Researchers at Ben-Gurion University of the Negev (BGU), Hadassah Medical Organization (HMO), and Cincinnati Children’s Hospital Medical Center (CCH) have developed a new life-saving monitoring technology called VitalMiner that predicts hemodynamic instability episodes in intensive care patients.

Hemodynamic instability in blood pressure, particularly hypotension, is considered one of the most critical events that requires prompt and effective intensive care unit (ICU) intervention. Hemodynamically unstable patients do not have enough pressure in the circulatory system to keep blood flowing reliably to all parts of the body at the same time, which can lead to organ failure. Once patients become unstable, treatment is much more difficult, with an increased chance of morbidity or mortality.

VitalMiner monitors real-time vital signs and applies advanced algorithms to predict impending hemodynamic instability episodes (eg, hemorrhagic shock) before they appear, which is a critical intervention window for maximum success.

This software can connect either locally or remotely to clinical information systems and vital signs monitors in a broad range of settings, including civilian and military ICUs, emergency rooms, intensive care transports (mobile, fixed, and rotatory wing), and home intensive care services.

VitalMiner’s predictive capability showed an improvement of up to 6% in sensitivity and up to 13% specificity compared to existing approaches at three hospitals.

The software was developed by Professor Mark Last of the BGU Department of Software and Information Systems Engineering, in collaboration with Professor Victor F. Garcia MD, founding director of Trauma Services at CCH and professor of surgery and pediatrics at the University of Cincinnati College of Medicine, and Professor Raphael Udassin in the Pediatric Surgery Department of Hadassah Hospital Ein Kerem.

“Hemodynamic instability is one of the most severe and life-threatening complications encountered in an ICU setting,” says Garcia. “Having the ability to predict a patient’s physiological deterioration earlier by using ‘smart’ monitoring software and machine-learning algorithms will save lives and enable better, informed resuscitation of the critically ill and injured.”

The investigators’ preliminary findings suggest the new system may be able to predict hemodynamic instability up to several hours before currently available monitors and modalities are capable of detecting a major clinical manifestation.