Archives of Trauma Research
Volume:12 Issue: 3, Jul-Sep 2023

The diabetic foot constitutes one of the major diabetes-related morbidity. Diabetic foot ulcers (DFUs) are a disastrous consequence of the illness's course and are brought on by peripheral neuropathy, peripheral vascular disease, immunosuppression, and loss of glycemic control. Chronic ulcers must be treated surgically to prevent recurrences in cases with DFU. Every surgeon has to deal with this condition from very early in his career.

To investigate the various forms of traditional and modern DFU procedures and their consequences, this study can provide very fruitful guidance to the surgeons.

In this study, we searched Google Scholar, PubMed, Embase, Cochrane Library, Web of Science, and Scopus from 2013 to 2023. The inclusion criteria were patients with DFUs, and patients with a known history of diabetes mellitus (type I or type II); the parameters included in the study were the occurrence of adverse responses, the rate of amputation, fatalities, recurrence rate and some other indications. Using the review manager (RevMan version 5.4), the risk of bias evaluation was done in this study, with risk assessment domains classified as high, unclear, or low risk. To check for biases for example reporting bias, performance bias, attrition bias, selection bias, etc., a recommended approach was used.

The current review included 8 studies in total, with a total of 888 cases of DFUs. A recurrence rate of 24.51% was noted for the entire patient population. The recurrences noted in none of the three investigations. Overall mortality was 14.26%, according to three investigations.

It can be concluded that the patients treated with surgery show a lower recurrence rate of DFU as compared to the patients treated with only drug treatment. It is crucial to implement a new surgical treatment for DFUs to increase their success rate. A recently developed treatment program utilizing a multidisciplinary team approach and cooperation with referral hospitals is urgently required to improve the outcomes of DFU.

The registry systems are a tool to provide information which can be used for improvement of medical care and preventive policies over time. The efficacy of trauma registries in improving the quality of care depends on the quality of their data. This study aimed to determine the completeness, reliability, validity, and patient coverage of the Sina Hospital Trauma Registry (SHTR). The data collected in the SHTR by May 28, 2021, were used to assess the data completeness. A random sample of 250 patients was re-registered to evaluate the reliability and validity. The intraclass correlation coefficient (ICC) and the kappa and weighted kappa statistics were used to assess the reliability of data and agreement. The coverage was determined by comparing the list of trauma patients obtained from the trauma registry and the one from the hospital information system. The overall average completeness for all variables was 97.9%. The inter-rater agreement on important variables including Abbreviated Injury Scale (AIS), Injury Severity Score (ISS), and final outcomes was, respectively, perfect (kappa= 0.90) optimal (ICC = 0.72), and substantial (kappa= 0.71). The patient coverage was 79.6%. The results confirm that the data quality and patient coverage were high. In addition, although the reliability of most variables was acceptable, the agreement between NTRI’s data and the medical record for AIS codes and ISS was moderate.

Recognizing and determining severe trauma is essential for choosing the appropriate treatment strategy. The aim of this study was to find the predictive value of the quick sequential organ failure assessment (qSOFA) score for in-hospital mortality in adult trauma patients. This prospective observational study was conducted on adult patients with multiple trauma presenting to the emergency department. The qSOFA score was calculated according to the initially recorded variables. The primary outcome was in-hospital mortality. The predictive value of qSOFA was evaluated using the Area Under Receiver Operating Characteristic (AUC) analysis. Finally, 775 multiple trauma patients with a mean age of 38.68±18.74 were admitted. Of these, 34 people (4.39%) died and 741 subjects were discharged from hospital. The mean qSOFA score was 0.41±0.64, significantly higher in the survived patients than in the non-survived patients (P < 0.001). The AUC of qSOFA score to predict in-hospital mortality was 0.878 (95% confidence interval: 0.853-0.900); thus, qSOFA was a good predictor of in-hospital mortality in multiple trauma patients. The qSOFA score can be considered a simple and rapid screening tool for identifying multiple-trauma patients.

Trauma registries collect and document data about the acute injury care in hospitals. The goal of trauma care systems is to reduce injury occurrence and enhance trauma patient survival rates. In this article, the Kashan trauma registry was used to predict trauma patient discharge status using machine learning. This study employed 3930 Kashan Trauma Centre Registry entries after preprocessing. The study experimented with decision trees of varying complexity, using three separate metrics - information gain, Gini index, and gain ratio - to build and evaluate the trees. Finally, bagging, boosting and stacking ensemble learning techniques were implemented to evaluate their predictive performance. Ensemble learning models were developed based on decision trees of varying depths that utilized different learning measures/metrics. The predictive performance of the algorithms was evaluated using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC). This study aimed to compare ensemble-learning techniques like bagging, boosting and stacking to decision trees configured with various parameter settings, to assess their ability to predict trauma patients' discharge status outcomes. The stacking technique, which used decision tree algorithms (depth=5) that integrated parameters like information gain, gain ratio and Gini index at the base level along with KNN (k=12) using Euclidean distance, and then incorporated logistic regression as the meta-classifier, demonstrated superior predictive performance compared to using individual decision trees, bagging or boosting approaches alone. However, while decision trees are straightforward algorithms and ensemble methods are more time-consuming and computationally complex, this study indicates that stacking learning is superior to single decision tree methods with a variety of parameters, bagging, and boosting.

