Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective 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...
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| Published in: | Resuscitation plus Vol. 4; p. 100046 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2020
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| ISSN: | 2666-5204, 2666-5204 |
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| Abstract | 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.
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.
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.
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. |
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| AbstractList | 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.
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.
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.
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. AbstractAim of the studyThe 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. MethodsIn 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. ResultsA 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. ConclusionA 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. 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.AIM OF THE STUDYThe 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.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.METHODSIn 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.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.RESULTSA 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.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.CONCLUSIONA 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. 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. 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. 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. 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. 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. |
| ArticleNumber | 100046 |
| Author | Kallonen, Antti Kuisma, Markku Tamminen, Joonas Hoppu, Sanna Nurmi, Jouni Olkkola, Klaus T. Pirneskoski, Jussi |
| Author_xml | – sequence: 1 givenname: Jussi orcidid: 0000-0001-9248-2235 surname: Pirneskoski fullname: Pirneskoski, Jussi email: jussi.pirneskoski@helsinki.fi organization: Department of Emergency Medicine and Services, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland – sequence: 2 givenname: Joonas surname: Tamminen fullname: Tamminen, Joonas organization: Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland – sequence: 3 givenname: Antti surname: Kallonen fullname: Kallonen, Antti organization: Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland – sequence: 4 givenname: Jouni surname: Nurmi fullname: Nurmi, Jouni organization: Department of Emergency Medicine and Services, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland – sequence: 5 givenname: Markku surname: Kuisma fullname: Kuisma, Markku organization: Department of Emergency Medicine and Services, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland – sequence: 6 givenname: Klaus T. orcidid: 0000-0001-7872-8665 surname: Olkkola fullname: Olkkola, Klaus T. organization: Department of Anaesthesiology, Intensive Care and Pain Medicine, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland – sequence: 7 givenname: Sanna surname: Hoppu fullname: Hoppu, Sanna organization: Emergency Medical Services, Tampere University Hospital, Tampere, Finland |
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| Cites_doi | 10.1016/j.amsu.2018.01.006 10.1016/j.resuscitation.2012.12.016 10.1017/S1049023X19005041 10.1016/j.ijmedinf.2019.02.002 10.1016/j.annemergmed.2017.08.005 10.1186/s13054-019-2351-7 10.1371/journal.pone.0201016 10.1097/CCM.0000000000001571 10.1023/A:1010933404324 10.1016/j.resuscitation.2018.08.028 10.1136/emermed-2018-208309 10.1111/aas.13310 10.1016/j.resuscitation.2014.12.029 10.1136/emermed-2016-206115 10.1097/CCM.0000000000003891 10.1371/journal.pone.0226518 10.1056/NEJMra1814259 |
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| Keywords | Early warning score Cardiac arrest prevention NEWS Machine learning Random forest National Early Warning Score Emergency medical services Prehospital |
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| SubjectTerms | Cardiac arrest prevention Clinical Paper Early warning score Emergency Emergency medical services Machine learning National Early Warning Score NEWS Prehospital Random forest |
| Title | Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study |
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