HOBOKEN, N.J., Dec. 18, 2019 /PRNewswire/ -- Autonomous Healthcare, Inc. today announced the recent publication of a research study in the Journal of Clinical Monitoring and Computing validating a critical component of its smart ventilation management technology. The study results showed that compared to clinicians, Autonomous Healthcare's Syncron-E™ system was twice as sensitive in detecting a specific patient-ventilator asynchrony event referred to as ineffective triggering.
Many patients in the intensive care unit (ICU) require mechanical ventilation due to lung injury or for other conditions. Patient-ventilator asynchrony (also called "fighting the ventilator") is a well-recognized problem in ICUs and recent studies have shown associations between asynchrony and mortality. Syncron-E™ developed by Autonomous Healthcare is a software technology that can analyze airway pressure and flow waveforms readily available from ventilators to detect various types of patient-ventilator asynchrony.
In this retrospective study, pressure and flow tracings of seven patients were analyzed by Syncron-E™ and independently by three clinicians to detect ineffective triggering, a condition where a patient's attempt to trigger a breath is not recognized by the ventilator. The analysis of Syncron-E™ and the panel of clinicians were later compared with diaphragm activity measurements, an invasive measurement technique currently considered the gold standard for detecting asynchrony. The results showed that while both Syncron-E™ and the clinicians had high specificity (98.5% and 99.3%, respectively), Syncron-E™ had a significantly higher sensitivity (83.2% vs. 41.1%).
"The first step needed to address the problem of patient-ventilator asynchrony is recognition and unfortunately because of constraints on healthcare providers, patients may be fighting the ventilator well before recognition by the providers," said James Bailey, MD, PhD, Chief Medical Officer of Autonomous Healthcare. "An automated system for real-time analysis and detection of asynchrony was an especially compelling problem to tackle that plays right into the strengths of intelligent pattern recognition algorithms," said Timothy Phan, Director of Engineering of Autonomous Healthcare. Syncron-E™ is used for investigational purposes, requires additional studies to establish safety and efficacy, and is not approved by the FDA.
About Autonomous Healthcare
Autonomous Healthcare is building a platform for smart patient management for intensive care units and operating rooms with the ultimate goal of reducing morbidity and mortality by leveraging recent advancements in machine learning and feedback control theory.
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Many patients in the intensive care unit (ICU) require mechanical ventilation due to lung injury or for other conditions. Patient-ventilator asynchrony (also called "fighting the ventilator") is a well-recognized problem in ICUs and recent studies have shown associations between asynchrony and mortality. Syncron-E™ developed by Autonomous Healthcare is a software technology that can analyze airway pressure and flow waveforms readily available from ventilators to detect various types of patient-ventilator asynchrony.
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In this retrospective study, pressure and flow tracings of seven patients were analyzed by Syncron-E™ and independently by three clinicians to detect ineffective triggering, a condition where a patient's attempt to trigger a breath is not recognized by the ventilator. The analysis of Syncron-E™ and the panel of clinicians were later compared with diaphragm activity measurements, an invasive measurement technique currently considered the gold standard for detecting asynchrony. The results showed that while both Syncron-E™ and the clinicians had high specificity (98.5% and 99.3%, respectively), Syncron-E™ had a significantly higher sensitivity (83.2% vs. 41.1%).
"The first step needed to address the problem of patient-ventilator asynchrony is recognition and unfortunately because of constraints on healthcare providers, patients may be fighting the ventilator well before recognition by the providers," said James Bailey, MD, PhD, Chief Medical Officer of Autonomous Healthcare. "An automated system for real-time analysis and detection of asynchrony was an especially compelling problem to tackle that plays right into the strengths of intelligent pattern recognition algorithms," said Timothy Phan, Director of Engineering of Autonomous Healthcare. Syncron-E™ is used for investigational purposes, requires additional studies to establish safety and efficacy, and is not approved by the FDA.
About Autonomous Healthcare
Autonomous Healthcare is building a platform for smart patient management for intensive care units and operating rooms with the ultimate goal of reducing morbidity and mortality by leveraging recent advancements in machine learning and feedback control theory.
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SOURCE Autonomous Healthcare, Inc.
