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Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients

Acute kidney injury (AKI) is a common complication among oncology patients associated with lower remission rates and higher mortality. To reduce the impact of this condition, we aimed to predict AKI earlier than existing tools, to allow clinical intervention before occurrence. We trained a random fo...

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Autores principales: Scanlon, Lauren A., O’Hara, Catherine, Garbett, Alexander, Barker-Hewitt, Matthew, Barriuso, Jorge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393922/
https://www.ncbi.nlm.nih.gov/pubmed/34439336
http://dx.doi.org/10.3390/cancers13164182
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author Scanlon, Lauren A.
O’Hara, Catherine
Garbett, Alexander
Barker-Hewitt, Matthew
Barriuso, Jorge
author_facet Scanlon, Lauren A.
O’Hara, Catherine
Garbett, Alexander
Barker-Hewitt, Matthew
Barriuso, Jorge
author_sort Scanlon, Lauren A.
collection PubMed
description Acute kidney injury (AKI) is a common complication among oncology patients associated with lower remission rates and higher mortality. To reduce the impact of this condition, we aimed to predict AKI earlier than existing tools, to allow clinical intervention before occurrence. We trained a random forest model on 597,403 routinely collected blood test results from 48,865 patients undergoing cancer treatment at The Christie NHS Foundation Trust between January 2017 and May 2020, to identify AKI events upcoming in the next 30 days. AKI risk levels were assigned to upcoming AKI events and tested through a prospective analysis between June and August 2020. The trained model gave an AUROC of 0.881 (95% CI 0.878–0.883), when assessing predictions per blood test for AKI occurrences within 30 days. Assigning risk levels and testing the model through prospective validation from the 1st June to the 31st August identified 73.8% of patients with an AKI event before at least one AKI occurrence, 61.2% of AKI occurrences. Our results suggest that around 60% of AKI occurrences experienced by patients undergoing cancer treatment could be identified using routinely collected blood results, allowing clinical remedial action to be taken and disruption to treatment by AKI to be minimised.
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spelling pubmed-83939222021-08-28 Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients Scanlon, Lauren A. O’Hara, Catherine Garbett, Alexander Barker-Hewitt, Matthew Barriuso, Jorge Cancers (Basel) Article Acute kidney injury (AKI) is a common complication among oncology patients associated with lower remission rates and higher mortality. To reduce the impact of this condition, we aimed to predict AKI earlier than existing tools, to allow clinical intervention before occurrence. We trained a random forest model on 597,403 routinely collected blood test results from 48,865 patients undergoing cancer treatment at The Christie NHS Foundation Trust between January 2017 and May 2020, to identify AKI events upcoming in the next 30 days. AKI risk levels were assigned to upcoming AKI events and tested through a prospective analysis between June and August 2020. The trained model gave an AUROC of 0.881 (95% CI 0.878–0.883), when assessing predictions per blood test for AKI occurrences within 30 days. Assigning risk levels and testing the model through prospective validation from the 1st June to the 31st August identified 73.8% of patients with an AKI event before at least one AKI occurrence, 61.2% of AKI occurrences. Our results suggest that around 60% of AKI occurrences experienced by patients undergoing cancer treatment could be identified using routinely collected blood results, allowing clinical remedial action to be taken and disruption to treatment by AKI to be minimised. MDPI 2021-08-20 /pmc/articles/PMC8393922/ /pubmed/34439336 http://dx.doi.org/10.3390/cancers13164182 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Scanlon, Lauren A.
O’Hara, Catherine
Garbett, Alexander
Barker-Hewitt, Matthew
Barriuso, Jorge
Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title_full Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title_fullStr Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title_full_unstemmed Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title_short Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
title_sort developing an agnostic risk prediction model for early aki detection in cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393922/
https://www.ncbi.nlm.nih.gov/pubmed/34439336
http://dx.doi.org/10.3390/cancers13164182
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