Complications in Emergency Department Patients with Acute Coronary Syndrome with Contemporary Care.

INTRODUCTION: With the implementation of early reperfusion therapy, the number of complications in patients with acute coronary syndrome (ACS) has diminished significantly. However, ACS patients are still routinely admitted to units with high-level monitoring such as the coronary or intensive care unit (CCU/ICU). The cost of these admissions is high and there is often a shortage of beds. The aim of this study was to analyze the complications in contemporary emergency department (ED) patients with ACS and to map patient management. METHODS: This observational study was a secondary analysis of data collected in the ESC-TROP trial (NCT03421873) that included 26,545 consecutive chest pain patients ≥18 years at five Swedish EDs. Complications were defined as the following within 30 days: death, cardiac arrest, cardiogenic shock, pulmonary edema, severe ventricular arrhythmia, high-degree atrioventricular (AV) block that required a pacemaker, and mechanical complications such as papillary muscle rupture, cardiac tamponade, or ventricular septum defects (VSDs). Complications were identified via diagnosis and/or intervention codes in the database, and manual chart review was performed in cases with complications. RESULTS: Of all 26,545 patients, 2,463 (9.3%) were diagnosed with ACS, and 151 of these (6.1%) suffered any complication within 30 days. Mean age was higher in patients with (79.2 years) than without (69.4 years) complications, and more were female (39.7% vs. 33.0%). Eighty-four (3.4% of all ACS patients) patients died, 33 (1.3%) had cardiac arrest, 22 (0.9%) respiratory failure, 13 (0.5%) high-degree AV block, 10 (0.4%) cardiogenic shock, 12 (0.5%) severe ventricular arrhythmia, and 2 each (<0.1%) had VSD or cardiac tamponade. Almost 30% of the complications were present already at the ED, and 40% of patients with complications were not admitted to the CCU/ICU. Only 80 (53%) of the patients with complications underwent coronary angiography and 62 (41%) were revascularized with percutaneous coronary intervention or coronary artery bypass grafting. CONCLUSION: With current care, serious complications occurred in only 6 out of 100 ACS patients, and 2 of these complications were present already at the ED. Four out of 10 ACS patients with complications were not admitted to the CCU/ICU and about half did not undergo coronary angiography. Further research is needed to improve risk assessment in ED ACS patients, which may allow more effective use of cardiac monitoring and hospital resources.

Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients.

At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice.

Clinical use of an emergency manual by resuscitation teams and impact on performance in the emergency department: a prospective mixed-methods study protocol.

INTRODUCTION: Simulation-based studies indicate that crisis checklist use improves management of patients with critical conditions in the emergency department (ED). An interview-based study suggests that use of an emergency manual (EM)-a collection of crisis checklists-improves management of clinical perioperative crises. There is a need for in-depth prospective studies of EM use during clinical practice, evaluating when and how EMs are used and impact on patient management. METHODS AND ANALYSIS: This 6-month long study prospectively evaluates a digital EM during management of priority 1 patients in the Skåne University Hospital at Lund's ED. Resuscitation teams are encouraged to use the EM after a management plan has been derived ('Do-Confirm'). The documenting nurse activates and reads from the EM, and checklists are displayed on a large screen visible to all team members. Whether the EM is activated, and which sections are displayed, are automatically recorded. Interventions performed thanks to Do-Confirm EM use are registered by the nurse. Fifty cases featuring such interventions are reviewed by specialists in emergency medicine blinded to whether the interventions were performed prior to or after EM use. All interventions are graded as indicated, of neutral relevance or not indicated. The primary outcome measures are the proportions of interventions performed thanks to Do-Confirm EM use graded as indicated, of neutral relevance, and not indicated. A secondary outcome measure is the team's subjective evaluation of the EM's value on a Likert scale of 1-6. Team members can report events related to EM use, and information from these events is extracted through structured interviews. ETHICS AND DISSEMINATION: The study is approved by the Swedish Ethical Review Authority (Dnr 2022-01896-01). Results will be published in a peer-reviewed journal and abstracts submitted to national and international conferences to disseminate our findings. TRIAL REGISTRATION NUMBER: NCT05649891.

[Analysis of acid-base disturbances in the emergency department]. / ABC om Syra­bastolkning på akuten.

The analysis of acid-base disturbances contributes to the diagnostic work-up of critically ill patients. Most emergency departments are equipped with blood gas point-of-care analyzers that quantify within minutes pH, pCO2, standard bicarbonate, standard base excess, sodium and chloride levels. This article provides a pragmatic stepwise approach to the analysis of acid-base disturbances in the emergency department. Standard base excess is used to assess the adequacy of the secondary (compensatory) response. Calculation of the anion gap based on the actual bicarbonate is used to identify the coexistence of metabolic acidosis and metabolic alkalosis. The delta anion gap allows for the identification of measurement errors, such as falsely elevated lactate and chloride values, which in turn may provide diagnostic clues.

Machine learning compared with rule-in/rule-out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations.

OBJECTIVE: Computerized decision-support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high-sensitivity cardiac troponin T (hs-cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI. METHODS: In this register-based, cross-sectional diagnostic study conducted retrospectively based on 5695 chest pain patients at 2 hospitals in Sweden 2013-2014 we used 5-fold cross-validation 200 times in order to compare the performance of an artificial neural network (ANN) with European guideline-recommended 0/1- and 0/3-hour algorithms for hs-cTnT and with logistic regression without interaction terms. Primary outcome was the size of the intermediate risk group where AMI could not be ruled in or out, while holding the sensitivity (rule-out) and specificity (rule-in) constant across models. RESULTS: ANN and logistic regression had similar (95%) areas under the receiver operating characteristics curve. In patients (n = 4171) where the timing requirements (0/1 or 0/3 hour) for the sampling were met, using ANN led to a relative decrease of 9.2% (95% confidence interval 4.4% to 13.8%; from 24.5% to 22.2% of all tested patients) in the size of the intermediate group compared to the recommended algorithms. By contrast, using logistic regression did not substantially decrease the size of the intermediate group. CONCLUSION: Machine learning algorithms allow for flexibility in sampling and have the potential to improve risk assessment among chest pain patients at the ED.