The counterintuitive effect of multiple injuries in severity scoring: a simple variable improves the predictive ability of NISS

How health service delivery guides the allocation of major trauma patients in the intensive care units of the inclusive (hub and spoke) trauma system of the Emilia Romagna Region (Italy). A cross-sectional study

OBJECTIVE

To evaluate cross-sectional patient distribution and standardised 30-day mortality in the intensive care units (ICU) of an inclusive hub and spoke trauma system.

SETTING

ICUs of the Integrated System for Trauma Patient Care (SIAT) of Emilia-Romagna, an Italian region with a population of approximately 4.5 million.

PARTICIPANTS

5300 patients with an Injury Severity Score (ISS) >15 were admitted to the regional ICUs and recorded in the Regional Severe Trauma Registry between 2007 and 2012. Patients were classified by the Abbreviated Injury Score as follows: (1) traumatic brain injury (2) multiple injuriesand (3) extracranial lesions. The SIATs were divided into those with at least one neurosurgical level II trauma centre (TC) and those with a neurosurgical unit in the level I TC only.

RESULTS

A higher proportion of patients (out of all SIAT patients) were admitted to the level I TC at the head of the SIAT with no additional neurosurgical facilities (1083/1472, 73.6%) compared with the level I TCs heading SIATs with neurosurgical level II TCs (1905/3815; 49.9%). A similar percentage of patients were admitted to level I TCs (1905/3815; 49.9%) and neurosurgical level II TCs (1702/3815, 44.6%) in the SIATs with neurosurgical level II TCs. Observed versus expected mortality (OE) was not statistically different among the three types of centre with a neurosurgical unit; however, the best mean OE values were observed in the level I TC in the SIAT with no neurosurgical unit.

CONCLUSION

The Hub and Spoke concept was fully applied in the SIAT in which neurosurgical facilities were available in the level I TC only. The performance of this system suggests that competition among level I and level II TCs in the same Trauma System reduces performance in both. The density of neurosurgical centres must be considered by public health system governors before implementing trauma systems.

Combining the new injury severity score with an anatomical polytrauma injury variable predicts mortality better than the new injury severity score and the injury severity score: a retrospective cohort study

Background

Anatomy-based injury severity scores are commonly used with physiological scores for reporting severity of injury in a standardized manner. However, there is lack of consensus on choice of scoring system, with the commonly used injury severity score (ISS) performing poorly for certain sub-groups, eg head-injured patients. We hypothesized that adding a dichotomous variable for polytrauma (yes/no for Abbreviated Injury Scale (AIS) scores of 3 or more in at least two body regions) to the New Injury Severity Score (NISS) would improve the prediction of in-hospital mortality in injured patients, including head-injured patients—a subgroup that has a disproportionately high mortality. Our secondary hypothesis was that the ISS over-estimates the risk of death in polytrauma patients, while the NISS under-estimates it.

Methods

Univariate and multivariable analysis was performed on retrospective cohort data of blunt injured patients aged 18 and over with an ISS over 9 from the Singapore National Trauma Registry from 2011–2013. Model diagnostics were tested using discrimination (c-statistic) and calibration (Hosmer-Lemeshow goodness-of-fit statistic). All models included age, gender, and comorbidities.

Results

Our results showed that the polytrauma and NISS model outperformed the other models (polytrauma and ISS, NISS alone or ISS alone) in predicting 30-day and in-hospital mortality. The NISS underestimated the risk of death for patients with polytrauma, while the ISS overestimated the risk of death for these patients.

When used together with the NISS and polytrauma, categorical variables for deranged physiology (systolic blood pressure of 90 mmHg or less, GCS of 8 or less) outperformed the traditional ‘ISS and RTS (Revised Trauma Score)’ model, with a c-statistic of greater than 0.90. This could be useful in cases when the RTS cannot be scored due to missing respiratory rate.

Discussion

The NISS and polytrauma model is superior to current scores for prediction of 30-day and in-hospital mortality. We propose that this score replace the ISS or NISS in institutions using AIS-based scores.

Conclusions

Adding polytrauma to the NISS or ISS improves prediction of 30-day mortality. The superiority of the NISS or ISS depends on the proportion of polytrauma and head-injured patients in the study population.

Risk prediction score for death of traumatised and injured children

BACKGROUND

Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children.

METHODS

Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logistic regression analysis that included 15 predictors. Model performance was assessed using the concordance statistic (C-statistic) and the observed per expected (O/E) ratio. Internal validation of the model was performed using a 200-repetition bootstrap analysis.

RESULTS

Death occurred in 1.7% of the injured children (95% confidence interval [95% CI]: 1.57-1.82). Ten predictors (i.e., age, airway intervention, physical injury mechanism, three injured body regions, the Glasgow Coma Scale, and three vital signs) were significantly associated with death. The C-statistic and the O/E ratio were 0.938 (95% CI: 0.929-0.947) and 0.86 (95% CI: 0.70-1.02), respectively. The scoring scheme classified three risk stratifications with respective likelihood ratios of 1.26 (95% CI: 1.25-1.27), 2.45 (95% CI: 2.42-2.52), and 4.72 (95% CI: 4.57-4.88) for low, intermediate, and high risks of death. Internal validation showed good model performance (C-statistic = 0.938, 95% CI: 0.926-0.952) and a small calibration bias of 0.002 (95% CI: 0.0005-0.003).

CONCLUSIONS

We developed a simplified Thai pediatric injury death prediction score with satisfactory calibrated and discriminative performance in emergency room settings.