up. However, differences in data capture, AKI awareness, monitoring, recognition, and clinical practice make comparisons between health care settings and periods difficult. In this review, we describe the growing role of large databases in determining the incidence and prognosis of AKI and evaluating initiatives to improve the quality of care in AKI. Using examples, we illustrate this use of routinely collected health data and discuss the strengths, limitations, and implications for researchers and clinicians.
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spelling pubmed-56486882017-10-25 Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI Sawhney, Simon Fraser, Simon D. Adv Chronic Kidney Dis Article Large observational databases linking kidney function and other routine patient health data are increasingly being used to study acute kidney injury (AKI). Routine health care data show an apparent rise in the incidence of population AKI and an increase in acute dialysis. Studies also report an excess in mortality and adverse renal outcomes after AKI, although with variation depending on AKI severity, baseline, definition of renal recovery, and the time point during follow-up. However, differences in data capture, AKI awareness, monitoring, recognition, and clinical practice make comparisons between health care settings and periods difficult. In this review, we describe the growing role of large databases in determining the incidence and prognosis of AKI and evaluating initiatives to improve the quality of care in AKI. Using examples, we illustrate this use of routinely collected health data and discuss the strengths, limitations, and implications for researchers and clinicians. W.B. Saunders 2017-07 /pmc/articles/PMC5648688/ /pubmed/28778358 http://dx.doi.org/10.1053/j.ackd.2017.05.001 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sawhney, Simon
Fraser, Simon D.
Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title_full Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title_fullStr Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title_full_unstemmed Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title_short Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI
title_sort epidemiology of aki: utilizing large databases to determine the burden of aki
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648688/
https://www.ncbi.nlm.nih.gov/pubmed/28778358
http://dx.doi.org/10.1053/j.ackd.2017.05.001
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