Algorithm Can Predict Acute Kidney Injury Sooner Than Hospital Systems

Dascena Inc. is touting proof that its machine learning algorithm Previse, can predict acute kidney injury (AKI) better than standard hospital systems.

A study evaluating Previse concluded the algorithm predicted the onset of AKI sooner than hospital systems like the XGBoost AKI prediction model and the Sequential Organ Failure Assessment (SOFA), up to 48 hours in advance of onset. Previse has previously received Breakthrough Device designation from the U.S. Food and Drug Administration (FDA).

The study was conducted to evaluate the ability of Previse to predict for Stage 2 or 3 AKI, as defined by KDIGO guidelines, compared to XGBoost and SOFA. Using convolutional neural networks (CNN) and patient Electronic Health Record (EHR) data, 12,347 patient encounters were analyzed, and measurements included Area Under the Receiver Operating Characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advanced prediction of AKI onset. Findings from the study demonstrated that on a hold-out test set, the algorithm attained an AUROC of 0.86, compared to 0.65 and 0.70 for XGBoost and SOFA, respectively, and PPV of 0.24, relative to a cohort AKI prevalence of 7.62 percent, for long-horizon AKI prediction at a 48-hour window prior to onset.
 
“AKI is a severe and complex condition that presents in many hospitalized patients, yet it is often diagnosed too late, resulting in significant kidney injury with no effective treatments to reverse damage and restore kidney function,” said David Ledbetter, chief clinical officer of Dascena. “If we are able to predict AKI onset earlier, physicians may be able to intervene sooner, reducing the damaging effects. These findings with Previse are exciting and further demonstrate the role we believe machine learning algorithms can play in disease prediction. Further, with Breakthrough Device designation from the FDA, we hope to continue to efficiently advance Previse through clinical studies so that we may be able to positively impact as many patients as possible through earlier detection.”
 
Previse is an algorithm that continuously monitors hospitalized patients and can predict acute kidney injury more than a full day before patients meet the clinical criteria for diagnosis, providing clinicians with ample time to intervene and prevent long-term injury.