Arnaud Follin1, Sébastien Jacqmin2, Vibol Chhor3, Florence Bellenfant4, Ségolène Robin5, Alain Guinvarc'h6, Frank Thomas7, Thomas Loeb8, Jean Mantz9, Romain Pirracchio10. 1. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: arnaud.follin@gmail.com. 2. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: jacqmin_s@yahoo.fr. 3. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: chhorv@gmail.com. 4. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: florence.bellenfant@aphp.fr. 5. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: segolene.robin@hotmail.fr. 6. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: alain.guinvarch2015@gmail.com. 7. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: fthomas@live.fr. 8. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France; SAMU 92, Hôpital Raymond Poincare, Université de Versailles St Quentin, Garches, France. Electronic address: tloeb@invivo.edu. 9. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France. Electronic address: jean.mantz@aphp.fr. 10. Service d'Anesthésie-Réanimation, Hôpital Européen Georges Pompidou, Université Paris 5 Descartes, Sorbonne Paris Cite, Paris, France; Department of Anesthesia and Perioperative Care, San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, USA; Département de Biostatistique et Informatique Médicale, INSERM U1153, équipe ECSTRA, Hôpital Saint Louis, Université Paris Diderot, Sorbonne Paris Cite, Paris, France; Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, USA. Electronic address: romainpirracchio@yahoo.fr.
Abstract
BACKGROUND: There is a need for better allocation of medical resources in polytrauma, by optimizing both the over and undertriage rates. The goal of this study is to provide a new working definition for polytrauma based on the prediction of the need for specialized trauma care. METHODS: This is a prospective, observational study, performed in a specialized trauma center in Paris. All consecutive patients admitted for a trauma at a major trauma center in Paris were included in the study. The primary outcome was the need for specialized trauma care as defined by the North American consensus. The explanatory variables included basic variables collected on scene. The modeling approach relied on recursive partitioning based decision trees. Its prediction performance was evaluated both internally and externally on a validation cohort, and compared to the MGAP (Mechanism, Glasgow coma scale, Age and Arterial pressure) score. MEASUREMENTS AND MAIN RESULTS: 1160 patients were included in the analysis over a 3-year period (2012-2014), out of which 41% needed specialized trauma care as defined by the recent US guidelines. The decision tree outperformed the MGAP and reached an area under the receiver operating characteristic curve of 0.82 [0.79-0.84]. This optimal decision rule was associated with a sensitivity of 0.94 [0.92-0.96], a specificity of 0.48 [0.44-0.52]. A conservative decision rule (refer to a trauma center all patient with a predicted probability ≥0.34) would result in an undertriage rate of 5.7% and an overtriage of 52.3% (respectively 7% and 64% in the validation cohort). CONCLUSIONS: Our tree-based decision algorithm is a user-friendly and reliable alternative to the preexisting scores, which offers good performance to predict the need for specialized trauma care.
BACKGROUND: There is a need for better allocation of medical resources in polytrauma, by optimizing both the over and undertriage rates. The goal of this study is to provide a new working definition for polytrauma based on the prediction of the need for specialized trauma care. METHODS: This is a prospective, observational study, performed in a specialized trauma center in Paris. All consecutive patients admitted for a trauma at a major trauma center in Paris were included in the study. The primary outcome was the need for specialized trauma care as defined by the North American consensus. The explanatory variables included basic variables collected on scene. The modeling approach relied on recursive partitioning based decision trees. Its prediction performance was evaluated both internally and externally on a validation cohort, and compared to the MGAP (Mechanism, Glasgow coma scale, Age and Arterial pressure) score. MEASUREMENTS AND MAIN RESULTS: 1160 patients were included in the analysis over a 3-year period (2012-2014), out of which 41% needed specialized trauma care as defined by the recent US guidelines. The decision tree outperformed the MGAP and reached an area under the receiver operating characteristic curve of 0.82 [0.79-0.84]. This optimal decision rule was associated with a sensitivity of 0.94 [0.92-0.96], a specificity of 0.48 [0.44-0.52]. A conservative decision rule (refer to a trauma center all patient with a predicted probability ≥0.34) would result in an undertriage rate of 5.7% and an overtriage of 52.3% (respectively 7% and 64% in the validation cohort). CONCLUSIONS: Our tree-based decision algorithm is a user-friendly and reliable alternative to the preexisting scores, which offers good performance to predict the need for specialized trauma care.
Authors: Christian Waydhas; Markus Baake; Lars Becker; Boris Buck; Helena Düsing; Björn Heindl; Kai Oliver Jensen; Rolf Lefering; Carsten Mand; T Paffrath; Uwe Schweigkofler; Kai Sprengel; Heiko Trentzsch; Bernd Wohlrath; Dan Bieler Journal: World J Surg Date: 2018-09 Impact factor: 3.352
Authors: D Bieler; H Trentzsch; M Baacke; L Becker; H Düsing; B Heindl; K O Jensen; R Lefering; C Mand; O Özkurtul; T Paffrath; U Schweigkofler; K Sprengel; B Wohlrath; C Waydhas Journal: Unfallchirurg Date: 2018-10 Impact factor: 1.000
Authors: Maximilian Kippnich; Maximilian Duempert; Nora Schorscher; Martin C Jordan; Andreas S Kunz; Patrick Meybohm; Thomas Wurmb Journal: Sci Rep Date: 2022-09-27 Impact factor: 4.996