A number of different risk factors for road traffic injuries have been identified following decades of research in the field of road traffic safety. Among the main risk factors, the personality characteristics of drivers have received much attention. Sensation-seeking personality is very common in drug addicts and is considered as a determining factor in increasing the risk of traffic accidents. This study aimed to investigate the relationships between sensation-seeking and addiction with accident risk among professional drivers with structural equation modeling approach. This cross-sectional study was conducted on 303 professional drivers in Kashan city. Sensation-seeking trait was measured using the Zuckerman Sensation-Seeking Scale-V (SSS-V) (it consists of 40 forced-choice questions). Furthermore, a questionnaire was prepared to evaluate the number of accidents and socio-demographic factors. Structural equation modeling approaches using software Amos 24.0 were used to investigate the research hypothesis. According to the results, the average age of the participants was 43.15 years (SD=10.29), some of them were passenger vehicle drivers (20.1%) and the rest were cargo transport drivers (79.9%), most of whom were married (94%). Participants drove for an average of 11.3 years (SD=9.2) with an average speed of 85.9 km/h (SD=13.2). The sensation-seeking questionnaire had acceptable validity and reliability indicators (factor loading, Cronbach's alpha, composite reliability, average variance extracted were all higher than 0.7, 0.7, 0.5, 0.7). According to the results of the final model, there are significant and positive relationships between sensation-seeking and addiction and accident risk, as well as the fit indices of the model are good (x2/df=2.79; CFI=0.98; RMSEA=0.07). There is a significant relationship between sensation seeking and addiction with the risk of accidents, therefore, a very important and primary issue in reducing road accidents and accidents is to know the prevalence and extent of occupational driving with drugs, in addition, it is necessary to further investigate the factors that lead to driving while taking drugs. Therefore, it is suggested that by examining the perceptions and attitudes of professional drivers, we will understand what causes this behavior in order to act as a deterrent for these behaviors by developing targeted educational strategies and also implementing relevant laws.

Although patients with mild traumatic brain injury (mTBI) rarely exhibit an identifiable lesion on neuroimaging, they frequently experience neurocognitive problems. The present study aimed to determine the cut-off point, sensitivity, and specificity of the Montreal Cognitive Assessment (MoCA) test in mTBI patients. In this cross-sectional-analytical study, the case group included 79 patients with mTBI were enrolled in the trauma, neurosurgery, and ICU ward of PourSina hospital (northern Iran), and there were 79 healthy individuals in the control group. Both groups were participating in this study were cognitively evaluated by the MoCA and MMSE test. Moreover, as retesting reliability and determining the concurrent and convergent validity of the MoCA, and Pearson correlation coefficient between two groups, MMSE test was performed on 20 mTBI patients with an average time interval of 3 days. The independent t-test, Cronbach’s alpha and discriminant analysis used for determining the distribution, internal consistency reliability and sensitivity, specificity, and diagnostic value of the MoCA test between groups respectively. The results showed a cut-off point of 26/27 as the probable point of cognitive impairment in mTBI. Also, in order to identify cognitive impairment in mTBI patients, this test reported sensitivity of 0.62 and   specificity of 0.81 with Youden's index of 0.43. In screening for possible mild cognitive impairment in mTBI patients, the MoCA is relatively useful and should not be used only as a substitute for a complete neuropsychological assessment with diagnostic purposes.

Road traffic accidents in Iran significantly contribute to morbidity and mortality, thereby profoundly impacting public health. According to 2019 World Health Organization (WHO) data, Iran ranks 130th out of 191 countries in traffic-related mortality rate due to accidents and impacts 21 per 100,000 individuals. In contrast, countries like Switzerland, Singapore, and Norway demonstrate admirable metrics by having less than 3 deaths per 100,000 individuals. Indeed, Iran's road traffic accident rate surpasses global benchmarks by 20%. Comprehensive studies undertaken within Iran have identified the primary contributors to road traffic accidents as: human error during driving, vehicular technical failures, suboptimal road infrastructure, and environmental conditions. Additionally, a scarcity of medical equipment and post-accident emergency medical care services exacerbate an already high mortality rate. An unfortunate reality is that road traffic accident-induced injuries rank among the top five causes of death in Iran, with highest number of fatalities recorded in 2004, accounting for 27,000 lives.[1,2]At the onset of the COVID-19 pandemic in Iran during 2020 and 2021, quarantine and travel restrictions led to a temporal decline in road traffic accidents (approximately 15,000 individuals annually). However, as 2022 witnessed a resurgence in intercity travel, the transient nature of this trend saw an escalation of accidents to over 19,000 (a 14.5% increase from the prior year) [Figure 1]. On average, 50 individuals were fatally injured on a daily basis due to road traffic accidents in 2022 (approximately 2 deaths per hour); which was amplified tenfold for those that were either injured or disabled. Yet the COVID-19 outbreak in 2020 and 2021 overshadowed road traffic accident data, and became the primary public health emphasis (prevention and treatment) during the pandemic. Despite a daily mortality rate of 50 individuals from road traffic accidents, a peak of over 500 daily COVID-19 deaths in Iran rendered the former public health concern all but inconspicuous. Certainly, such a disparity could be attributed to travel restrictions and the rapid escalation and unknown nature of the SARS-CoV-2 virus, the lack of vaccines and targeted treatments initially, or the extraordinary surge in mortality rates.[3,4]Fatalities from road traffic accidents historically have failed to evoke the same magnitude of public alarm as did the considerably higher mortality rates from COVID-19. Despite global initiatives that have substantially controlled death from COVID-19 (sometimes reaching zero in Iran), road traffic accident rates remain largely unchanged. This is a call to action for road traffic accident casualties to cease in Iran.  We urge Iranians to adopt vehicular safety behaviors (avoiding speeding, not wearing seat belts, reckless driving, drowsiness, and distractions) with the same vigor as pandemic precautions. We appeal to the Iranian Ministry of Roads and Urban Development and National Road Safety Commission along with automotive manufacturing sectors to work together and continue to prioritize and enhance efforts to address this escalating crisis. Envisioning a safer and traffic accident-free Iran remains a challenge, but is a challenge worth confronting.