Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland

Aim: To show whether adding blood glucose to the National Early Warning Score (NEWS) parameters in a machine learning model predicts 30-day
mortality more precisely than the standard NEWS in a prehospital setting.
Methods: In this study, vital sign data prospectively collected from 3632 unselected prehospital patients in June 2015 were used to compare the
standard NEWS to random forest models for predicting 30-day mortality. The NEWS parameters and blood glucose levels were used to develop the
random forest models. Predictive performance on an unknown patient population was estimated with a ten-fold stratified cross-validation method.
Results: All NEWS parameters and blood glucose levels were reported in 2853 (79%) eligible patients. Within 30 days after contact with
ambulance staff, 97 (3.4%) of the analysed patients had died. The area under the receiver operating characteristic curve for the 30-day mortality
of the evaluated models was 0.682 (95% confidence interval [CI], 0.6190.744) for the standard NEWS, 0.735 (95% CI, 0.6790.787) for the
random forest-trained NEWS parameters only and 0.758 (95% CI, 0.7050.807) for the random forest-trained NEWS parameters and blood
glucose. The models predicted secondary outcomes similarly, but adding blood glucose into the random forest model slightly improved its
performance in predicting short-term mortality.
Conclusions: Among unselected prehospital patients, a machine learning model including blood glucose and NEWS parameters had a fair
performance in predicting 30-day mortality.