Performance of the modified TRISS for evaluating trauma care in subpopulations: A cohort study
Introduction
Prediction models that adequately predict survival are required to determine the quality of care in trauma patients. Trauma is a major cause of mortality in young adults worldwide [1]. In 2014, almost 84 000 patients were admitted due to injuries in the Netherlands and the 30-day mortality rate was 2.1% [2]. Scoring systems and prediction models are important tools to quantify the probability of survival and to evaluate and improve the quality of care for the large number of injured patients [3].
The Trauma and Injury Severity Score (TRISS) was developed from the Major Trauma Outcome Study (MTOS) in 1987 to evaluate the quality of trauma care by comparing outcomes with a norm score [4]. The TRISS is a weighted score based on the Injury Severity Score (ISS), age, and the coded Revised Trauma score (RTS). The RTS combines the Glasgow Coma Score (GCS), Systolic Blood Pressure (SBP) and Respiratory Rate (RR). The MTOS was a retrospective study conducted in North America from 1982 through 1987 and was of great value for the development of TRISS. It has been shown previously that the TRISS has several limitations [[5], [6]]. The use of TRISS in an external population raises concerns, because differences between cohorts are distinct [[7], [8]].
Previous research in the Dutch population demonstrated an adequate performance of the TRISS with coefficients from the MTOS or from the National Trauma Data Bank (NTDB) in the total population, but demonstrated a poor reflection of the mortality risk of elderly patients (with hip fractures) [[9], [10], [11], [12]]. Furthermore, Frankema et al. [13] suggested developing and using a more accurate model for the evaluation of trauma care in the Dutch trauma population. A recent review showed that there is no agreement on a better and practically applicable model to use in the evaluation of trauma care [14].
In 2015 the Dutch Trauma Registry developed a new model based solely on the variables in the TRISS model according to their trauma population, including the elderly patients with hip fracture [15]. This model is used to compare quality of care between Dutch hospitals, but has never been validated in subsets. The aim of this study was to determine the performance of the modified TRISS in subpopulations and to determine where this model needs improvement for better survival predictions in the Dutch trauma population.
Section snippets
Study population and data collection
At present, the Netherlands consists of eleven trauma regions, all including a coordinating trauma level I center. The region Noord-Brabant is representative of the total Dutch trauma population. It covers 16% of all admitted trauma patients in the Netherlands and includes urban as well as rural populations [2]. Eleven hospitals in the region Noord-Brabant contributed to the Brabant Trauma Registry (BTR), including one level I hospital and ten level II or III hospitals. The registry database
Baseline characteristics
A total of 72411 patients were included in the BTR. Patients with penetrating injury (N = 2539) were excluded because the number of non-survivors (N = 32) was too low to interpret the results of discrimination and calibration. Patients with unknown survival outcome (N = 125) were excluded from analysis. The excluded patients did not differ in age, RTS, or ISS from the total cohort (data not shown).
There were 69747 patients used for further analysis, including 11 514 elderly patients with hip
Discussion
Prediction models need to be reliable if used in evaluating the quality of trauma care. Although discrimination of the modified TRISS in the total trauma cohort was adequate, the model performed much better when excluding elderly, with or without hip fractures. Overall, calibration of the modified TRISS was adequate for the total cohort. However, the model overestimates the survival for the elderly and underestimates survival for patients without TBI.
Discrimination of a model is dependent on
Conflicts of interest
The authors declare no conflict of interest.
Acknowledgement
This work was supported by a grant of the Dutch organization for health research and care innovation (ZonMW) section TopCare projects (grantnumber: 80-84200-98-14226). ZonMw had no role in the design, analyses or interpretation of this study.
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