Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality : A retrospective study

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Pysyväisosoite

http://hdl.handle.net/10138/334541

Lähdeviite

Pirneskoski , J , Tamminen , J , Kallonen , A , Nurmi , J , Kuisma , M , Olkkola , K T & Hoppu , S 2020 , ' Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality : A retrospective study ' , Resuscitation plus , vol. 4 , 100046 . https://doi.org/10.1016/j.resplu.2020.100046

Julkaisun nimi: Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality : A retrospective study
Tekijä: Pirneskoski, Jussi; Tamminen, Joonas; Kallonen, Antti; Nurmi, Jouni; Kuisma, Markku; Olkkola, Klaus T.; Hoppu, Sanna
Tekijän organisaatio: Helsinki University Hospital Area
HUS Emergency Medicine and Services
Staff Services
HUS Perioperative, Intensive Care and Pain Medicine
Department of Diagnostics and Therapeutics
Clinicum
Anestesiologian yksikkö
Päiväys: 2020-12
Kieli: eng
Sivumäärä: 7
Kuuluu julkaisusarjaan: Resuscitation plus
ISSN: 2666-5204
DOI-tunniste: https://doi.org/10.1016/j.resplu.2020.100046
URI: http://hdl.handle.net/10138/334541
Tiivistelmä: Aim of the study: The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs. Methods: In this retrospective study, all electronic ambulance mission reports between 2008 and 2015 in a single EMS system were collected. Adult patients (>= 18 years) were included in the analysis. Random forest models with and without blood glucose were compared to the traditional NEWS for predicting one-day mortality. A ten-fold cross-validation method was applied to train and validate the random forest models. Results: A total of 26,458 patients were included in the study of whom 278 (1.0%) died within one day of ambulance mission. The area under the receiver operating characteristic curve for one-day mortality was 0.836 (95% CI, 0.810-0.860) for NEWS, 0.858 (95% CI, 0.832-0.883) for a random forest trained with NEWS variables only and 0.868 (0.843-0.892) for a random forest trained with NEWS variables and blood glucose. Conclusion: A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance.
Avainsanat: Emergency medical services
Prehospital
Cardiac arrest prevention
Early warning score
National Early Warning Score
NEWS
Random forest
Machine learning
IN-HOSPITAL MORTALITY
CARE-UNIT ADMISSION
RISK
3126 Surgery, anesthesiology, intensive care, radiology
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: cc_by
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: publishedVersion


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