Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients
| Autor: | Lauren A. Scanlon, Jorge Barriuso, Matthew Barker-Hewitt, Catherine O'Hara, Alexander Garbett |
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| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
medicine.medical_specialty oncology service urologic and male genital diseases clinical decision making Article medicine Blood test risk factors hospital Prospective cohort study RC254-282 medicine.diagnostic_test business.industry urogenital system Acute kidney injury Neoplasms. Tumors. Oncology. Including cancer and carcinogens Cancer Retrospective cohort study medicine.disease artificial intelligence hematologic tests female genital diseases and pregnancy complications prospective studies Cancer treatment retrospective studies machine learning Oncology acute kidney injury Emergency medicine Oncology patients Complication business early diagnosis |
| Zdroj: | Cancers Cancers, Vol 13, Iss 4182, p 4182 (2021) Volume 13 Issue 16 Scanlon, L A, O’Hara, C, Garbett, A, Barker-Hewitt, M & Barriuso, J 2021, ' Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients ', Cancers, vol. 13, no. 16, 4182 . https://doi.org/10.3390/cancers13164182 |
| ISSN: | 2072-6694 |
| DOI: | 10.3390/cancers13164182 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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