L Serviá1, M Badia1, N Montserrat1, J Trujillano2. 1. Servicio de Medicina Intensiva, Hospital Universitario Arnau de Vilanova, Lleida, España. 2. Universidad de Lleida, IRBLLEIDA, Lleida, España. Electronic address: jtruji@cmb.udl.es.
Abstract
INTRODUCTION: The goals of this project were to compare both the anatomic and physiologic severity scores in trauma patients admitted to intensive care unit (ICU), and to elaborate mixed statistical models to improve the precision of the scores. METHODS: A prospective study of cohorts. The combined medical/surgical ICU in a secondary university hospital. Seven hundred and eighty trauma patients admitted to ICU older than 16 years of age. Anatomic models (ISS and NISS) were compared and combined with physiological models (T-RTS, APACHE II [APII], and MPM II). The probability of death was calculated following the TRISS method. The discrimination was assessed using ROC curves (ABC [CI 95%]), and the calibration using the Hosmer-Lemeshoẃs H test. The mixed models were elaborated with the tree classification method type Chi Square Automatic Interaction Detection. RESULTS: A 14% global mortality was recorded. The physiological models presented the best discrimination values (APII of 0.87 [0.84-0.90]). All models were affected by bad calibration (P<.01). The best mixed model resulted from the combination of APII and ISS (0.88 [0.83-0.90]). This model was able to differentiate between a 7.5% mortality for elderly patients with pathological antecedents and a 25% mortality in patients presenting traumatic brain injury, from a pool of patients with APII values ranging from 10 to 17 and an ISS threshold of 22. CONCLUSIONS: The physiological models perform better than the anatomical models in traumatic patients admitted to the ICU. Patients with low scores in the physiological models require an anatomic analysis of the injuries to determine their severity.
INTRODUCTION: The goals of this project were to compare both the anatomic and physiologic severity scores in traumapatients admitted to intensive care unit (ICU), and to elaborate mixed statistical models to improve the precision of the scores. METHODS: A prospective study of cohorts. The combined medical/surgical ICU in a secondary university hospital. Seven hundred and eighty traumapatients admitted to ICU older than 16 years of age. Anatomic models (ISS and NISS) were compared and combined with physiological models (T-RTS, APACHE II [APII], and MPM II). The probability of death was calculated following the TRISS method. The discrimination was assessed using ROC curves (ABC [CI 95%]), and the calibration using the Hosmer-Lemeshoẃs H test. The mixed models were elaborated with the tree classification method type Chi Square Automatic Interaction Detection. RESULTS: A 14% global mortality was recorded. The physiological models presented the best discrimination values (APII of 0.87 [0.84-0.90]). All models were affected by bad calibration (P<.01). The best mixed model resulted from the combination of APII and ISS (0.88 [0.83-0.90]). This model was able to differentiate between a 7.5% mortality for elderly patients with pathological antecedents and a 25% mortality in patients presenting traumatic brain injury, from a pool of patients with APII values ranging from 10 to 17 and an ISS threshold of 22. CONCLUSIONS: The physiological models perform better than the anatomical models in traumaticpatients admitted to the ICU. Patients with low scores in the physiological models require an anatomic analysis of the injuries to determine their severity.
Authors: Luis Serviá; Neus Montserrat; Mariona Badia; Juan Antonio Llompart-Pou; Jesús Abelardo Barea-Mendoza; Mario Chico-Fernández; Marcelino Sánchez-Casado; José Manuel Jiménez; Dolores María Mayor; Javier Trujillano Journal: BMC Med Res Methodol Date: 2020-10-20 Impact factor: 4.615
Authors: Luis Serviá; Juan Antonio Llompart-Pou; Mario Chico-Fernández; Neus Montserrat; Mariona Badia; Jesús Abelardo Barea-Mendoza; María Ángeles Ballesteros-Sanz; Javier Trujillano Journal: Crit Care Date: 2021-12-07 Impact factor: 9.097