Upvote Story 5

Characterising Risk of In-Hospital Mortality Following Cardiac Arrest Using Machine Learning

Characterising Risk of In-Hospital Mortality Following Cardiac Arrest Using Machine Learning

Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.

CriticalCare.news
January 8, 2019

Leave Your Comment