Search Tag: early detection

2021 19 Apr

Sepsis incidence in very old patients is high and related mortality and morbidity are a major health concern. Frailty and severity of illness, not just age or sepsis diagnosis, are the determinant factors in outcome. Early identification and treatment are decisive in their survival. N owadays, we are facing some important facts: there has...Read more

2018 11 Dec

Acute respiratory distress syndrome (ARDS) results in substantial mortality but remains underdiagnosed in clinical practice. For this reason, automated “sniffer” systems that analyse electronic records have been developed to assist clinicians with ARDS diagnosis. However, a new systematic review found that these sniffer tools had moderate to high predictive...Read more

2017 24 May

Using electronic health records (EHRs) to identify patients in hospital at risk for sepsis is now possible using machine learning. Machine learning does not rely on rules, but is able to learn complex patterns in data without being programmed to do so. Researchers from the University of Pennsylvania Health System presented their study of a machine-learning...Read more

2016 26 Aug

A model using five parameters has sufficient sensitivity and an acceptable false positive rate to identify patients presenting to the emergency department (ED) with sepsis. A group, led by Samuel M. Brown, MD, of Intermountain Medical Center, Utah, developed a probabilistic model to identify septic patients presenting to the emergency department...Read more

2014 14 Oct

Researchers at Penn Medicine have developed an early warning and response tool dubbed the “sepsis sniffer” which uses laboratory data and vital sign readings in patients’ electronic health records to automatically alert physicians when a patient has reached a dangerous threshold and should be stabilised. By identifying those patients at high risk for...Read more