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Background The Scandinavian Neurotrauma Committee (SNC) has recommended the use of serum S100B as a biomarker for mild low-risk Traumatic brain injuries (TBI). This study aimed to assess the adherence to the SNC guidelines in clinical practice and the diagnostic performance of S100B in patients with TBI. The aims of this study were to examine adherence to the SNC guideline and the diagnostic accuracy of serum protein S100B. Methods Data of consecutive patients of 18 years and above who presented to the emergency department (ED) at Helsingborg Hospital with isolated head injuries, were retrieved from hospital records. Patients with multitrauma, follow-up visits, and visits managed by a nurse without physician involvement were excluded. Results A total of 1671 patients were included of which 93 (5.6%) had intracranial hemorrhage. CT scans were performed in 62% of patients. S100B was measured in 26% of patients and 30% of all measurements targeted the low-risk mild head injuries indicated by the guideline. S100B's recommended cut-off value (≥ 0.10 µg/L) had a 100% sensitivity, 47% specificity, 10.1% positive predictive value, and 100% negative predictive value—if applied to the target SNC category (SNC 4). If applied to all patients tested, the sensitivity was 93% for traumatic intracranial hemorrhage (TICH). Current ED practices were adherent to the SNC guideline in 55% of patients. Non-adherent practices occurred in 64% of patients with low-risk mild head injuries (SNC4) including overtesting or undertesting of S100B and CT scans. Conclusion Adherence to guidelines was low and associated with a higher admission rate than non-adherence practice but no significant increase in missed TICH or death associated with non-adherence to guideline was found. In routine care, we found that the sensitivity and NPV of serum protein S100B was excellent and safely ruled out TICH when measured in the patient category recommended by the guideline. However, measuring serum protein S100B in patients not recommended by the guideline rendered unacceptably low sensitivity with possible missed TICHs as a consequence. To further delineate the magnitude and impact of non-adherence, more studies are needed.

Background Current guidelines from Scandinavian Neuro Committee mandate a 24-hour observation for head trauma patients on anticoagulants, even with normal initial head CT scans, as a means not to miss delayed intracranial hemorrhages. This study aimed to assess the prevalence, and time to diagnosis, of clinically relevant delayed intracranial hemorrhage in head trauma patients treated with oral anticoagulants. Method Utilizing comprehensive two-year data from Region Skåne’s emergency departments, which serve a population of 1.3 million inhabitants, this study focused on adult head trauma patients prescribed oral anticoagulants. We identified those with intracranial hemorrhage within 30 days, defining delayed intracranial hemorrhage as a bleeding not apparent on their initial CT head scan. These cases were further defined as clinically relevant if associated with mortality, any intensive care unit admission, or neurosurgery. Results Out of the included 2,362 head injury cases (median age 84, 56% on a direct acting oral anticoagulant), five developed delayed intracranial hemorrhages. None of these five cases underwent neurosurgery nor were admitted to an intensive care unit. Only two cases (0.08%, 95% confidence interval [0.01–0.3%]) were classified as clinically relevant, involving subdural hematomas in patients aged 82 and 87 years, who both subsequently died. The diagnosis of these delayed intracranial hemorrhages was made at 4 and 7 days following initial presentation to the emergency department. Conclusion In patients with head trauma, on oral anticoagulation, the incidence of clinically relevant delayed intracranial hemorrhage was found to be less than one in a thousand, with detection occurring four days or later after initial presentation. This challenges the effectiveness of the 24-hour observation period recommended by the Scandinavian Neurotrauma Committee guidelines, suggesting a need to reassess these guidelines to optimise care and resource allocation. Trial registration This is a retrospective cohort study, does not include any intervention, and has therefore not been registered.

