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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/13/16/4182 |
| _version_ | 1797524352973930496 |
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| author | Lauren A. Scanlon Catherine O’Hara Alexander Garbett Matthew Barker-Hewitt Jorge Barriuso |
| author_facet | Lauren A. Scanlon Catherine O’Hara Alexander Garbett Matthew Barker-Hewitt Jorge Barriuso |
| author_sort | Lauren A. Scanlon |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T08:56:09Z |
| format | Article |
| id | doaj.art-0a459292399943bfa1846837325ee0a7 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T08:56:09Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-0a459292399943bfa1846837325ee0a72023-11-22T07:05:01ZengMDPI AGCancers2072-66942021-08-011316418210.3390/cancers13164182Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer PatientsLauren A. Scanlon0Catherine O’Hara1Alexander Garbett2Matthew Barker-Hewitt3Jorge Barriuso4The Christie NHS Foundation Trust, Manchester M20 4BX, UKThe Christie NHS Foundation Trust, Manchester M20 4BX, UKThe Christie NHS Foundation Trust, Manchester M20 4BX, UKThe Christie NHS Foundation Trust, Manchester M20 4BX, UKDivision of Cancer Sciences, Manchester Cancer Research Centre, The University of Manchester, Manchester M13 9PL, UKAcute 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.https://www.mdpi.com/2072-6694/13/16/4182acute kidney injuryartificial intelligenceclinical decision makingearly diagnosishematologic testsmachine learning |
| spellingShingle | Lauren A. Scanlon Catherine O’Hara Alexander Garbett Matthew Barker-Hewitt Jorge Barriuso Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients Cancers acute kidney injury artificial intelligence clinical decision making early diagnosis hematologic tests machine learning |
| 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 | acute kidney injury artificial intelligence clinical decision making early diagnosis hematologic tests machine learning |
| url | https://www.mdpi.com/2072-6694/13/16/4182 |
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