Literature DB >> 30725101

Development and Validation of a Prediction Model for Prehospital Triage of Trauma Patients.

Eveline A J van Rein1, Rogier van der Sluijs1, Frank J Voskens1, Koen W W Lansink2,3, R Marijn Houwert1,3, Rob A Lichtveld4, Mariska A de Jongh5, Marcel G W Dijkgraaf6, Howard R Champion7,8, Frank J P Beeres9, Luke P H Leenen1, Mark van Heijl1,10.   

Abstract

Importance: Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective: To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants: In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures: A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region.
Results: Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance: The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals.

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Mesh:

Year:  2019        PMID: 30725101      PMCID: PMC6537785          DOI: 10.1001/jamasurg.2018.4752

Source DB:  PubMed          Journal:  JAMA Surg        ISSN: 2168-6254            Impact factor:   14.766


  41 in total

1.  A multisite assessment of the American College of Surgeons Committee on Trauma field triage decision scheme for identifying seriously injured children and adults.

Authors:  Craig D Newgard; Dana Zive; James F Holmes; Eileen M Bulger; Kristan Staudenmayer; Michael Liao; Thomas Rea; Renee Y Hsia; N Ewen Wang; Ross Fleischman; Jonathan Jui; N Clay Mann; Jason S Haukoos; Karl A Sporer; K Dean Gubler; Jerris R Hedges
Journal:  J Am Coll Surg       Date:  2011-12       Impact factor: 6.113

2.  A national evaluation of the effect of trauma-center care on mortality.

Authors:  Ellen J MacKenzie; Frederick P Rivara; Gregory J Jurkovich; Avery B Nathens; Katherine P Frey; Brian L Egleston; David S Salkever; Daniel O Scharfstein
Journal:  N Engl J Med       Date:  2006-01-26       Impact factor: 91.245

3.  Comparison of injury severity between AIS 2005 and AIS 1990 in a large injury database.

Authors:  J Barnes; A Hassan; R Cuerden; R Cookson; J Kohlhofer
Journal:  Ann Adv Automot Med       Date:  2009-10

4.  Accuracy of Prehospital Triage in Selecting Severely Injured Trauma Patients.

Authors:  Frank J Voskens; Eveline A J van Rein; Rogier van der Sluijs; Roderick M Houwert; Robert Anton Lichtveld; Egbert J Verleisdonk; Michiel Segers; Ger van Olden; Marcel Dijkgraaf; Luke P H Leenen; Mark van Heijl
Journal:  JAMA Surg       Date:  2018-04-01       Impact factor: 14.766

5.  Accuracy of prehospital triage protocols in selecting severely injured patients: A systematic review.

Authors:  Eveline A J van Rein; R Marijn Houwert; Amy C Gunning; Rob A Lichtveld; Luke P H Leenen; Mark van Heijl
Journal:  J Trauma Acute Care Surg       Date:  2017-08       Impact factor: 3.313

6.  A prospective comparison of paramedic judgment and the trauma triage rule in the prehospital setting.

Authors:  G R Fries; G McCalla; M A Levitt; R Cordova
Journal:  Ann Emerg Med       Date:  1994-11       Impact factor: 5.721

7.  Refining the trauma triage algorithm at an Australian major trauma centre: derivation and internal validation of a triage risk score.

Authors:  M M Dinh; K J Bein; M Oliver; A-S Veillard; R Ivers
Journal:  Eur J Trauma Emerg Surg       Date:  2013-07-31       Impact factor: 3.693

8.  A revision of the Trauma Score.

Authors:  H R Champion; W J Sacco; W S Copes; D S Gann; T A Gennarelli; M E Flanagan
Journal:  J Trauma       Date:  1989-05

9.  Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines.

Authors:  Andrea Marshall; Douglas G Altman; Roger L Holder; Patrick Royston
Journal:  BMC Med Res Methodol       Date:  2009-07-28       Impact factor: 4.615

10.  Effect of an organizational change in a prehospital trauma care protocol and trauma transport directive in a large urban city: a before and after study.