Background Patients discharged with nonspecific diagnoses after acute hospital care are frequent and represent potential diagnostic uncertainty at discharge. Adverse outcomes indicate missed diagnoses with a potential for improving patient safety. However, research and interventions are limited by population heterogeneity. We aimed to identify clusters of patients discharged with nonspecific diagnoses by employing unsupervised machine learning and to assess the risk of readmission and mortality of each cluster. Methods Observational, register-based study of emergency department arrivals discharged with nonspecific diagnoses (ICD-10: R and Z03 chapters) from March 2019 to February 2020 in Denmark. We applied partitional (k-prototypes) and hierarchical (agglomerative) clustering based on demographics, socioeconomics, comorbidities, administrative information, biochemistry, and 50 nonspecific discharge diagnosis groups. The risk of 30-day readmission and mortality after discharge was assessed as cumulative incidence for each cluster. Results We included 92,650 patients. A 20 clusters k-prototypes model best fitted our data. Clusters 1–5 were differentiated by no or limited biochemistry across different age and comorbidity patterns. Clusters 6–9 consisted mainly of young adults with low comorbidity, except Cluster 9 with notable neuropsychiatric and substance abuse comorbidities. Clusters 10–20 described the older patients: 10–14 with single comorbidities and 15–20 with substantial comorbidity of different cooccurring patterns. The risk of 30-day readmission and mortality ranged from 5% to 27% and 0% to 9% across clusters, respectively. Conclusion Patients with nonspecific discharge diagnoses after acute hospital contacts can be grouped into 20 distinct clusters based on clinical, socioeconomic, administrative, and biochemical features. The clusters can be used to form delimited populations allowing for better and more individualized prediction models.

At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. Transfer learning has been a successful strategy for mitigating data requirements in other applications, but the benefits for predicting AMI are understudied. Here we show that a straightforward application of transfer learning leads to large improvements also in this domain. We pre-train models to classify sex and age using a collection of 840 k ECGs from non-chest-pain patients, and fine-tune the resulting models to predict AMI using 44 k ECGs from chest-pain patients. The results are compared with models trained without transfer learning. We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. This suggests that even a simple form of transfer learning from a moderately sized dataset of non-chest-pain ECGs can lead to major improvements in predicting AMI.

Background Shock is a condition with high mortality even with early intervention and treatment. Usual care for shock and hypotension in the Emergency Department (ED) is intravenous fluid resuscitation which can lead to fluid overload and other complications. When fluid therapy fails or risk of complications are high, the next treatment step is the use of vasopressors for stabilisation. Noradrenaline therapy for hypotension and shock are commonly used in ED’s outside Scandinavia, but the evidence on the optimal initiation time is sparse. The lack of noradrenaline therapy in Scandinavia provides a unique environment to investigate the possible implications of early initiation. The aim of this trial is to investigate whether the use of early initiated noradrenaline compared to ED fluid therapy can improve blood pressure goals and by that, reduce the need for ICU admittance. Methods This protocol describes a pragmatic, multi-center, superiority randomized controlled trial, randomizing patients with hypotension to intervention or control. Eligible patients are ≥ 18-year-old who have received at least 500 ml intravenous fluids (including prehospital administration), and without suspected cardiogenic, haemorrhagic, anaphylactic, or neurogenic causes, or require direct ICU admittance due to non-hemodynamic severe organ failure. The intervention group receives noradrenaline initiated at 0.05 mcg/kg/min with a maximum of 0.15 mcg/kg/min through a peripheral venous catheter for up to 24 h. The control group receives usual care. Treatment is targeted for a systolic blood pressure ≥ 100 mmHg, a mean arterial pressure ≥ 65 mmHg or a clinician defined blood pressure target. We require a sample size of 320 patients to show a significant difference in proportion of patients achieving shock control within 90 min (primary endpoint). Key secondary outcomes include ICU free days alive within 30-days and 30-day all-cause mortality. Discussion Previous prospective randomized trials on early peripheral noradrenaline treatment for shock are sparse and are investigated in settings where noradrenaline use is already usual care. Since noradrenaline are not used as standard treatment for shock in Scandinavian EDs, this provides a unique opportunity not only to investigate the early initiation of noradrenaline for shock, but also comparing it directly to ED fluid only approach. Trial registration : EU CT ID 2023-504584-16-00. ClinicalTrials.gov NCT05931601. URL: https://classic.clinicaltrials.gov/ct2/show/NCT05931601

