Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.

Abstract

Purpose: Acute Kidney Injury (AKI) is the sudden onset of kidney damage. This damage usually comes without warning and can lead to increased mortality and inpatient costs and is of particular significance to patients undergoing cancer treatment. In previous work, we developed a machine learning algorithm to predict AKI up to 30 days prior to the event, trained on cancer patient data. Here, we validate this model on non-cancer data.

Methods/patients: Medical Information Mart for Intensive Care (MIMIC) is a large, freely available database containing de-identified data from patients who were admitted to the critical care units of the Beth Israel Deaconess Medical Center. Data from 28,498 MIMIC patients were used to validate our algorithm, non-availability of Total Protein measure being the largest removal criterion.

Results and conclusions: Applying our algorithm to MIMIC data generated an AUROC of 0.821 (95% CI 0.820-0.821) per blood test. Our cancer derived algorithm compares positively with other AKI models derived and/or tested on MIMIC, with our model predicting AKI at the longest time frame of up to 30 days. This suggests that our model can achieve a good performance on patient cohorts very different to those from which it was derived, demonstrating the transferability and applicability for implementation in a clinical setting.