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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8393922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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