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

Resuscitation Plus

Sanna Hoppu¹  Antti Kallonen²  Jari Kalliomäki³  Joonas Tamminen³ 

[1] Corresponding author at: Medical School, University of Tampere and Emergency Medical Services, Tampere University Hospital, PO Box 2000, FI-33521 Tampere, Finland.;Emergency Medical Services, Tampere University Hospital, PO Box 2000, FI-33521 Tampere, Finland;Faculty of Medicine and Health Technology, Tampere University, PO Box 2000, FI-33521 Tampere, Finland;

关键词: Machine learning;    Prehospital;    Risk stratification;    NEWS;   

DOI  :  

来源: DOAJ

【 摘 要 】

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], 0.619–0.744) for the standard NEWS, 0.735 (95% CI, 0.679–0.787) for the random forest-trained NEWS parameters only and 0.758 (95% CI, 0.705–0.807) 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.

【 授权许可】

Unknown