Prehospital prediction of severe injury in road traffic injuries: A multicenter cross-sectional study

Care needs of adults with spinal trauma in the prehospital and hospital setting from the perspective of patient care team: A qualitative research

BACKGROUND

Appropriate care of patients with definite spinal cord injury or at risk of it in the prehospital and hospital stages requires comprehensive planning in the health system. It is also the requirement of any successful program to explain the needs from the perspective of its stakeholders. Thus, this study aimed to discover the care needs of adults with spinal trauma in prehospital and hospital settings from the perspective of the patient care team.

MATERIALS AND METHODS

This qualitative study was conducted with the participation of urban and rural prehospital emergency personnel and emergency departments of educational and therapeutic hospitals affiliated to Isfahan, Tehran, Shiraz, Kermanshah, Ahvaz, and Yasuj Universities of Medical Sciences, through conducting 36 in-depth semi-structured interviews from September to December 2021. Using purposive sampling method, the participants were selected considering the maximum variation. The data saturation was reached after conducting interviews and group discussions with 36 subjects. Data were analyzed using conventional content analysis approach. Lundman and Graneheim approach were used for the study rigour. Data were simultaneously analyzed using MAXQDA software version 10.

RESULT

During the data analysis, two themes of prehospital care with two main categories (emergency care and management of secondary complications of spinal trauma) and hospital care with two main categories (emergency care and management of secondary complications of spinal trauma) emerged.

CONCLUSION

Emergency care and management of secondary complications of spinal cord injury in the prehospital and hospital stages can affect treatment results, improve quality of life, and reduce mortality rate, secondary injuries, and healthcare costs. Thus, identification of the care needs of the adults with spinal trauma from the perspective of the patient care team can help the authorities to plan appropriate interventions.

Machine Learning-Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

Background

Early recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies.

Objective

In this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident.

Methods

Audio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

Results

Although the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments.

Conclusions

A machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

Extrication techniques of entrapped car crash victims: a scoping review

OBJECTIVE

to summarize the extrication techniques of entrapped car crash victims with potential spinal injury.

METHOD

a literature review study, of scoping review type, using the MEDLINE/PubMed, CINAHL, Scopus, Science Direct, Web of Science, Cochrane Library and gray literature data sources, without time frame, with studies that addressed extricating techniques extrication of entrapped car crash victims.

RESULTS

a total of 33 studies were included that enabled identifying and summarizing the different types of extrication and respective devices for extrication of entrapped car crash victims, indicated according to injury assessment and the victim's clinical condition. All pointed to the need for techniques to maintain neutral alignment and prevent spine twists.

CONCLUSION

this study indicated that injury assessment with an emphasis on the victim's clinical condition provides a coherent decision-making regarding the technique and device to be used. However, carrying out other comparative studies between existing techniques may help in the decision-making process more assertively.