Effect of the First Wave of the Belgian COVID-19 Pandemic on Physician-Provided Prehospital Critical Care in the City of Antwerp (Belgium)

Introduction: There is evidence to suggest that patients delayed seeking urgent medical care during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. A delay in health-seeking behavior could increase the disease severity of patients in the prehospital setting. The combination of COVID-19-related missions and augmented disease severity in the prehospital environment could result in an increase in the number and severity of physician-staffed prehospital interventions, potentially putting a strain on this highly specialized service. Study Objective: The aim was to investigate if the COVID-19 pandemic influences the frequency of physician-staffed prehospital interventions, prehospital mortality, illness severity during prehospital interventions, and the distribution in the prehospital diagnoses. Methods: A retrospective, multicenter cohort study was conducted on prehospital charts from March 14, 2020 through April 30, 2020, compared to the same period in 2019, in an urban area. Recorded data included demographics, prehospital diagnosis, physiological parameters, mortality, and COVID-status. A modified National Health Service (NHS) National Early Warning Score (NEWS) was calculated for each intervention to assess for disease severity. Data were analyzed with univariate and descriptive statistics. Results: There was a 31% decrease in physician-staffed prehospital interventions during the period under investigation in 2020 as compared to 2019 (2019: 644 missions and 2020: 446 missions), with an increase in prehospital mortality (OR = 0.646; 95% CI, 0.435 – 0.959). During the study period, there was a marked decrease in the low and medium NEWS groups, respectively, with an OR of 1.366 (95% CI, 1.036 – 1.802) and 1.376 (0.987 – 1.920). A small increase was seen in the high NEWS group, with an OR of 0.804 (95% CI, 0.566 – 1.140); 2019: 80 (13.67%) and 2020: 69 (16.46%). With an overall decrease in cases in all diagnostic categories, a significant increase was observed for respiratory illness (31%; P = .004) and cardiac arrest (54%; P < .001), combined with a significant decrease for intoxications (-58%; P = .007). Due to the national test strategy at that time, a COVID-19 polymerase chain reaction (PCR) result was available in only 125 (30%) patients, of which 20 (16%) were positive. Conclusion: The frequency of physician-staffed prehospital interventions decreased significantly. There was a marked reduction in interventions for lower illness severity and an increase in higher illness severity and mortality. Further investigation is needed to fully understand the reasons for these changes.

Prehospital FAST reduces time to admission and operative treatment: a prospective, randomized, multicenter trial

Background The focused assessment with sonography in trauma (FAST) exam is an established trauma care diagnostic procedure. Ultrasound performed during prehospital care can improve early treatment and management of the patients. In this prospective randomized clinical trial, we wanted to assess whether a pre-hospital FAST (p-FAST) influences pre-hospital strategy and the time to operative treatment. Methods We studied 296 trauma victims in a prehospital setting. Inclusion criteria were potential abdominal injuries identified either by clinical examination or suggested by the mechanism of injury. Physician-staffed helicopters and emergency ambulances were equipped with portable ultrasound devices. According to a scheme related to calendar weeks, a clinical exam only (CEX) or a clinical exam together with a p-FAST (CEX-p-FAST) was conducted. Outcome variables were prehospital diagnosis and strategy, the time to admission to the trauma room and to operation theater. The study was approved by the university ethical committee (REB#: 46/06). Results CEX-p-FAST showed a high sensitivity (94.7%) and specificity (97.6%) in detection of free fluid compared to CEX-only (80.0%, 84.4%). The median time to admission was reduced significantly by 13 min and to operative treatment by 15 min after CEX-p-FAST. We observed a cross-over rate of 30.8% of p-FAST (n = 36) to CEX-p-FAST during the CEX-only weeks. Conclusion According to the experience of the principal investigators, CEX-p-FAST was superior to CEX-only. Despite the time needed for p-FAST, the relevant admission time was significantly shorter. Thus, p-FAST is recommended in addition to CEX if possible for decision-making in prehospital trauma care. Trial registration German Clinical Trials Register #DRKS00022117—Registered 10 July 2020—Retrospectively registered, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00022117.

