New algorithm uses disease history to predict intensive care patients' chances of survival

ScienceDaily | 5/24/2019 | Staff
Every year, tens of thousands of patients are admitted to intensive care units throughout Denmark. Determining which treatment is best for the individual patient is a great challenge. To make this decision, doctors and nurses use various methods to try to predict the patient's chances of survival and mortality. However, the existing methods can be significantly improved.

Therefore, researchers from the Faculty of Health and Medical Sciences at the University of Copenhagen and Rigshospitalet have developed a new algorithm which much more accurately predicts an intensive care patient's chances of surviving. Their research has been published in the scientific journal Lancet Digital Health.

'We - Health - Data - Way - Algorithm

'We have used Danish health data in a new way, using an algorithm to analyse file data from the individual patient's disease history. The Danish National Patient Registry contains data on the disease history of millions of Danes, and in principle the algorithm is able to draw on the history of the individual citizen of benefit to the individual patient in connection with treatment,' says Professor Søren Brunak from the Novo Nordisk Foundation Center for Protein Research.

Developing the algorithm, the researchers used data on more than 230,000 patients admitted to intensive care units in Denmark in the period 2004-2016. In the study the algorithm analysed the individual patient's disease history, covering as much as 23 years.

Time - Calculations - Measurements - Tests - Hours

At the same time, they included in their calculations measurements and tests made during the first 24 hours of the admission in question. The result was a significantly more accurate prediction of the patient's mortality risk than offered by existing methods.

'Excessive treatment is a serious risk among terminally ill patients treated in Danish intensive care units. Doctors and nurses have...
(Excerpt) Read more at: ScienceDaily
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