Authors:  Rebecka Rubenson Wahlin; Sari Ponzer; Markus B Skrifvars; Hans Morten Lossius; Maaret Castrén
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2016-03-09       Impact factor: 2.953

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  11 in total

1.  External validation of a prediction model for pain and functional outcome after elective lumbar spinal fusion.

Authors:  Ayesha Quddusi; Hubert A J Eversdijk; Anita M Klukowska; Marlies P de Wispelaere; Julius M Kernbach; Marc L Schröder; Victor E Staartjes
Journal:  Eur Spine J       Date:  2019-10-22       Impact factor: 3.134

Review 2.  Individual risk factors predictive of major trauma in pre-hospital injured older patients: a systematic review.

Authors:  Abdullah Pandor; Gordon Fuller; Munira Essat; Lisa Sabir; Chris Holt; Helen Buckley Woods; Hridesh Chatha
Journal:  Br Paramed J       Date:  2022-03-01

3.  The predictive value of serum lactate to forecast injury severity in trauma-patients increases taking age into account.

Authors:  Paul Hagebusch; Philipp Faul; Christian Ruckes; Philipp Störmann; Ingo Marzi; Reinhard Hoffmann; Uwe Schweigkofler; Yves Gramlich
Journal:  Eur J Trauma Emerg Surg       Date:  2022-07-19       Impact factor: 2.374

4.  Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

Authors:  Riku Klén; Disha Purohit; Ricardo Gómez-Huelgas; José Manuel Casas-Rojo; Juan Miguel Antón-Santos; Jesús Millán Núñez-Cortés; Carlos Lumbreras; José Manuel Ramos-Rincón; Noelia García Barrio; Miguel Pedrera-Jiménez; Antonio Lalueza Blanco; María Dolores Martin-Escalante; Francisco Rivas-Ruiz; Maria Ángeles Onieva-García; Pablo Young; Juan Ignacio Ramirez; Estela Edith Titto Omonte; Rosmery Gross Artega; Magdy Teresa Canales Beltrán; Pascual Ruben Valdez; Florencia Pugliese; Rosa Castagna; Ivan A Huespe; Bruno Boietti; Javier A Pollan; Nico Funke; Benjamin Leiding; David Gómez-Varela
Journal:  Elife       Date:  2022-05-17       Impact factor: 8.713

5.  Trauma-team-activation in Germany: how do emergency service professionals use the activation due to trauma mechanism? Results from a nationwide survey.

Authors:  Paul Hagebusch; Philipp Faul; Frank Naujoks; Alexander Klug; Reinhard Hoffmann; Uwe Schweigkofler
Journal:  Eur J Trauma Emerg Surg       Date:  2020-06-24       Impact factor: 3.693

6.  Development and validation of a novel prediction model to identify patients in need of specialized trauma care during field triage: design and rationale of the GOAT study.

Authors:  Rogier van der Sluijs; Thomas P A Debray; Martijn Poeze; Loek P H Leenen; Mark van Heijl
Journal:  Diagn Progn Res       Date:  2019-06-20

7.  An economic evaluation of triage tools for patients with suspected severe injuries in England.

Authors:  Daniel Pollard; Gordon Fuller; Steve Goodacre; Eveline A J van Rein; Job F Waalwijk; Mark van Heijl
Journal:  BMC Emerg Med       Date:  2022-01-11

8.  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.

Authors:  Jamie Miles; Richard Jacques; Janette Turner; Suzanne Mason
Journal:  Diagn Progn Res       Date:  2021-11-08

9.  Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury.

Authors:  Jean-Denis Moyer; Patrick Lee; Charles Bernard; Lois Henry; Elodie Lang; Fabrice Cook; Fanny Planquart; Mathieu Boutonnet; Anatole Harrois; Tobias Gauss
Journal:  World J Emerg Surg       Date:  2022-08-03       Impact factor: 8.165

10.  Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.

Authors:  Jamie Miles; Janette Turner; Richard Jacques; Julia Williams; Suzanne Mason
Journal:  Diagn Progn Res       Date:  2020-10-02
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