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dc.contributor.authorScanlon, Lauren A
dc.contributor.authorO'Hara, Catherine
dc.contributor.authorGarbett, Alexander
dc.contributor.authorBarker-Hewitt, Matthew
dc.contributor.authorBarriuso, Jorge
dc.date.accessioned2021-09-07T13:16:10Z
dc.date.available2021-09-07T13:16:10Z
dc.date.issued2021en
dc.identifier.citationScanlon LA, O’Hara C, Garbett A, Barker-Hewitt M, Barriuso J. Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients. Cancers. 2021 Aug 20;13(16):4182.en
dc.identifier.pmid34439336en
dc.identifier.doi10.3390/cancers13164182en
dc.identifier.urihttp://hdl.handle.net/10541/624510
dc.description.abstractAcute 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.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.3390/cancers13164182en
dc.titleDeveloping an agnostic risk prediction model for early AKI detection in cancer patientsen
dc.typeArticleen
dc.contributor.departmentThe Christie NHS Foundation Trust, Manchester M20 4BXen
dc.identifier.journalCancersen
dc.description.noteen]
refterms.dateFOA2021-09-08T13:34:29Z


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