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

Hemopneumothorax detection through the process of artificial evolution - a feasibility study

Background Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model. Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air or saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy, sensitivity and specificity ranging from 64 to 100%, 63 to 100%, and 63 to 100%, respectively, in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more, and regardless of background noise. Conclusions We present a novel, objective device for rapid diagnosis of potentially lethal thoracic injuries. With further optimization, such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

Outcomes of Patients Treated with Prehospital Noninvasive Ventilation: Observational Retrospective Multicenter Study in the Northern French Alps

Noninvasive ventilation (NIV) improves the outcome of acute cardiogenic pulmonary edema (AcPE) and acute exacerbation of chronic obstructive pulmonary disease (aeCOPD) but is not recommended in pneumonia. The aim of this study was to assess the appropriateness of the use of NIV in a prehospital setting, where etiological diagnostics rely mainly on clinical examination. This observational multicenter retrospective study included all the patients treated with NIV by three mobile medical emergency teams in 2015. Prehospital diagnoses and hospital diagnoses were extracted from the medical charts. The appropriateness of NIV was determined by matching the hospital diagnosis to the current guidelines. Among the 14,067 patients screened, 172 (1.2%) were treated with NIV. The more frequent prehospital diagnoses were AcPE (n = 102, 59%), acute respiratory failure of undetermined cause (n = 46, 28%) and aeCOPD (n = 17, 10%). An accurate prehospital diagnosis was more frequent for AcPE (83/88, 94%) than for aeCOPD (14/32, 44%; p < 0.01). Only two of the 25 (8%) pneumonia cases were diagnosed during prehospital management. Prehospital NIV was inappropriate for 32 (21%) patients. Patients with inappropriate NIV had a higher rate of in-hospital intubation than patients with appropriate NIV (38% vs. 8%; p < 0.001). This high frequency of inappropriate NIV could be reduced by an improvement in the prehospital detection of aeCOPD and pneumonia.

Prehospital Diagnosis of Shortness of Breath Caused by Profound Metformin-Associated Metabolic Acidosis

Shortness of breath is a common complaint among patients in emergency medicine. While most common causes are usually promptly identified, less frequent aetiologies might be challenging to diagnose, especially in the pre-hospital setting. We report a case of prehospital dyspnoea initially ascribed to pulmonary oedema which turned out to be the result of profound metformin-associated metabolic acidosis. This diagnosis was already made during the prehospital phase by virtue of arterial blood gas measurement. Pre-hospital measurement of arterial blood gases is therefore feasible and can improve diagnostic accuracy in the field, thus avoiding unnecessary delay and potential harm to the patient before initiating the appropriate therapeutic actions.

Hemopneumothorax Detection Through the Process of Artificial Evolution - A Feasibility Study

Background: Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

Hemopneumothorax Detection Through the Process of Artificial Evolution - A Feasibility Study

Background : Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods : Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results : The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise. Conclusions : We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

Hemopneumothorax Detection Through the Process of Artificial Evolution

Background: Tension pneumothorax is a leading cause of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

Physician Prehospital Care in Mexico City: Retrospective Analysis of Endotracheal Intubation in Patients with Severe Head Trauma

Introduction:In Mexico, physicians have become part of public service prehospital care. Head injured patients are a sensitive group that can benefit from early advanced measures to protect the airway, with the objective to reduce hypoxia and maintain normocapnia.Problem:The occurrence of endotracheal intubation to patients with severe head injuries by prehospital physicians working at Mexico City’s Service of Emergency Medical Care (SAMU) is unknown.Methods:A retrospective analysis of five-year data (2012-2016) from Mexico City’s Medical Emergencies Regulation Center was performed. Only SAMU ambulance services were analyzed. Adult patients with a prehospital diagnosis of head injury based on mechanism of injury and physical examination with a Glasgow Coma Scale (GCS) <nine were included.Results:A total of 293 cases met the inclusion criteria; the mean GCS was five points. Of those, 150 (51.1%) patients were intubated. There was no difference in the occurrence of intubation among the different GCS scales, or if the patient was considered to have isolated head trauma versus polytrauma. Fifteen patients were intubated using sedation and neuromuscular blockage. Four patients were intubated with sedation alone and six patients with neuromuscular blockage alone. One patient was intubated using opioid analgesia, sedation, and neuromuscular blockage.Conclusions:Patients with severe head injuries cared by prehospital physicians in Mexico City were intubated 51.1% of the time and were more likely to be intubated without the assistance of anesthetics.