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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...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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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. |
format | Online Article Text |
id | pubmed-8573016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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