Automatic detection of acute kidney injury episodes from primary care data

Acute kidney injury (AKI) is characterised by a rapid deterioration in kidney function, and can be identified by examining the rate of change in a patient's estimated glomerular filtration rate (eGFR) signal. Due to the potentially irreversible nature of the damage AKI episodes cause to renal function, their detection plays a significant role in predicting a kidney's effectiveness. Although algorithms for the detection of AKI are available for patients under constant monitoring, e.g. inpatients, their applicability to primary care settings is less clear as the eGFR signal often contains large lapses in time between measurements. However, waiting for hospital admittance before AKI is undesirable, as detecting AKI early can help to mitigate the degradation of kidney function and the associated increase in morbidity and mortality. Traditionally, a clinician in a primary care setting would manually identify AKI episodes from direct observation of eGFR signals. While this approach may work for individual patients, the time consuming nature of it precludes quick large-scale monitoring. We therefore present two alternative automated approaches for detecting AKI: as the outlier points when using Gaussian process regression and using a novel technique we entitle Surrey AKI detection algorithm (SAKIDA). Using SAKIDA, we can identify the number of AKI episodes a patient experiences with an accuracy of 70%, when evaluated against the performance of human experts.