Original Article 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 t...
Published in: | JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION |
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Main Authors: | , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
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ELSEVIER TAIWAN
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001170833600001 |
author |
Wang Shao-An; Chang Chih-Jung; Do Shin Shan; Chu Sheng-En; Huang Chun-Yen; Hsu Li -Min; Lin Hao-Yang; Hong Ki Jeong; Jamaluddin Sabariah Faizah; Son Do Ngoc; Ramakrishnan T. V.; Chiang Wen-Chu; Sun Jen-Tang; Ma Matthew Huei-Ming |
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Wang Shao-An; Chang Chih-Jung; Do Shin Shan; Chu Sheng-En; Huang Chun-Yen; Hsu Li -Min; Lin Hao-Yang; Hong Ki Jeong; Jamaluddin Sabariah Faizah; Son Do Ngoc; Ramakrishnan T. V.; Chiang Wen-Chu; Sun Jen-Tang; Ma Matthew Huei-Ming Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study General & Internal Medicine |
author_facet |
Wang Shao-An; Chang Chih-Jung; Do Shin Shan; Chu Sheng-En; Huang Chun-Yen; Hsu Li -Min; Lin Hao-Yang; Hong Ki Jeong; Jamaluddin Sabariah Faizah; Son Do Ngoc; Ramakrishnan T. V.; Chiang Wen-Chu; Sun Jen-Tang; Ma Matthew Huei-Ming |
author_sort |
Wang |
spelling |
Wang, Shao-An; Chang, Chih-Jung; Do Shin, Shan; Chu, Sheng-En; Huang, Chun-Yen; Hsu, Li -Min; Lin, Hao-Yang; Hong, Ki Jeong; Jamaluddin, Sabariah Faizah; Son, Do Ngoc; Ramakrishnan, T. V.; Chiang, Wen-Chu; Sun, Jen-Tang; Ma, Matthew Huei-Ming Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION English Article 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 Z 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. Copyright (c) 2023, Formosan Medical Association. ELSEVIER TAIWAN 0929-6646 1876-0821 2024 123 1 10.1016/j.jfma.2023.07.011 General & Internal Medicine gold WOS:001170833600001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001170833600001 |
title |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
title_short |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
title_full |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
title_fullStr |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
title_full_unstemmed |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
title_sort |
Original Article Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study |
container_title |
JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION |
language |
English |
format |
Article |
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 Z 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. Copyright (c) 2023, Formosan Medical Association. |
publisher |
ELSEVIER TAIWAN |
issn |
0929-6646 1876-0821 |
publishDate |
2024 |
container_volume |
123 |
container_issue |
1 |
doi_str_mv |
10.1016/j.jfma.2023.07.011 |
topic |
General & Internal Medicine |
topic_facet |
General & Internal Medicine |
accesstype |
gold |
id |
WOS:001170833600001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001170833600001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809678796866977792 |