ObjectivesEmerging evidence supports that PR interval prolongation is associated with increased mortality. However, most previous studies have limited confounder control, and clinical impact in a population of acute ill patients is unknown. The aim of this study was to investigate whether 1-year all-cause mortality was increased in patients presenting with PR interval prolongation in the emergency department (ED).Design and settingWe conducted a register-based cohort study in two Swedish and two Danish EDs. We included all adult patients with an ECG performed at arrival to the Danish EDs during March 2013 to May 2014 and Swedish EDs during January 2010 to January 2011. Using propensity score matching, we analysed HR for 1-year all-cause mortality comparing patients with PR interval prolongation (>200 ms) and normal PR interval (120–200 ms).Participants and resultsWe included 106 124 patients. PR interval prolongation occurred in 8.9% (95% CI 8.7% to 9.0%); these patients were older and had more comorbidity than those with a normal PR interval. The absolute 1-year risk of death was 13% (95% CI 12.3% to 13.7%) for patients with PR interval prolongation and 7.9% (95% CI 7.7% to 8.0%) for those without. After confounder adjustments by propensity score matching, PR interval prolongation showed no association with 1-year mortality with a HR of 1.00 (95% CI 0.93% to 1.08%).ConclusionPR interval prolongation does not constitute an independent risk factor for 1-year mortality in ED patients.

The objective was to develop an artificial intelligence (AI) model for predicting acute coronary occlusion myocardial infarction (OMI) in patients with chest pain at the emergency department (ED), using information that is widely available early in the ED assessment. In a cohort of 24,511 consecutive adult ED patients with chest pain from 5 Swedish hospitals, OMI cases were identified through register data and manual review of health records and angiographies. Ambulance patients bypassing the ED due to ST-elevation myocardial infarction (STEMI) were not included in the cohort. A deep-learning AI model was created to predict OMI using the electrocardiogram, optionally combined with other early ED data, including medical history and initial lab values. The model was internally validated on held-out data and compared with the STEMI criteria. A total of 467 patients (1.9%) were identified as OMI, corresponding to 29% of all acute myocardial infarction cases. The 30-day mortality rate was 6.6% for OMI, compared with 3.3% for non-OMI. Only 5.4% of the OMI cases received angiography within the guideline-recommended maximum of 90 minutes after ED arrival. The AI model achieved an area under the receiver operating characteristic (AUC) of 95.3% (95% CI, 93.8%-97.3%), with a sensitivity of 62% compared with 27% for the STEMI criteria (difference 34.5%; 95% CI, 22.9%-45.2%) at the same specificity (97.4%). Our AI model identified OMI in ED patients with chest pain with an AUC of 95%, doubling sensitivity compared with the STEMI criteria at the same specificity. Using the model could reduce time to intervention, as only about 1 in 20 OMI cases currently receive timely angiography.

Background/aimIn ED chest pain patients, a 0-hour/1-hour protocol based on high sensitivity cardiac troponin T (hs-cTnT) tests combined with clinical risk stratification in diagnosing acute coronary syndrome is recommended. Two of the most promising risk stratification tools are the History, ECG, Age, Risk Factors and Troponin (HEART) and Emergency Department Assessment of Chest Pain (EDAC) scores. Few studies have assessed the diagnostic accuracy of the 0-hour/1-hour hs-cTnT protocol when combined with HEART score, and none with EDACS. In ED chest pain patients, we aimed to evaluate the diagnostic accuracy of a 0-hour/1-hour hs-cTnT protocol combined the HEART Pathway, or the EDACS accelerated diagnostic pathway (EDACS-ADP).MethodsThis was a secondary analysis of data from a prospective observational study enrolling 1167 ED chest pain patients who visited the ED at Skåne University Hospital in Lund, Sweden in the period between February 2013 and April 2014. HEART and EDAC scores were assessed together with hs-cTnT at 0 and 1 hour and compared with HEART score alone. Sensitivity, specificity, negative predictive value (NPV) and likelihood ratios were evaluated. The primary outcome was major adverse cardiac events (MACE) including unstable angina within 30 days. The secondary outcome was index visit acute myocardial infarction (AMI).ResultsA total of 939 patients were included in the final analysis. When combined with 0-hour/1-hour hs-cTnT testing, the HEART Pathway and EDACS-ADP identified 49.8% and 49.6% of the patients for rule-out, with NPVs for 30-day MACE of 99.8% and 99.1%, compared with the HEART score alone that identified 53.4% of the patients for rule-out with NPV of 99.2%. The NPV for index visit AMI were 100%, 99.8% and 99.2%, respectively.ConclusionThe combination of the HEART Pathway or the EDACS-ADP with a 0-hour/1-hour hs-cTnT protocol allows safe and early rule-out in a large proportion of ED chest pain patients.

