Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest : A multicenter cohort study
Copyright © 2023 Formosan Medical Association. Published by Elsevier B.V. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:123 |
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| Enthalten in: |
Journal of the Formosan Medical Association = Taiwan yi zhi - 123(2024), 1 vom: 07. Jan., Seite 23-35 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Shao-An [VerfasserIn] |
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| Links: |
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| Themen: |
Emergency medical service |
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| Anmerkungen: |
Date Completed 26.01.2024 Date Revised 26.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jfma.2023.07.011 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest |b A multicenter cohort study |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Formosan Medical Association. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Emergency medical service | |
| 650 | 4 | |a Out-of-hospital cardiac arrest | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a Trauma | |
| 650 | 4 | |a Witness | |
| 700 | 1 | |a Chang, Chih-Jung |e verfasserin |4 aut | |
| 700 | 1 | |a Do Shin, Shan |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Sheng-En |e verfasserin |4 aut | |
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| 700 | 1 | |a Hsu, Li-Min |e verfasserin |4 aut | |
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| 700 | 1 | |a Ramakrishnan, T V |e verfasserin |4 aut | |
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| 700 | 1 | |a Sun, Jen-Tang |e verfasserin |4 aut | |
| 700 | 1 | |a Huei-Ming Ma, Matthew |e verfasserin |4 aut | |
| 700 | 0 | |a PATOS Clinical Research Network |e verfasserin |4 aut | |
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| 700 | 1 | |a Tanaka, Hideharu |e investigator |4 oth | |
| 700 | 1 | |a Velasco, Bernadett |e investigator |4 oth | |
| 700 | 1 | |a Hong, Ki Jeong |e investigator |4 oth | |
| 700 | 1 | |a Sun, Jen Tang |e investigator |4 oth | |
| 700 | 1 | |a Khruekarnchana, Pairoj |e investigator |4 oth | |
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| 700 | 1 | |a Bin Mohidin, Mohd Amin |e investigator |4 oth | |
| 700 | 1 | |a Saim, Al-Hilmi |e investigator |4 oth | |
| 700 | 1 | |a Kean, Lim Chee |e investigator |4 oth | |
| 700 | 1 | |a Anthonysamy, Cecilia |e investigator |4 oth | |
| 700 | 1 | |a Din Mohd Yssof, Shah Jahan |e investigator |4 oth | |
| 700 | 1 | |a Ji, Kang Wen |e investigator |4 oth | |
| 700 | 1 | |a Kheng, Cheah Phee |e investigator |4 oth | |
| 700 | 1 | |a Ali, Shamila Bt Mohamad |e investigator |4 oth | |
| 700 | 1 | |a Ramanathan, Periyanayaki |e investigator |4 oth | |
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| 700 | 1 | |a Song, Kyoung Jun |e investigator |4 oth | |
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| 700 | 1 | |a Moon, Sung Woo |e investigator |4 oth | |
| 700 | 1 | |a Kim, Joo-Yeong |e investigator |4 oth | |
| 700 | 1 | |a Cha, Won Chul |e investigator |4 oth | |
| 700 | 1 | |a Lee, Seung Chul |e investigator |4 oth | |
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| 700 | 1 | |a Yeom, Seok Ran |e investigator |4 oth | |
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