To evaluate the validity of the model, we studied discrimination and calibration in the whole sample and distributed according to blunt or penetrating mechanisms of injury. Discrimination refers to the ability to distinguish between patients who die and those that survive. The greater the area, the better the discrimination. The calibration of a prognostic model evaluates the concordance between the probability observed in the sample and the probability predicted by the model, describing how the prognostic scale works over wide ranges of predicted mortality.
A probability of close to 1 represents better adjustment. Patient dropout from the sample was due to different reasons, the most important being inability to determine hospital outcome in In most cases, transfer was done to another country, making follow-up impossible. The flowchart in Fig. The mean patient age was The mean ISS score was A total of patients suffered blunt trauma Table 1 shows the data referred to patient epidemiology, acute management, resource utilization and main outcome.. Epidemiological and clinical data of the patients included in the study..
In patients with blunt trauma, the observed and predicted mortality rates were The global sample of patients presented an area under the ROC curve of 0. Patients with blunt trauma Fig. Discrimination based on the area under the receiver operating characteristic ROC curve for patients with blunt A and penetrating trauma B..
The results of the Hosmer—Lemeshow HL goodness-of-fit test, in both in the total cohorts of patients of patients and distributed according to the mechanism of trauma blunt or penetrating are shown in Table 3. The correlation between predicted and observed mortality is shown in Fig.
Hosmer—Lemeshow HL goodness-of-fit test.. Calibration curve comparing predicted and observed mortality. Penetrating trauma showed better discrimination and good calibration. Altogether, these results suggest that newly calibrated b coefficient scales are necessary in our setting.. Our sample of patients offers an initial picture of patients with severe trauma admitted to the ICUs of our setting, taking into account the severity of injury, the care provided, length of stay and mortality. These Units represent level I and II centers. Such patients usually present high ISS values, important resource utilization, and high mortality.
It was first developed in the s through several logistic regression models 5,9 with different b coefficients considering blunt or penetrating injuries. Several updates have been made since then.. When applied to our patients, TRISS showed good discrimination with inadequate calibration — a fact that limits the use of this prognostic model.
This observation is consistent with other studies, and in general, with other prognostic scores in the ICU setting, where the main shortcoming corresponds to inadequate calibration despite good discrimination. This fact is consistent with previous studies differentiating between blunt and penetrating trauma. In the latter type of trauma, discrimination and calibration is better, perhaps due to lesser improvement in their specific care. Poor calibration and discrimination does not necessarily refer to the quality of the care provided but rather to incorrect application of the model to a population with specific characteristics.
Mortality prediction according to TRISS has therefore been questioned 14,15 : its clinical application has shown opposite results, 16—18 especially when used in non-MTOS patients. However, although they have slightly improved the predictive ability of TRISS methodology, the latter remains the most widely used tool in clinical practice.
Our study has a number of limitations — some attributable to the TRISS model itself, and other specific of our sample.
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The most relevant are a the limited number of patients for this kind of analysis despite the multicenter nature of the study. This was due to the large number of patients from different countries that are admitted to our ICUs and are subsequently transferred to their reference hospitals at home, thereby making follow-up impossible.. All authors listed in the study declare that they have no conflicts of interest.. ISSN: Previous article Next article.
Trauma History Questionnaire (THQ) - PTSD: National Center for PTSD
Issue 7. Pages October More article options. Download PDF. Llompart-Pou b ,. Corresponding author. This item has received. Article information. Show more Show less. Table 1. Table 2. Design A prospective, multicenter registry evaluation was carried out. Patients Individuals with traumatic disease and available data admitted to the participating ICUs. Main variables of interest Predicted and observed mortality. Results A total of patients were analyzed. TRISS methodology underestimated mortality in patients with low predicted mortality and overestimated mortality in patients with high predicted mortality.
Conclusions TRISS methodology in the evaluation of severe trauma in Spanish ICUs showed good discrimination, with inadequate calibration — particularly in blunt trauma. Resultados Analizamos 1. Palabras clave:. Introduction Trauma registries constitute an useful tool for monitoring trauma patient care, since they accurately reflect management and care in different settings. Figure 1. Epidemiological and clinical data of the patients included in the study. Discrimination based on the area under the receiver operating characteristic ROC curve for patients with blunt A and penetrating trauma B.
Figure 2. Table 3. Hosmer—Lemeshow HL goodness-of-fit test. Figure 3. Moore, D. Injury, 39 , pp. Med Intensiva, 37 , pp. Lefering, S. Eur J Trauma Emerg Surg, 38 , pp. Ballesteros Sanz, L. Cordero Lorenzana, F. Med Intensiva, 39 , pp.
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Llompart-Pou, F. Fase piloto. Lemeshow, D. Hosmer Jr.. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol, , pp. Champion, W.
Sacco, T. World J Surg, 7 , pp. Nassar Jr. Mocelin, A. Nunes, F. Giannini, L. Brauer, F. Andrade, et al. Caution when using prognostic models: a prospective comparison of 3 recent prognostic models. J Crit Care, 27 , pp. Rogers, T. Osler, M. Krasne, A. Rogers, E. Bradburn, J. Lee, et al. J Trauma Acute Care Surg, 73 , pp. Brockamp, M.
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