Impact of Pre-Hospital Activation of STEMI on False Positive Activation Rate and Door to Balloon Time

Informing Utstein-style reporting guidelines for prehospital thrombolysis: A scoping review

BACKGROUND

Rural Australians with acute myocardial infarction (AMI) face higher mortality rates due to limited access to specialised cardiac services. Paramedic-administered prehospital thrombolysis (PHT) has emerged as an alternative to primary percutaneous intervention (pPCI) for patients facing barriers or delays to cardiac care. There is variability in PHT practices among Australian ambulance services, lacking standardised definitions and outcome measures. The aim of this scoping review was to identify quality indicators and influencing factors associated with outcomes for patients receiving PHT.

METHODS

A systematic search of literature in SCOPUS and Academic Search Complete, CINAHL and Health Source: Nursing/Academic Edition databases via EBSCO (Health) was conducted following the Joanna Briggs Institute methodology. Peer-reviewed studies from the past decade were screened using search criteria relevant to prehospital thrombolysis and quality indicators. Data extraction was performed and themed using five domains from the Utstein-style template commonly known for standardised prehospital cardiac arrest reporting.

RESULTS

After removing duplicates, the search yielded 3596 articles with 28 empirical studies meeting inclusion criteria for the review. These were primarily retrospective cohort studies performed in Australia, Canada and the United States. The scoping review identified 24 clinical quality indicators and factors related to Emergency Medical Service (EMS) systems, AMI recognition and ambulance dispatch, patient variables, PHT processes and patient outcomes. These findings correlate to the Donabedian structure-process-outcome quality of care model and have utility to inform future PHT reporting guidelines for jurisdictional ambulance services.

CONCLUSIONS

Given the variability in prehospital practice across Australian ambulance services, standardised reporting on quality indicators for PHT is needed. The Utstein-style template used to report data on pre-hospital cardiac arrest, trauma and airway management could be used for quality improvement in PHT. This review presents 24 quality indicators representing system, recognition and response, patient, process, and outcomes related to PHT. These results could be used to inform a future Delphi study and Utstein-like reporting guideline for prehospital thrombolysis.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Prehospital Activation of the Cardiac Catheterization Laboratory in ST-Segment-Elevation Myocardial Infarction for Primary Percutaneous Coronary Intervention

Background Prehospital activation of the cardiac catheter laboratory is associated with significant improvements in ST-segment-elevation myocardial infarction (STEMI) performance measures. However, there are equivocal data, particularly within Australia, regarding its influence on mortality. We assessed the association of prehospital activation on performance measures and mortality in patients with STEMI treated with primary percutaneous coronary intervention from the Queensland Cardiac Outcomes Registry (QCOR). Methods and Results Consecutive ambulance-transported patients with STEMI treated with primary percutaneous coronary intervention were analyzed from January 1, 2017 to December 31, 2020 from the QCOR. The total and direct effects of prehospital activation on the primary outcomes (30-day and 1-year cardiovascular mortality) were estimated using logistic regression analyses. Secondary outcomes were STEMI performance measures. Among 2498 patients (mean age: 62.2±12.4 years; 79.2% male), 73% underwent prehospital activation. Median door-to-balloon time (34 minutes [26-46] versus 86 minutes [68-113]; P<0.001), first-electrocardiograph-to-balloon time (83.5 minutes [72-98] versus 109 minutes [81-139]; P<0.001), and proportion of patients meeting STEMI targets (door-to-balloon <60 minutes 90% versus 16%; P<0.001), electrocardiograph-to-balloon time <90 minutes (62% versus 33%; P<0.001) were significantly improved with prehospital activation. Prehospital activation was associated with significantly lower 30-day (1.6% versus 6.6%; P<0.001) and 1-year cardiovascular mortality (2.9% versus 9.5%; P<0.001). After adjustment, no prehospital activation was strongly associated with increased 30-day (odds ratio [OR], 3.6 [95% CI, 2.2-6.0], P<0.001) and 1-year cardiovascular mortality (OR, 3.0 [95% CI, 2.0-4.6]; P<0.001). Conclusions Prehospital activation of cardiac catheterization laboratory for primary percutaneous coronary intervention was associated with significantly shorter time to reperfusion, achievement of STEMI performance measures, and lower 30-day and 1-year cardiovascular mortality.

A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

(1) Background: Acute pericarditis is often confused with ST-segment elevation myocardial infarction (STEMI) among patients presenting with acute chest pain in the emergency department (ED). Since a deep learning model (DLM) has been validated to accurately identify STEMI cases via 12-lead electrocardiogram (ECG), this study aimed to develop another DLM for the detection of acute pericarditis in the ED. (2) Methods: This study included 128 ECGs from patients with acute pericarditis and 66,633 ECGs from patients visiting the ED between 1 January 2010 and 31 December 2020. The ECGs were randomly allocated based on patients to the training, tuning, and validation sets, at a 3:1:1 ratio. We used raw ECG signals to train a pericarditis-DLM and used traditional ECG features to train a machine learning model. A human–machine competition was conducted using a subset of the validation set, and the performance of the Philips automatic algorithm was also compared. STEMI cases in the validation set were extracted to analyze the DLM ability of differential diagnosis between acute pericarditis and STEMI using ECG. We also followed the hospitalization events in non-pericarditis cases to explore the meaning of false-positive predictions. (3) Results: The pericarditis-DLM exceeded the performance of all participating human experts and algorithms based on traditional ECG features in the human–machine competition. In the validation set, the pericarditis-DLM could detect acute pericarditis with an area under the receiver operating characteristic curve (AUC) of 0.954, a sensitivity of 78.9%, and a specificity of 97.7%. However, our pericarditis-DLM also misinterpreted 10.2% of STEMI ECGs as pericarditis cases. Therefore, we generated an integrating strategy combining pericarditis-DLM and a previously developed STEMI-DLM, which provided a sensitivity of 73.7% and specificity of 99.4%, to identify acute pericarditis in patients with chest pains. Compared to the true-negative cases, patients with false-positive results using this strategy were associated with higher risk of hospitalization within 3 days due to cardiac disorders (hazard ratio (HR): 8.09; 95% confidence interval (CI): 3.99 to 16.39). (4) Conclusions: The AI-enhanced algorithm may be a powerful tool to assist clinicians in the early detection of acute pericarditis and differentiate it from STEMI using 12-lead ECGs.