A new algorithm can predict how many patients will need intensive COVID-related healthcare. This is valuable knowledge when it comes to prioritizing caregivers and ventilators in individual hospitals. The innovation could save lives, according to the UCPH researcher behind the algorithm.
When the COVID-19 pandemic peaked in December of 2020, Danish hospitals were under maximum pressure. Hospital staffs were stretched thin and the Danish Health Authority had to make tough decisions to prioritise treatments. Among other things, this resulted in 35,500 postponed operations.
Now, an innovative algorithm will help alleviate pressure whenever hospitals are confronted by new waves of COVID. Researchers from the University of Copenhagen, among others, have developed the algorithm, which can predict the course of COVID patients' illnesses in relation to how many of them will be highly likely or unlikely to require intensive care or ventilation.
This is important for the allocation of staff across the hospitals in for example Denmark, will cymbalta cause weight gain explains one of the study's authors.
"If we can see that we'll have capacity issues five days out because too many beds are taken at Rigshospitalet, for example, we can plan better and divert patients to hospitals with more space and staffing. As such, our algorithm has the potential save lives," explains Stephan Lorenzen, a postdoc at the University of Copenhagen's Department of Computer Science.
The algorithm uses individual patient data from Sundhedsplatform (the National Health Platform) including information about a patient's gender, age, medications, BMI, whether they smoke or not, blood pressure and more.
This allows the algorithm to predict how many patients, within a one-to-fifteen day time frame, will need intensive care in the form of, for example, ventilators and constant monitoring by nurses and doctors.
Along with colleagues at the University of Copenhagen, as well as researchers at Rigshospitalet and Bispebjerg Hospital, Lorenzen developed the new algorithm based on health data from 42,526 Danish patients who tested positive for the coronavirus between March 2020 and May 2021.
Predicts the number of intensive care patients with 90 percent accuracy
Traditionally, researchers have used regression models to predict Covid-related hospital admissions. However, these models haven't taken individual disease histories, age, gender and other factors into account.
"Our algorithm is based on more detailed data than other models. This means that we can predict the number of patients who will be admitted to intensive care units or who need a ventilator within five days with over 90 percent accuracy," states Stephan Lorenzen.
In fact, the algorithm provides extremely accurate predictions for the likely number of intensive care patients for up to ten days.
"We make better predictions than comparable models because we are able to more accurately map the potential need for ventilators and 24-hour intensive care for up to ten days. Precision decreases slightly beyond that, similar to that of the existing algorithmic models used to predict the course of illness in Covid cases," he elaborates.
In principle, the algorithm is ready to be deployed in Danish hospitals. As such, the researchers are about to begin discussions with relevant health professionals.
"We have shown that data can be used for so incredibly much. And, that we in Denmark, are lucky to have so much health information to draw from. Hopefully, our new algorithm can help our hospitals avoid Covid overload when a new wave of the illness hits," concludes Stephan Lorenzen.
University of Copenhagen – Faculty of Science
Lorenzen, S.S., et al. (2021) Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark. Scientific Reports. doi.org/10.1038/s41598-021-98617-1.
Posted in: Device / Technology News | Medical Research News | Disease/Infection News
Tags: Blood, Blood Pressure, Coronavirus, Hospital, Intensive Care, Machine Learning, Mortality, Pandemic, Ventilator
Source: Read Full Article