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

Rogier van der Sluijs; Thomas P A Debray; Martijn Poeze; Loek P H Leenen; Mark van Heijl

doi:10.1186/s41512-019-0058-5

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

Diagn Progn Res. 2019 Jun 20:3:12. doi: 10.1186/s41512-019-0058-5. eCollection 2019.

Authors

Rogier van der Sluijs^{1

2

3}, Thomas P A Debray^{4

5}, Martijn Poeze¹, Loek P H Leenen², Mark van Heijl^{2

3}

Affiliations

¹ 1Department of Traumatology, Maastricht University Medical Center, Maastricht, The Netherlands.
² 2Department of Traumatology, University Medical Center Utrecht, Utrecht, The Netherlands.
³ Department of Surgery, Diakonessenhuis Utrecht/Zeist/Doorn, Utrecht, The Netherlands.
⁴ 4Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
⁵ 5Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Abstract

Background: Adequate field triage of trauma patients is crucial to transport patients to the right hospital. Mistriage and subsequent interhospital transfers should be minimized to reduce avoidable mortality, life-long disabilities, and costs. Availability of a prehospital triage tool may help to identify patients in need of specialized trauma care and to determine the optimal transportation destination.

Methods: The GOAT (Gradient Boosted Trauma Triage) study is a prospective, multi-site, cross-sectional diagnostic study. Patients transported by at least five ground Emergency Medical Services to any receiving hospital within the Netherlands are eligible for inclusion. The reference standards for the need of specialized trauma care are an Injury Severity Score ≥ 16 and early critical resource use, which will both be assessed by trauma registrars after the final diagnosis is made. Variable selection will be based on ease of use in practice and clinical expertise. A gradient boosting decision tree algorithm will be used to develop the prediction model. Model accuracy will be assessed in terms of discrimination (c-statistic) and calibration (intercept, slope, and plot) on individual participant's data from each participating cluster (i.e., Emergency Medical Service) through internal-external cross-validation. A reference model will be externally validated on each cluster as well. The resulting model statistics will be investigated, compared, and summarized through an individual participant's data meta-analysis.

Discussion: The GOAT study protocol describes the development of a new prediction model for identifying patients in need of specialized trauma care. The aim is to attain acceptable undertriage rates and to minimize mortality rates and life-long disabilities.

Keywords: Diagnosis; Emergency Medical Services; External validation; Gradient boosting; Machine learning; Meta-analysis; Prediction model; Study protocol; Trauma Triage App; Triage.