By Salma Aziz on March, 24 2020
Analyze, identify, and treat patients at risk of sepsis-associated mortality and morbidity as a result of respiratory failure.
Every few years, we are faced with strains of antibiotic resistant bacteria—SARs, Ebola, MERS, H1N1, and most recently COVID-19. Is our healthcare prepared to handle this kind of pandemic as COVID-19 will not be the last one to put a strain on the system.
From what we have seen so far, all these viruses infect a large chunk of the global population over a period of time and eventually take the lives of many. Unlike the flu, COVID-19 is novel to humans, so there is no latent immunity in the global population. The confirmed reported cases are high, but we will never know the real number as there are asymptomatic carriers, making containment efforts futile.
If we look at real-time numbers to understand the situation, 86% of reported cases had a positive outcome where patients recovered and were sent home. However, the mortality rate among the reported closed cases is 14% and changing by the minute: over 18,900 deaths as of today, which is more prevalent among the elderly due to the presence of other preexisting medical conditions (respiratory or cardiovascular illness) that compromise the patient’s health.
Today, researchers around the world are tirelessly working to understand COVID-19, working on vaccines, and even identifying ways to reduce complications in patients fighting COVID-19. In a virtual conference on sepsis and its relation to COVID-19 at the University Hospital Charité in Berlin, experts showed data suggesting that sepsis is diagnosed in 100% of COVID-associated deaths, with respiratory failure as the predominant organ failure.
As there is still no accepted treatment for COVID-19, the hospital provides supportive care to help patients stay alive while the body fights the disease and trying to prevent the onset of sepsis. Can predictive analytics play a role in arming our frontline healthcare workers with automated insights? Can more lives be saved with early sepsis intervention – especially at a time like this?
Related: 4 ways the healthcare industry can benefit from Predictive Analytics
Analytics-led early sepsis intervention
Analytics can serve as a trusted advisor when making a decision that has significant consequences. Nurse-led screening interventions is a common approach adopted in healthcare to improve early sepsis care in the emergency department, but is this method timely, accurate, and scalable?
It is known that mortality from sepsis increases by as much as 8% for every hour that treatment is delayed. Also, as much as 80% of sepsis death could be prevented with rapid diagnosis and treatment. So we ask, where is our healthcare today with adopting predictive analytics to potentially save at-risk patients?
With access to predictive analytics at the point of care, early sepsis intervention and proactive care is possible. Here at Ducen IT, we are passionate about identifying patients at risk of developing sepsis—early in the process—in critical healthcare areas like in emergency departments, surgical units, and the NICU.
We are happy to demonstrate how we have used Analance™ to train models to predict the onset of sepsis in real-time data. Contact us for more information.