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COViage™ Helps Healthcare Providers with Early Identification of Adult Patients in Need of Ventilation
OAKLAND, Calif. – October 2, 2020 – Dascena, Inc., a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, today announced the company has received Emergency Use Authorization (EUA) from the U.S. Food and Drug Administration (FDA) for its Hemodynamic Instability and Respiratory Decompensation Prediction System, COViageTM. COViage, a machine learning algorithm, received EUA for use by healthcare providers in the hospital setting for adult patients with confirmed COVID-19 to assist with the early identification of patients likely to experience hemodynamic instability or respiratory decompensation.
Leveraging Dascena’s Algorithm Development Platform
The COViage system analyzes patient data from electronic health records (EHR) systems and gives healthcare providers advance notification of patients who are predicted to experience unstable blood pressure or respiratory decline requiring mechanical ventilation.
“COVID-19 remains a significant public health emergency both in the U.S. and around the globe, and we are encouraged that by receiving this EUA, our machine learning algorithm can help caregivers diagnose critical conditions resulting from COVID-19 earlier and more accurately,” said Ritankar Das, president and chief executive officer of Dascena. “The early identification of patients at risk of respiratory decompensation or hemodynamic instability would enable physicians to more aggressively monitor these patients in a controlled environment and provide earlier treatment.”
COVID-19 Clinical Trial Outcome: Dascena’s algorithm identified patients likely to require mechanical ventilation with 36% higher accuracy than standard of care
COViage was evaluated in a clinical trial that enrolled 197 patients who visited the emergency department or were admitted to the hospital at five U.S. hospitals between March 24, 2020 and May 4, 2020. Evaluable patients had confirmed COVID-19 diagnoses and their first set of vital sign and lab measurements were taken within two hours of arrival or admission. Data were analyzed by COViage and the standard of care Modified Early Warning Score (MEWS) for comparison. The outcome of respiratory decompensation leading to mechanical ventilation, defined as invasive ventilation requiring endotracheal tube or mechanical ventilation not including BIPAP or CPAP, was assessed 24 hours after model predictions were made. The COViage algorithm achieved an area under the receiver operator characteristic curve of 87% compared to 64% by MEWS (a 36% increase), demonstrating a substantially higher sensitivity and specificity.
“COViage demonstrated the ability to help diagnose respiratory decompensation and hemodynamic instability earlier and more accurately than the standard of care. We are excited to bring this machine learning algorithm to the bedside, which may enable the preservation of many lives and improve allocation of hospital personnel,” said Das.
The data from this trial were published in a paper titled “Prediction of Respiratory Decompensation in COVID-19 Patients Using Machine Learning: The READY Trial,” in the peer-reviewed journal Computers in Biology and Medicine.
Dan Budwick, 1AB