The latest medical research on Emergency Medicine

A short systematic review was undertaken to assess whether intradermal sterile water injections (ISWI) provide effective pain relief in adult patie...

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The high incidence of airway management failure in the emergency department (ED) necessitates a comparative analysis of laryngoscopy methods. This study aims to compare the success and complications associated with video-assisted laryngoscopy (VL) and direct laryngoscopy (DL) in emergency tracheal intubation in ED.

This retrospective cohort study was conducted at the ED of Thammasat University Hospital. It involved adult patients undergoing emergency tracheal intubation using either VL (GlideScope®) or DL (Macintosh®). The outcomes assessed were success rates of intubation and occurrence of peri-intubation adverse events. Propensity score matching and multivariable risk regression analysis were employed for statistical evaluation.

The study included 3,424 patients, with 342 in the VL group and 3,082 in the DL group. The initial analysis revealed no significant differences in the intubation success rates between the two methods. However, the VL group experienced fewer peri-intubation adverse events (33% compared to 40%). After propensity score matching, a higher first-attempt success rate was observed in the DL group (88.9% vs. 81.3%, risk difference: 7.6, 95% CI: 1.9 to 13.2, p=0.009), but there was no statistically significant difference in peri-intubation adverse events. VL had a lower first-attempt success rate among low-experience intubators. Subgroup analyses of intubators with moderate and high experience, as well as patients who received both induction agents and neuromuscular blocking agents, show results consistent with the analysis of the entire cohort.

Both VL and DL have comparable first-attempt success rates and peri-intubation adverse events. VL is particularly beneficial when used by moderately or highly experienced intubator. The choice of intubation method, combined with clinical experience and technique plays a critical role in the success and safety of emergency intubations.

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EudraCT 201,600,377,317, April 20, 2017. https://eudract.ema.europa.eu/ .

This prospective, equally randomized, open-label, non-inferiority trial was conducted at a Pediatric Emergency Department. Previously healthy children 3-15 years of age, with an extremity fracture or luxation or a burn and requiring procedural sedation and analgesia were eligible. Patients were randomized to receive either intranasal dexmedetomidine or inhaled nitrous oxide. The primary outcome measure was highest pain level during the procedure, assessed with Face, Legs, Activity, Cry, Consolability scale (FLACC). Mann-Whitney U test (continuous variables) and Fisher's test (categorical variables) were used for statistical analysis.

The highest FLACC was median 4 (IQR 3-6) with intranasal dexmedetomidine and median 4 (IQR 2-6) with nitrous oxide. The median of the difference between samples from each group for FLACC was 0 with 95%CI (0-1), thus intranasal dexmedetomidine was not inferior to nitrous oxide with respect to the level of pain during the procedure. The same method for procedural sedation and analgesia would be accepted by 52/74 (82.5%) children and 65/74 (91.5%) parents in the intranasal dexmedetomidine group respectively 59/74 (88.1%) versus 70/74 (94.6%) with nitrous oxide. No serious adverse events were reported.

The results of this trial support that intranasal dexmedetomidine is not inferior to 50% nitrous oxide in providing analgesia for a painful procedure in children 3-15 years of age and can be considered as an alternative to 50% nitrous oxide for procedural sedation and analgesia.

It is crucial to identify high-risk patients with infectious conditions for appropriate management. We previously found that inflammatory markers a...

Emergency medical services often serve as the initial point of contact for septic patients, offering crucial pre-hospital intervention opportunitie...

Trauma remains a significant cause of mortality and morbidity. Non-compressible torso haemorrhage is one of the key drives of these mortality data....

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This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critical care within 12 h of ED arrival.

Three machine learning models (LASSO regression, gradient-boosted trees, and a deep learning model with embeddings) were developed using retrospective data from 670,841 ED visits to the Jewish General Hospital from June 2012 to Jan 2021. The model outcome was the need for critical care within the first 12 h of ED arrival. Metrics, including the areas under the receiver-operator characteristic curve (ROC) and precision-recall curve (PRC) were used for performance evaluation. Shapley additive explanation scores were used to compare predictor importance.

The three machine learning models (deep learning, gradient-boosted trees and LASSO regression) had areas under the ROC of 0.926 ± 0.003, 0.912 ± 0.003 and 0.892 ± 0.004 respectively, and areas under the PRC of 0.27 ± 0.01, 0.24 ± 0.01 and 0.23 ± 0.01 respectively. In comparison, the CTAS score had an area under the ROC of 0.804 ± 0.006 and under the PRC of 0.11 ± 0.01. The predictors of most importance were similar between the models.

