OAKLAND, Calif. – March 16, 2021 – Dascena, Inc., a machine learning diagnostic algorithm
company that is targeting early disease intervention to improve patient care outcomes, today
announced the publication in Kidney International Reports of results from a study evaluating the
company’s machine learning algorithm, Previse TM , for the earlier prediction of acute kidney injury
(AKI). Findings showed that Previse was able to predict the onset of AKI sooner than the
standard hospital systems, 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).
“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.”
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%, for long-horizon AKI prediction at a 48-hour window prior to onset.
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.
Dascena is developing machine learning diagnostic algorithms to enable early disease
intervention and improve care outcomes for patients. For more information, visit dascena.com