Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study

Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study

Background/Purpose: To develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route. Methods: We developed a prediction model using th...

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Published in:Journal of the Formosan Medical Association
Main Author: Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
Format: Article
Language:English
Published: Elsevier B.V. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167977244&doi=10.1016%2fj.jfma.2023.07.011&partnerID=40&md5=441c3a1785fc312942572baf040804cc
id 2-s2.0-85167977244
spelling 2-s2.0-85167977244
Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
2024
Journal of the Formosan Medical Association
123
1
10.1016/j.jfma.2023.07.011
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167977244&doi=10.1016%2fj.jfma.2023.07.011&partnerID=40&md5=441c3a1785fc312942572baf040804cc
Background/Purpose: To develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route. Methods: We developed a prediction model using the classical cross-validation method from the Pan-Asia Trauma Outcomes Study (PATOS) database from 1 January 2015 to 31 December 2020. Eligible patients aged ≥18 years were transported to the hospital by the EMS. The primary outcome (EMS-witnessed TCA) was defined based on changes in vital signs measured on the scene or en route. We included variables that were immediately measurable as potential predictors when EMTs arrived. An integer point value system was built using multivariable logistic regression. The area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration in the derivation and validation cohorts. Results: In total, 74,844 patients were eligible for database review. The model comprised five prehospital predictors: age <40 years, systolic blood pressure <100 mmHg, respiration rate >20/minute, pulse oximetry <94%, and levels of consciousness to pain or unresponsiveness. The AUROC in the derivation and validation cohorts was 0.767 and 0.782, respectively. The HL test revealed good calibration of the model (p = 0.906). Conclusion: We established a prediction model using variables from the PATOS database and measured them immediately after EMS personnel arrived to predict EMS-witnessed TCA. The model allows prehospital medical personnel to focus on high-risk patients and promptly administer optimal treatment. © 2023 Formosan Medical Association
Elsevier B.V.
9296646
English
Article
All Open Access; Gold Open Access
author Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
spellingShingle Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
author_facet Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
author_sort Wang S.-A.; Chang C.-J.; Do Shin S.; Chu S.-E.; Huang C.-Y.; Hsu L.-M.; Lin H.-Y.; Hong K.J.; Jamaluddin S.F.; Son D.N.; Ramakrishnan T.V.; Chiang W.-C.; Sun J.-T.; Huei-Ming Ma M.; Participating Nation Investigators; Tanaka H.; Velasco B.; Khruekarnchana P.; Fares S.; Participating Site Investigators; Rao R.; Abraham G.P.; Bin Mohidin M.A.; Saim A.-H.; Kean L.C.; Anthonysamy C.; Din Mohd Yssof S.J.; Ji K.W.; Kheng C.P.; Ali S.B.M.; Ramanathan P.; Yang C.B.; Chia H.W.; Hamad H.B.; Ismail S.A.; Wan Abdullah W.R.B.; Kimura A.; Gundran C.D.; Convocar P.; Sabarre N.G.; Tiglao P.J.; Song K.J.; Jeong J.; Moon S.W.; Kim J.-Y.; Cha W.C.; Lee S.C.; Ahn J.Y.; Lee K.H.; Yeom S.R.; Ryu H.H.; Kim S.J.; Kim S.C.; Hu R.-H.; Wang R.-F.; Hsieh S.-L.; Kao W.-F.; Riyapan S.; Tianwibool P.; Buaprasert P.; Akaraborworn O.; Al Sakaf O.A.; Huy L.B.; Van Dai N.
title Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
title_short Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
title_full Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
title_fullStr Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
title_full_unstemmed Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
title_sort Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study
publishDate 2024
container_title Journal of the Formosan Medical Association
container_volume 123
container_issue 1
doi_str_mv 10.1016/j.jfma.2023.07.011
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167977244&doi=10.1016%2fj.jfma.2023.07.011&partnerID=40&md5=441c3a1785fc312942572baf040804cc
description Background/Purpose: To develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route. Methods: We developed a prediction model using the classical cross-validation method from the Pan-Asia Trauma Outcomes Study (PATOS) database from 1 January 2015 to 31 December 2020. Eligible patients aged ≥18 years were transported to the hospital by the EMS. The primary outcome (EMS-witnessed TCA) was defined based on changes in vital signs measured on the scene or en route. We included variables that were immediately measurable as potential predictors when EMTs arrived. An integer point value system was built using multivariable logistic regression. The area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration in the derivation and validation cohorts. Results: In total, 74,844 patients were eligible for database review. The model comprised five prehospital predictors: age <40 years, systolic blood pressure <100 mmHg, respiration rate >20/minute, pulse oximetry <94%, and levels of consciousness to pain or unresponsiveness. The AUROC in the derivation and validation cohorts was 0.767 and 0.782, respectively. The HL test revealed good calibration of the model (p = 0.906). Conclusion: We established a prediction model using variables from the PATOS database and measured them immediately after EMS personnel arrived to predict EMS-witnessed TCA. The model allows prehospital medical personnel to focus on high-risk patients and promptly administer optimal treatment. © 2023 Formosan Medical Association
publisher Elsevier B.V.
issn 9296646
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1812871796871921664

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