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External validation of the Madrid Acute Kidney Injury Prediction Score

BACKGROUND: The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical p...

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Autores principales: Del Carpio, Jacqueline, Marco, Maria Paz, Martin, Maria Luisa, Craver, Lourdes, Jatem, Elias, Gonzalez, Jorge, Chang, Pamela, Ibarz, Mercedes, Pico, Silvia, Falcon, Gloria, Canales, Marina, Huertas, Elisard, Romero, Iñaki, Nieto, Nacho, Segarra, Alfons
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573016/
https://www.ncbi.nlm.nih.gov/pubmed/34754433
http://dx.doi.org/10.1093/ckj/sfab068
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author Del Carpio, Jacqueline
Marco, Maria Paz
Martin, Maria Luisa
Craver, Lourdes
Jatem, Elias
Gonzalez, Jorge
Chang, Pamela
Ibarz, Mercedes
Pico, Silvia
Falcon, Gloria
Canales, Marina
Huertas, Elisard
Romero, Iñaki
Nieto, Nacho
Segarra, Alfons
author_facet Del Carpio, Jacqueline
Marco, Maria Paz
Martin, Maria Luisa
Craver, Lourdes
Jatem, Elias
Gonzalez, Jorge
Chang, Pamela
Ibarz, Mercedes
Pico, Silvia
Falcon, Gloria
Canales, Marina
Huertas, Elisard
Romero, Iñaki
Nieto, Nacho
Segarra, Alfons
author_sort Del Carpio, Jacqueline
collection PubMed
description BACKGROUND: The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical practice. However, to date, it has not been externally validated. The aim of our study was to perform an external validation of the MAKIPS in a hospital with different characteristics and variable case mix. METHODS: This external validation cohort study of the MAKIPS was conducted in patients admitted to a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using the area under the receiver operating characteristics curve and calibration plots. RESULTS: A total of 5.3% of the external validation cohort had HA-AKI. When compared with the MAKIPS cohort, the validation cohort showed a higher percentage of men as well as a higher prevalence of diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas the prevalence of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and acquired immune deficiency syndrome was significantly lower. In the validation cohort, the MAKIPS showed an area under the curve of 0.798 (95% confidence interval 0.788–0.809). Calibration plots showed that there was a tendency for the MAKIPS to overestimate the risk of HA-AKI at probability rates ˂0.19 and to underestimate at probability rates between 0.22 and 0.67. CONCLUSIONS: The MAKIPS can be a useful tool, using data that are easily obtainable from electronic records, to predict the risk of HA-AKI in hospitals with different case mix characteristics.
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spelling pubmed-85730162021-11-08 External validation of the Madrid Acute Kidney Injury Prediction Score Del Carpio, Jacqueline Marco, Maria Paz Martin, Maria Luisa Craver, Lourdes Jatem, Elias Gonzalez, Jorge Chang, Pamela Ibarz, Mercedes Pico, Silvia Falcon, Gloria Canales, Marina Huertas, Elisard Romero, Iñaki Nieto, Nacho Segarra, Alfons Clin Kidney J Original Article BACKGROUND: The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical practice. However, to date, it has not been externally validated. The aim of our study was to perform an external validation of the MAKIPS in a hospital with different characteristics and variable case mix. METHODS: This external validation cohort study of the MAKIPS was conducted in patients admitted to a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using the area under the receiver operating characteristics curve and calibration plots. RESULTS: A total of 5.3% of the external validation cohort had HA-AKI. When compared with the MAKIPS cohort, the validation cohort showed a higher percentage of men as well as a higher prevalence of diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas the prevalence of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and acquired immune deficiency syndrome was significantly lower. In the validation cohort, the MAKIPS showed an area under the curve of 0.798 (95% confidence interval 0.788–0.809). Calibration plots showed that there was a tendency for the MAKIPS to overestimate the risk of HA-AKI at probability rates ˂0.19 and to underestimate at probability rates between 0.22 and 0.67. CONCLUSIONS: The MAKIPS can be a useful tool, using data that are easily obtainable from electronic records, to predict the risk of HA-AKI in hospitals with different case mix characteristics. Oxford University Press 2021-03-26 /pmc/articles/PMC8573016/ /pubmed/34754433 http://dx.doi.org/10.1093/ckj/sfab068 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of ERA-EDTA. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Del Carpio, Jacqueline
Marco, Maria Paz
Martin, Maria Luisa
Craver, Lourdes
Jatem, Elias
Gonzalez, Jorge
Chang, Pamela
Ibarz, Mercedes
Pico, Silvia
Falcon, Gloria
Canales, Marina
Huertas, Elisard
Romero, Iñaki
Nieto, Nacho
Segarra, Alfons
External validation of the Madrid Acute Kidney Injury Prediction Score
title External validation of the Madrid Acute Kidney Injury Prediction Score
title_full External validation of the Madrid Acute Kidney Injury Prediction Score
title_fullStr External validation of the Madrid Acute Kidney Injury Prediction Score
title_full_unstemmed External validation of the Madrid Acute Kidney Injury Prediction Score
title_short External validation of the Madrid Acute Kidney Injury Prediction Score
title_sort external validation of the madrid acute kidney injury prediction score
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573016/
https://www.ncbi.nlm.nih.gov/pubmed/34754433
http://dx.doi.org/10.1093/ckj/sfab068
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