Prehospital Basic Life Support Research Articles

“ChatGPT, Can You Help Me Save My Child’s Life?” - Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis

BackgroundPaediatric emergencies are challenging for healthcare workers, first aiders, and parents waiting for emergency medical services to arrive. With the expected rise of virtual assistants, people will likely seek help from such digital AI tools, especially in regions lacking emergency medical services. Large Language Models like ChatGPT proved effective in providing health-related information and are competent in medical exams but are questioned regarding patient safety. Currently, there is no information on ChatGPT’s performance in supporting parents in paediatric emergencies requiring help from emergency medical services. This study aimed to test 20 paediatric and two basic life support case vignettes for ChatGPT and GPT-4 performance and safety in children.MethodsWe provided the cases three times each to two models, ChatGPT and GPT-4, and assessed the diagnostic accuracy, emergency call advice, and the validity of advice given to parents.ResultsBoth models recognized the emergency in the cases, except for septic shock and pulmonary embolism, and identified the correct diagnosis in 94%. However, ChatGPT/GPT-4 reliably advised to call emergency services only in 12 of 22 cases (54%), gave correct first aid instructions in 9 cases (45%) and incorrectly advised advanced life support techniques to parents in 3 of 22 cases (13.6%).ConclusionConsidering these results of the recent ChatGPT versions, the validity, reliability and thus safety of ChatGPT/GPT-4 as an emergency support tool is questionable. However, whether humans would perform better in the same situation is uncertain. Moreover, other studies have shown that human emergency call operators are also inaccurate, partly with worse performance than ChatGPT/GPT-4 in our study. However, one of the main limitations of the study is that we used prototypical cases, and the management may differ from urban to rural areas and between different countries, indicating the need for further evaluation of the context sensitivity and adaptability of the model. Nevertheless, ChatGPT and the new versions under development may be promising tools for assisting lay first responders, operators, and professionals in diagnosing a paediatric emergency.Trial registrationNot applicable.

Epidemiology of Pediatric Prehospital Basic Life Support Care in the United States

Children have unique medical needs compared to adults. Emergency medical services personnel need proper equipment and training to care for children. The purpose of this study is to characterize emergency medical services pediatric basic life support to help better understand the needs of children transported by ambulance. Pediatric basic life support patients were identified in this retrospective descriptive study. Descriptive statistics were used to examine incident location, possible injury, cardiac arrest, resuscitation attempted, chief complaint, primary symptom, provider's primary impression, cause of injury, and procedures performed during pediatric basic life support calls using the largest aggregate of emergency medical services data available, the 2013 National Emergency Medical Services Information System (NEMSIS) Public Release Research Data Set. Pediatric calls represented 7.4% of emergency medical services activations. Most pediatric patients were male (49.8%), White (40.0%), and of non-Hispanic origin (56.5%). Most incidents occurred in the home. Injury, cardiac arrest, and resuscitation attempts were highest in the 15 to 19 year old age group. Global complaints (37.1%) predominated by anatomic location and musculoskeletal complaints (26.9%) by organ system. The most common primary symptom was pain (30.3%) followed by mental/psychiatric (13.4%). Provider's top primary impression was traumatic injury (35.7%). The most common cause of injury was motor vehicle accident (32.3%). The most common procedure performed was patient assessment (27.4%). Median EMS system response time was 7 minutes (IQR: 5–12). Median EMS scene time was 12 minutes (IQR: 8–19). Median transport time was 14 minutes (IQR: 8–24). Median EMS total call time was 51 minutes (IQR: 33–77). The epidemiology of pediatric basic life support can help to guide efforts in both emergency medical services operations and training.

The Reliability of Modified Termination of Resuscitation Rules after Arrival at the Emergency Department

Introduction Pre-hospital termination of resuscitation (TOR) is not a usual practice in many cities. The current study aimed to examine the reliability of the modified basic life support (ED-BLS) and advanced life support (ED-ALS) rules for TOR after patient arrival at the emergency department (ED). Methods In this retrospective cohort study, adult non-traumatic cardiac arrest patients who received pre-hospital basic life support and defibrillator (BLS-D) mode of service in Taoyuan County in northern Taiwan during the study period were assessed. Data were retrieved from web-based registry records. Results Of the 1612 patients included, 40 (2.5%) achieved survival to discharge. The ED-ALS rule showed higher specificity (ED-ALS rule: 82.5% {95% confidence interval [CI]: 68.1-91.3} vs. ED-BLS rule: 50.0% {95%CI: 35.2-64.8}) and positive predictive value (ED-ALS rule: 99.0% {95% CI: 97.9-99.5} vs. ED-BLS rule: 98.6% {95%CI: 97.8-99.1}) than the ED-BLS rule in terms of predicting no survival to discharge after patient arrival at the ED. Among patients who fulfilled all criteria for the ED-BLS and ED-ALS rule, 20 (1.4%) and seven (1.0%) survived to discharge, respectively. Application of the ED-BLS and ED-ALS rules could have reduced further resuscitation efforts after arrival at the ED by 86.4% and 43.1%, respectively. Conclusion For non-traumatic out-of-hospital cardiac arrest patients who receive BLS-D service, the ED-ALS rule has a higher specificity and PPV than the ED-BLS rule to predict no survival to discharge after patient arrival at the ED. Using the ED-ALS rule to terminate resuscitation after arrival at the ED should be prospectively validated. (Hong Kong j.emerg.med. 2014;21:283-290)

Clinical Decision Rules for Termination of Resuscitation in Out-of-Hospital Cardiac Arrest

Background: Out-of-hospital cardiac arrest (OHCA) has a low probability of survival to hospital discharge. Four clinical decision rules (CDRs) have been validated to identify patients with no probability of survival. Three of these rules focus on exclusive prehospital basic life support care for OHCA, and two of these rules focus on prehospital advanced life support care for OHCA. Clinical Question: Can a CDR for the termination of resuscitation identify a patient with no probability of survival in the setting of OHCA? Evidence Review: Six validation studies were selected from a PubMed search. A structured review of each of the studies is presented. Results: In OHCA receiving basic life support care, the BLS-TOR (basic life support termination of resuscitation) rule has a positive predictive value for death of 99.5% (95% confidence interval 98.9–99.8%), and decreases the transportation of all patients by 62.6%. This rule has been appropriately validated for widespread use. In OHCA receiving advanced life support care, no current rule has been appropriately validated for widespread use. Conclusions: The BLS-TOR rule is a simple rule that identifies patients who will not survive OHCA. Further research is required to identify similarly robust CDRs for patients receiving advanced life support care in the setting of OHCA.

The effectiveness of supraglottic airway devices in pre hospital basic life support airway management

Basic life support (BLS) providers resuscitate in ambulances using oropharyngeal (OP) airways and bag valve mask (BVM) ventilation. This study is designed to evaluate whether mobile BVM ventilation is effective,...