Machine learning models outperformed CTAS in identifying, at the point of ED triage, patients likely to need early critical care. If validated in future studies, machine learning models such as the ones developed here may be considered for incorporation in future revisions of the CTAS triage algorithm, potentially improving discrimination and reliability.

Functional Neurological Disorder (FND) presents unique challenges in the emergency department (ED), where patients often arrive with varied and vag...

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The early assessment of the severity of polytrauma patients is key for their optimal management. The aim of this study was to investigate the discriminative performance of the NACA score in a large dataset by stratifying the severity of polytraumatized patients in correlation to injury severity score (ISS), Glasgow Coma Scale (GCS), and mortality.

This study on the Swiss Trauma Registry investigated 2239 polytraumatized patient (54.3 ± 22.8 years) enrolled from 2015 to 2023: 0.5% were NACA 3, 76.7% NACA 4, 21.4% NACA 5, and 1.4% NACA 6. The NACA predictive value of patients' mortality was investigated, as well as the correlation of ISS and GCS scores, and other factors influencing patients' survival at discharge and after 28 days.

In NACA 4 and 5 the survival rate during hospitalization was 97.7% and 82.5%, respectively, and 28-day mortality 3.5% and 23.5%, respectively (p < 0.0005). NACA correlated with GCS in the prehospital phase and in the emergency room (p < 0.0005), as well as with ISS (p < 0.0005). NACA 4 and 5 presented different injury patterns (fall < 3 m vs vehicle accident) with NACA 5 requiring more CPR and intubation (p < 0.001, p < 0.0005). The ROC AUC analysis showed the prehospital NACA and GCS values as the strongest variables predicting patients' survival.

This study provides valuable evidence supporting the effectiveness of the NACA score in assessing the severity of polytrauma patients in both the pre-ER and ER condition. Considering the statistical significant correlation with the GCS and with the ISS, NACA is a valid score for assessing polytrauma patients.

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In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability, bias, and incorrect patient classification. Studies suggest that Machine Learning (ML) and Natural Language Processing (NLP) could enhance triage accuracy and consistency. This review analyzes studies on ML and/or NLP algorithms for ED patient triage.

Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we conducted a systematic review across five databases: Web of Science, PubMed, Scopus, IEEE Xplore, and ACM Digital Library, from their inception of each database to October 2023. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only articles employing at least one ML and/or NLP method for patient triage classification were included.

Sixty studies covering 57 ML algorithms were included. Logistic Regression (LR) was the most used model, while eXtreme Gradient Boosting (XGBoost), decision tree-based algorithms with Gradient Boosting (GB), and Deep Neural Networks (DNNs) showed superior performance. Frequent predictive variables included demographics and vital signs, with oxygen saturation, chief complaints, systolic blood pressure, age, and mode of arrival being the most retained. The ML algorithms showed significant bias risk due to critical bias assessment in classification models.

NLP methods improved ML algorithms' classification capability using triage nursing and medical notes and structured clinical data compared to algorithms using only structured data. Feature engineering (FE) and class imbalance correction methods enhanced ML workflows' performance, but FE and eXplainable Artificial Intelligence (XAI) were underexplored in this field. Registration and funding. This systematic review has been registered (registration number: CRD42024604529) in the International Prospective Register of Systematic Reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=604529 . Funding for this work was provided by the National Council for Scientific and Technological Development (CNPq), Brazil.

SGLT-2 inhibitors (SGLT-2i) are linked to a higher risk of diabetic ketoacidosis (DKA). However, it is still unclear whether the severity of SGLT-2...

Randomised trials have long been recognised as the gold standard research tool for evidence-based medicine. The past decade has seen the emergence ...

The latest medical research on Emergency Medicine

Best evidence topic report: can intradermal sterile water injections provide effective pain relief in patients with renal colic?

Outcome of video laryngoscopy versus direct laryngoscopy for emergency tracheal intubation in emergency department: a propensity score matching analysis.

A randomized clinical trial of intranasal dexmedetomidine versus inhaled nitrous oxide for procedural sedation and analgesia in children.

Added value of inflammatory markers to vital signs for predicting mortality in patients with suspected infection: external validation and model development.

Sepsis management in pre-hospital care - the earlier, the better?

Is it time to reframe resuscitation in trauma?

Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.

Practical strategies for caring for patients with functional neurological disorder in the ED.

The NACA score predicts mortality in polytrauma patients before hospital admission: a registry-based study.

Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.

Chronic treatment with SGLT-2 inhibitors is associated with ICU admission and disease severity in patients with diabetic ketoacidosis: a propensity score-matched cohort study.

Review article: Primer for clinical researchers on innovative trial designs for emergency medicine.

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