The off-hour effect on mortality in traumatic brain injury according to age group

A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients

Background

Accurately predicting the short-term in-hospital mortality risk for patients with stroke and TBI (Traumatic Brain Injury) is crucial for improving the quality of emergency medical care.

Method

This study analyzed data from 2,125 emergency admission patients with stroke and traumatic brain injury at two Grade a hospitals in China from January 2021 to March 2024. LASSO regression was used for feature selection, and the predictive performance of logistic regression was compared with six machine learning algorithms. A 70:30 ratio was applied for cross-validation, and confidence intervals were calculated using the bootstrap method. Temporal validation was performed on the best-performing model. SHAP values were employed to assess variable importance.

Results

The random forest algorithm excelled in predicting in-hospital 3-day mortality, achieving an AUC of 0.978 (95% CI: 0.966-0.986). Time series validation demonstrated the model's strong generalization capability, with an AUC of 0.975 (95% CI: 0.963-0.986). Key predictive factors in the final model included metabolic syndrome, NEWS2 score, Glasgow Coma Scale (GCS), whether surgery was performed, bowel movement status, potassium level (K), aspartate transaminase (AST) level, and temporal factors. SHAP value analysis further confirmed the significant contributions of these variables to the predictive outcomes. The random forest model developed in this study demonstrates good accuracy in predicting short-term in-hospital mortality rates for stroke and traumatic brain injury patients. The model integrates emergency scores, clinical signs, and key biochemical indicators, providing a comprehensive perspective for risk assessment. This approach, which incorporates emergency data, holds promise for assisting decision-making in clinical practice, thereby improving patient outcomes.

The determinants for death in hospital following moderate to severe traumatic brain injury in Australia

OBJECTIVES

To establish the determinants of death in hospital for patients with moderate to severe traumatic brain injury (TBI) in Australia.

DESIGN, SETTING, PARTICIPANTS

Retrospective analysis of Australia New Zealand Trauma Registry (ANZTR) data. Cases were included if they presented to a participating hospital between 1 July 2015 and 30 June 2020 and had an Abbreviated Injury Severity (AIS) score - head greater than 2.

MAIN OUTCOME MEASURES

Death in hospital.

RESULTS

There were 16 350 patients. Their mean age was 51 years and 71% were male. After adjusting for measures of injury severity, there was an increased odds of in-hospital death for patients whose injury occurred outside daylight hours or first mode of transport was road ambulance, who were not transferred from another hospital, had an endotracheal tube placed prior to definitive hospital arrival or received their definitive hospital care outside Victoria.

CONCLUSION

Among people presenting to a major trauma hospital in Australia following moderate to severe TBI, there were multiple factors independently associated with death in hospital. The potentially modifiable determinants of in-hospital death included out-of-hours access to emergency care, mode of transfer from the scene of the injury, prior facility care and pre-definitive hospital endotracheal intubation.

Effect of trauma quality improvement initiatives on outcomes and costs at community hospitals: A scoping review

BACKGROUND

Due to complex geography and resource constraints, trauma patients are often initially transported to community or rural facilities rather than a larger Level I or II trauma center. The objective of this scoping review was to synthesize evidence on interventions that improved the quality of trauma care and/or reduced healthcare costs at non-Level I or II facilities.

METHODS

A scoping review was performed to identify studies implementing a Quality Improvement (QI) initiative at a non-major trauma center (i.e., non-Level I or II trauma center [or equivalent]). We searched 3 electronic databases (MEDLINE, Embase, CINAHL) and the grey literature (relevant networks, organizations/associations). Methodological quality was evaluated using NIH and JBI study quality assessment tools. Studies were included if they evaluated the effect of implementing a trauma care QI initiative on one or more of the following: 1) trauma outcomes (mortality, morbidity); 2) system outcomes (e.g., length of stay [LOS], transfer times, provider factors); 3) provider knowledge or perception; or 4) healthcare costs. Pediatric trauma, pre-hospital and tele-trauma specific studies were excluded.