Background Emergency departments use triage to identify time-critical patients and reduce waiting times by assigning higher priority. However, no existing method directly measures this core clinical function. We developed and validated Triage Effectiveness (TE) as a framework for quantifying how well triage systems reduce waiting times for time-critical patients. Methods Using data from 463,209 visits across eight emergency departments, we developed TE as a scale where 0% equals a first-come-first-serve strategy (no triage) and 100% equals perfect prioritization. The framework includes two complementary measures: 1. Waiting Time-based TE (WTE) measuring actual waiting time reduction, where 100% represents zero waiting time, and 2. Rank-based TE (RTE) measuring queue position improvement where 100% represents placement first in queue. Both can be calculated as theoretical TE (based only on priorities) and observed TE (actual clinical performance). We validated WTE and RTE calculations using analytical queueing models adapted for classification uncertainty and assessed transferability across hospitals. Results Queueing theory validation showed strong alignment with both WTE and RTE calculations, with RTE demonstrating greater robustness and less sensitivity to ED utilization patterns. TE increased with accuracy and produced negative values when triage performance was worse than chance. When applying the framework we found substantial gaps between theoretical and observed TE across all emergency departments, indicating significant post triage reprioritization. Conclusions The TE framework provides the first method for directly measuring the effectiveness of triage while utilizing full ordinal priority information. TE can assess the effectiveness of initial triage and the impact of post-triage re-prioritization.

BackgroundStudies carried out in simulated environments suggest that checklists improve the management of surgical and intensive care crises. Whether checklists improve the management of medical crises simulated in actual emergency departments (EDs) is unknown.MethodsEight crises (anaphylactic shock, life-threatening asthma exacerbation, haemorrhagic shock from upper gastrointestinal bleeding, septic shock, calcium channel blocker poisoning, tricyclic antidepressant poisoning, status epilepticus, increased intracranial pressure) were simulated twice (once with and once without checklist access) in each of four EDs—of which two belong to an academic centre—and managed by resuscitation teams during their clinical shifts. A checklist for each crisis listing emergency interventions was derived from current authoritative sources. Checklists were displayed on a screen visible to all team members. Crisis and checklist access were allocated according to permuted block randomisation. No team member managed the same crisis more than once. The primary outcome measure was the percentage of indicated emergency interventions performed.ResultsA total of 138 participants composing 41 resuscitation teams performed 76 simulations (38 with and 38 without checklist access) including 631 interventions. Median percentage of interventions performed was 38.8% (95% CI 35% to 46%) without checklist access and 85.7% (95% CI 80% to 88%) with checklist access (p=7.5×10−8). The benefit of checklist access was similar in the four EDs and independent of senior physician and senior nurse experience, type of crisis and use of usual cognitive aids. On a Likert scale of 1–6, most participants agreed (gave a score of 5 or 6) with the statement ‘I would use the checklist if I got a similar case in reality’.ConclusionIn this multi-institution study, checklists markedly improved local resuscitation teams’ management of medical crises simulated in situ, and most personnel reported that they would use the checklists if they had a similar case in reality.