The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol

Abstract Background Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are ‘low-acuity’ and do not require t...

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Autores principales: Jamie Miles, Richard Jacques, Janette Turner, Suzanne Mason
Formato: article
Lenguaje:EN
Publicado: BMC 2021
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Acceso en línea:https://doaj.org/article/cf32cb4110b947d181fbf3bd1a29c071
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spelling oai:doaj.org-article:cf32cb4110b947d181fbf3bd1a29c0712021-11-14T12:24:45ZThe Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol10.1186/s41512-021-00108-42397-7523https://doaj.org/article/cf32cb4110b947d181fbf3bd1a29c0712021-11-01T00:00:00Zhttps://doi.org/10.1186/s41512-021-00108-4https://doaj.org/toc/2397-7523Abstract Background Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are ‘low-acuity’ and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED. Methods and analysis All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a ‘non-urgent attendance’. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results. Discussion Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system. Trial Registration This study was retrospectively registered with the ISRCTN: 12121281Jamie MilesRichard JacquesJanette TurnerSuzanne MasonBMCarticleAmbulanceEMSEmergencyTriageAcuityMachine learningMedicine (General)R5-920ENDiagnostic and Prognostic Research, Vol 5, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Ambulance
EMS
Emergency
Triage
Acuity
Machine learning
Medicine (General)
R5-920
spellingShingle Ambulance
EMS
Emergency
Triage
Acuity
Machine learning
Medicine (General)
R5-920
Jamie Miles
Richard Jacques
Janette Turner
Suzanne Mason
The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
description Abstract Background Demand for both the ambulance service and the emergency department (ED) is rising every year and when this demand is excessive in both systems, ambulance crews queue at the ED waiting to hand patients over. Some transported ambulance patients are ‘low-acuity’ and do not require the treatment of the ED. However, paramedics can find it challenging to identify these patients accurately. Decision support tools have been developed using expert opinion to help identify these low acuity patients but have failed to show a benefit beyond regular decision-making. Predictive algorithms may be able to build accurate models, which can be used in the field to support the decision not to take a low-acuity patient to an ED. Methods and analysis All patients in Yorkshire who were transported to the ED by ambulance between July 2019 and February 2020 will be included. Ambulance electronic patient care record (ePCR) clinical data will be used as candidate predictors for the model. These will then be linked to the corresponding ED record, which holds the outcome of a ‘non-urgent attendance’. The estimated sample size is 52,958, with 4767 events and an EPP of 7.48. An XGBoost algorithm will be used for model development. Initially, a model will be derived using all the data and the apparent performance will be assessed. Then internal-external validation will use non-random nested cross-validation (CV) with test sets held out for each ED (spatial validation). After all models are created, a random-effects meta-analysis will be undertaken. This will pool performance measures such as goodness of fit, discrimination and calibration. It will also generate a prediction interval and measure heterogeneity between clusters. The performance of the full model will be updated with the pooled results. Discussion Creating a risk prediction model in this area will lead to further development of a clinical decision support tool that ensures every ambulance patient can get to the right place of care, first time. If this study is successful, it could help paramedics evaluate the benefit of transporting a patient to the ED before they leave the scene. It could also reduce congestion in the urgent and emergency care system. Trial Registration This study was retrospectively registered with the ISRCTN: 12121281
format article
author Jamie Miles
Richard Jacques
Janette Turner
Suzanne Mason
author_facet Jamie Miles
Richard Jacques
Janette Turner
Suzanne Mason
author_sort Jamie Miles
title The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
title_short The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
title_full The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
title_fullStr The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
title_full_unstemmed The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
title_sort safety index of prehospital on scene triage (sinepost) study: the development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene—a protocol
publisher BMC
publishDate 2021
url https://doaj.org/article/cf32cb4110b947d181fbf3bd1a29c071
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