RESULTS

Of 1046 data sources screened, 36 were included for full review (29 journal articles, 7 abstracts/posters without full text). Educational initiatives including the Rural Trauma Team Development Course and the Advanced Trauma Life Support course were the most common QI interventions investigated. Study outcomes included process metrics such as transfer time to tertiary care and hospital LOS, along with measures of provider perception and knowledge. Improvement in mortality was reported in a single study evaluating the impact of establishing a dedicated trauma service at a community hospital.

CONCLUSIONS

Our review captured a broad spectrum of trauma QI projects implemented at non-major trauma centers. Educational interventions did result in process outcome improvements and high rates of self-reported improvements in trauma care. Given the heterogeneous capabilities of community and rural hospitals, there is no panacea for trauma QI at these facilities. Future research should focus on patient outcomes like mortality and morbidity, and locally relevant initiatives.

Effect of Daytime versus Nighttime on Prehospital Care and Outcomes after Severe Traumatic Brain Injury

Background/Objectives: Severe traumatic brain injury (TBI) is a frequent cause of morbidity and mortality worldwide. In the Netherlands, suspected TBI is a criterion for the dispatch of the physician-staffed helicopter emergency medical services (HEMS) which are operational 24 h per day. It is unknown if patient outcome is influenced by the time of day during which the incident occurs. Therefore, we investigated the association between the time of day of the prehospital treatment of severe TBI and 30-day mortality. Methods: A retrospective analysis of prospectively collected data from the BRAIN-PROTECT study was performed. Patients with severe TBI treated by one of the four Dutch helicopter emergency medical services were included and followed up to one year. The association between prehospital treatment during day- versus nighttime, according to the universal daylight period, and 30-day mortality was analyzed with multivariable logistic regression. A planned subgroup analysis was performed in patients with TBI with or without any other injury. Results: A total of 1794 patients were included in the analysis, of which 1142 (63.7%) were categorized as daytime and 652 (36.3%) as nighttime. Univariable analysis showed a lower 30-day mortality in patients with severe TBI treated during nighttime (OR 0.74, 95% CI 0.60-0.91, p = 0.004); this association was no longer present in the multivariable model (OR 0.82, 95% CI 0.59-1.16, p = 0.262). In a subgroup analysis, no association was found between mortality rates and the time of prehospital treatment in patients with combined injuries (TBI and any other injury). Patients with isolated TBI had a lower mortality rate when treated during nighttime than when treated during daytime (OR 0.51, 95% CI 0.34-0.76, p = 0.001). Within the whole cohort, daytime versus nighttime treatments were not associated with differences in functional outcome defined by the Glasgow Outcome Scale. Conclusions: In the overall study population, no difference was found in 30-day mortality between patients with severe TBI treated during day or night in the multivariable model. Patients with isolated severe TBI had lower mortality rates at 30 days when treated at nighttime.

Weekend Admission and Mortality in Patients With Traumatic Brain Injury: A Meta-analysis

Objective

Previous studies have reported the presence of a "weekend effect" with respect to mortality in serious emergency admissions, including cases of traumatic brain injury (TBI). However, the relationship between weekend hospitalization and TBI mortality has not been fully established. This study aimed to conduct a systematic review of available evidence and investigate differences in mortality among TBI patients between weekday and weekend admissions.

Methods

Electronic databases including PubMed, Cochrane Library, and Embase were used to obtain relevant articles. Mortality, as the primary outcome of interest, encompassed in-hospital or 30-day mortality. Mortality rates were compared between the 2 groups, weekend and weekday admissions. Additionally, meta-regression analysis was performed on potential confounders to verify and provide comparative results.

Results

A total of 7 studies involving 522,942 TBI patients were eligible for inclusion in the synthesis of the systematic review. Of these patients, 71.6% were admitted during weekdays, whereas 28.4% were hospitalized on weekends. The overall integrated mortality was 11.0% (57,286/522,942), with a mortality rate of 10.8% in the weekday group and 11.3% in the weekend group. Pooled analysis revealed no significant difference in mortality between the weekday and weekend groups (risk ratio, 0.99; 95% confidence interval, 0.90-1.09; p=0.78). Furthermore, the meta-regression analysis for sensitivity assessment showed no modifying effect on mortality (p=0.79).

Conclusion

This study found no difference in mortality rates between weekday and weekend admissions among TBI patients. Additional sensitivity analyses also demonstrated no significant increase in the risk of mortality in the weekend group.