The dynamics of prehospital/hospital care and modes of transport during civil conflict and terrorist incidents

Factors Influencing Outcomes of Trauma Patients Transferred in Trauma Systems by Air or Ground Ambulance: A Systematic Review

OBJECTIVES

This systematic review aims to determine the effectiveness of ambulance transportation versus helicopter transportation on mortality for trauma patients.

METHODS

A systematic review of published and unpublished databases (to August 2023) was performed. Studies, reporting mortality, for people who experienced trauma and were transported to a trauma unit by ambulance or helicopter were eligible. The Newcastle-Ottawa scale was employed to evaluate study quality.

RESULTS

Of the 7,323 studies screened, 63 met the inclusion criteria. Thirty-two percent of these studies included patients with diverse injury types, while nine studies included patients across all age groups. The majority (92%) of the included data were retrospective in nature. Eighteen studies (28.57%) achieved the highest score on the Newcastle-Ottawa scale suggesting high-quality evidence. Seven studies examining 24-h mortality reported variable findings. Eighteen studies reported mortality without exact time points through adjusted analyses, 17 favored air transport. Air transport showed an advantage across all subgroups in the adjusted data, while the unadjusted data presented relatively similar outcomes between the two modes of transport.

CONCLUSIONS

This systematic review found that adjusted analyses consistently favored air transport over ground transport. Unadjusted analyses showed no significant difference between the two modes of transport, except in specific subgroups. Further subgroup analyses revealed notable disparities between the two modalities, suggesting that these differences may be influenced by multiple factors. These findings highlight the need for further research to clarify the true impact of transport modality on trauma outcomes.

Pre-hospital emergency response to terrorist attacks: A scoping review

Background:

Terrorist incidents are on the rise in the world, and many countries have been involved so far. Unfortunately, many innocent people fall victim to such incidents every year. The pre-hospital emergency, as one of the most important organs in the management and service of the victims, plays a vital role in these events.

Objectives:

This scoping review aimed to evaluate the performance and preparedness of the pre-hospital emergency in the world for such Terrorist incidents.

Methods:

In order to access the documents and scientific evidence relevant to the purpose of our research, selected keywords were searched in PubMed, Scopus, and Web of Science databases. Finally, we collected the required information through a pre-designed data extraction form that designed based on the purpose of this study.

Results:

The initial search, with the specified search strategies, resulted into 794 documents (263, 488, and 43 documents from PubMed, Scopus, and Web of Science databases, respectively). Finally, eight papers were selected through the full text of the selected articles, three of which were qualitative and three were quantitative, and two were mix-methods (qualitative/quantitative). Our findings show that published papers have so far emphasized four main axes, namely, preparation, training and practice, effective communication, and the proper triage and transmission of these four axes.

Conclusion:

A few studies have been done in this area and more studies should be done in different areas and sectors, and given that terrorist incidents are on rise and the pre-hospital emergency organization as one of the most important organizations is not well prepared to respond these events although it has a vital role to play, they need to be more prepared to effectively manage these incidents.

INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING

Due to the advancement in data collection and maintenance strategies, the current clinical databases around the globe are rich in a sense that these contain detailed information not only about the individual’s medical conditions, but also about the environmental features, associated with the individual. Classification within this data could provide new medical insights. Data mining technology has become an attraction for researchers due to its affectivity and efficacy in the field of biomedicine research. Due to the diverse structure of such data sets, only few successful techniques and easy to use softwares, are available in literature. A Bayesian analysis provides a more intuitive statement of probability that hypothesis is true. Bayesian approach uses all available information and can give answers to complex questions more accurately. This means that Bayesian methods include prior information. In Bayesian analysis, no relevant information is excluded as prior represents all the available information apart from data itself. Bayesian techniques are specifically used for decision making. Uncertainty is the main hurdle in making decisions. Due to lack of information about relevant parameters, there is uncertainty about given decision. Bayesian methods measure these uncertainties by using probability. In this study, selected techniques of biostatistical Bayesian inference (the probability based inferencing approach, to identify uncertainty in databases) are discussed. To show the efficiency of a Hybrid technique, its application on two distinct data sets is presented in a novel way.