Neurological outcomes of neurosurgical operations for multiple trauma elderly patients in Hong Kong

Wong, George Kwok Chu; Graham, Colin A.; Ng, Elsie; Yeung, John H.K.; Rainer, Timothy; Poon, Wai-sang

doi:10.4103/0974-2700.83861

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…In addition, it should be noted that in this study most of the people affected by accidents had recovered and only a small number of accident victims had died, which is consistent with the final accident outcomes as determined in previous studies conducted on the age group of individuals 60 years old and older (37,38). Although most injuries end in recovery, each of these injuries can cause significant mental, physical, and economic and financial difficulties for individuals and societies (37,38).…”

Section: Discussionsupporting

confidence: 90%

The Prevalence of Different Types of Accidents Among Elderly People Residing in Khorramabad, Iran: A Descriptive-Analytical Study

et al. 2019

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Background: Nowadays, accidents among the elderly are of great importance due to the increasing population of the elderly and the effects of their lifestyles. Objectives: Therefore, the present study aimed to investigate the epidemiology of accidents among the elderly in Khorramabad, Iran, during 2016-2017. Methods: This descriptive-analytical study was conducted on 434 elderly individuals aged 60 years old and older who had experienced accidents and had referred to hospitals in Khorramabad. The census sampling method was used. The data collection instrument was a checklist to investigate the types of accidents among the elderly. Data were entered into SPSS software version 22 and analyzed using the chi-squared test at the significance level of 0.05. Results: The most common types of accidents were falls (64.5%), burns (14.7%), poisoning (14.5%), trauma (6%) and drowning (0.2%). There was a significant difference between the two sexes in terms of type of accident (P = 0.002). The prevalence of the abovementioned incidents was higher in females than males, except for poisoning, which was more common in males. There were no significant relationships among the type of accident with age and the location of the accident. Conclusions: Fall as the most common accident in the elderly calls for the design of preventive programs to enhance both the lifestyle and the safety of the living environment. The higher prevalence of poisoning in men can be attributed to opioid abuse, which is more common among males.

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Section: Discussionsupporting

confidence: 90%

The Prevalence of Different Types of Accidents Among Elderly People Residing in Khorramabad, Iran: A Descriptive-Analytical Study

et al. 2019

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“…Further associations demonstrate the increased likelihood of severe extracranial injury with RTA and falls. Paralleling these findings previous studies showed clinical outcomes worsen with increasing age [ 26 , 27 , 28 ] with low energy falls being four times as common mechanism over the age of 65 [ 28 ]. There is emerging relevance of TBI in the elderly population due to its increasing incidence, which is one of the key features of changing epidemiology in traumatic brain injury [ 1 ].…”

Section: Discussionsupporting

confidence: 82%

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis

2016

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BackgroundTraumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance.MethodsWe analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90–100%. Probabilistic associations were depicted using Bayesian networks.ResultsComplete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138–0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402–0.6641 and pAUCSE = 0.6332, 95% CI: 0.62–0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543–0.679, pAUCSE = 0.6436, 95% CI: 0.6289–0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did.ConclusionWe demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

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“…Mortality in elderly patients suffering major trauma is much higher than that in their younger counterparts, 22 23 but with appropriately configured services good outcomes can be achieved. 3 24 25 For many elderly patients suffering trauma undergoing non-operative management, targeted rehabilitation on a dedicated trauma unit may yield similar mortality benefits to those observed in orthopaedic trauma units with embedded orthogeriatric services. 26 Identification and early aggressive management of patients at higher risk of developing complications such as those with blunt chest wall trauma 27 may improve mortality and morbidity while reducing hospital stay.…”

Section: Discussionmentioning

confidence: 99%

The changing face of major trauma in the UK

et al. 2015

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AimMajor trauma (MT) has traditionally been viewed as a disease of young men caused by high-energy transfer mechanisms of injury, which has been reflected in the configuration of MT services. With ageing populations in Western societies, it is anticipated that the elderly will comprise an increasing proportion of the MT workload. The aim of this study was to describe changes in the demographics of MT in a developed Western health system over the last 20 years.MethodsThe Trauma Audit Research Network (TARN) database was interrogated to identify all cases of MT (injury severity score >15) between 1990 and the end of 2013. Age at presentation, gender, mechanism of injury and use of CT were recorded. For convenience, cases were categorised by age groups of 25 years and by common mechanisms of injury. Longitudinal changes each year were recorded.ResultsProfound changes in the demographics of recorded MT were observed. In 1990, the mean age of MT patients within the TARN database was 36.1, the largest age group suffering MT was 0–24 years (39.3%), the most common causative mechanism was road traffic collision (59.1%), 72.7% were male and 33.6% underwent CT. By 2013, mean age had increased to 53.8 years, the single largest age group was 25–50 years (27.1%), closely followed by those >75 years (26.9%), the most common mechanism was low falls (39.1%), 68.3% were male and 86.8% underwent CT.ConclusionsThis study suggests that the MT population identified in the UK is becoming more elderly, and the predominant mechanism that precipitates MT is a fall from <2 m. Significant improvements in outcomes from MT may be expected if services targeting the specific needs of the elderly are developed within MT centres.

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Neurological outcomes of neurosurgical operations for multiple trauma elderly patients in Hong Kong

Cited by 10 publications

References 18 publications

The Prevalence of Different Types of Accidents Among Elderly People Residing in Khorramabad, Iran: A Descriptive-Analytical Study

The Prevalence of Different Types of Accidents Among Elderly People Residing in Khorramabad, Iran: A Descriptive-Analytical Study

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis

The changing face of major trauma in the UK

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