Management of patients suspected for non-ST elevation-acute coronary syndrome in the prehospital phase.

The management of patients with suspected acute coronary syndrome, especially in prehospital settings, is challenging. This Special Report focuses on studies in emergency medical services concerning chest pain patients' triage and risk stratification. In addition, it emphasizes advancements in point-of-care cardiac troponin testing. These developments are compared with in-hospital guidelines, proposing an initial framework for a new acute care pathway. This pathway integrates a risk stratification tool with high-sensitivity cardiac troponin testing, aiming to deliver optimal care and collaboration within the acute care chain. It has the potential to contribute to a significant reduction in hospital referrals, reduce observation time and overcrowding at emergency departments and hospital admissions.

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study.

BACKGROUND: Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital. OBJECTIVE: The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics. METHODS: Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model. RESULTS: The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%. CONCLUSIONS: The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

Improved prehospital triage for acute cardiac care: results from HART-c, a multicentre prospective study.

BACKGROUND: Cardiac symptoms are one of the most prevalent reasons for emergency department visits. However, over 80% of patients with such symptoms are sent home after acute cardiovascular disease has been ruled out. OBJECTIVE: The Hollands-Midden Acute Regional Triage-cardiology (HART-c) study aimed to investigate whether a novel prehospital triage method, combining prehospital and hospital data with expert consultation, could increase the number of patients who could safely stay at home after emergency medical service (EMS) consultation. METHODS: The triage method combined prehospital EMS data, such as electrocardiographic and vital parameters in real time, and data from regional hospitals (including previous medical records and admission capacity) with expert consultation. During the 6­month intervention and control periods 1536 and 1376 patients, respectively, were consulted by the EMS. The primary endpoint was the percentage change of patients who could stay at home after EMS consultation. RESULTS: The novel triage method led to a significant increase in patients who could safely stay at home, 11.8% in the intervention group versus 5.9% in the control group: odds ratio 2.31 (95% confidence interval (CI) 1.74-3.05). Of 181 patients staying at home, only 1 (< 1%) was later diagnosed with ACS; no patients died. Furthermore the number of interhospital transfers decreased: relative risk 0.81 (95% CI 0.67-0.97). CONCLUSION: The HART­c triage method led to a significant decrease in interhospital transfers and an increase in patients with cardiac symptoms who could safely stay at home. The presented method thereby reduced overcrowding and, if implemented throughout the country and for other medical specialties, could potentially reduce the number of cardiac and non-cardiac hospital visits even further.

Managing Hospital Capacity: Achievements and Lessons from the COVID-19 Pandemic.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic challenged health care systems in an unprecedented way. Due to the enormous amount of hospital ward and intensive care unit (ICU) admissions, regular care came to a standstill, thereby overcrowding ICUs and endangering (regular and COVID-19-related) critical care. Acute care coordination centers were set up to safely manage the influx of COVID-19 patients. Furthermore, treatments requiring ICU surveillance were postponed leading to increased waiting lists. HYPOTHESIS: A coordination center organizing patient transfers and admissions could reduce overcrowding and optimize in-hospital capacity. METHODS: The acute lack of hospital capacity urged the region West-Netherlands to form a new regional system for patient triage and transfer: the Regional Capacity and Patient Transfer Service (RCPS). By combining hospital capacity data and a new method of triage and transfer, the RCPS was able to effectively select patients for transfer to other hospitals within the region or, in close collaboration with the National Capacity and Patient Transfer Service (LCPS), transfer patients to hospitals in other regions within the Netherlands. RESULTS: From March 2020 through December 2021 (22 months), the RCPS West-Netherlands was requested to transfer 2,434 COVID-19 patients. After adequate triage, 1,720 patients with a mean age of 62 (SD = 13) years were transferred with the help of the RCPS West-Netherlands. This concerned 1,166 ward patients (68%) and 554 ICU patients (32%). Overcrowded hospitals were relieved by transferring these patients to hospitals with higher capacity. CONCLUSION: The health care system in the region West-Netherlands benefitted from the RCPS for both ward and ICU occupation. Due to the coordination by the RCPS, regional ICU occupation never exceeded the maximal ICU capacity, and therefore patients in need for acute direct care could always be admitted at the ICU. The presented method can be useful in reducing the waiting lists caused by the delayed care and for coordination and transfer of patients with new variants or other infectious diseases in the future.

Prehospital triage of patients with acute cardiac complaints: study protocol of HART-c, a multicentre prospective study.

INTRODUCTION: Emergency department (ED) overcrowding is a major healthcare problem associated with worse patient outcomes and increased costs. Attempts to reduce ED overcrowding of patients with cardiac complaints have so far focused on in-hospital triage and rapid risk stratification of patients with chest pain at the ED. The Hollands-Midden Acute Regional Triage-Cardiology (HART-c) study aimed to assess the amount of patients left at home in usual ambulance care as compared with the new prehospital triage method. This method combines paramedic assessment and expert cardiologist consultation using live monitoring, hospital data and real-time admission capacity. METHODS AND ANALYSIS: Patients visited by the emergency medical services (EMS) for cardiac complaints are included. EMS consultation consists of medical history, physical examination and vital signs, and ECG measurements. All data are transferred to a newly developed platform for the triage cardiologist. Prehospital data, in-hospital medical records and real-time admission capacity are evaluated. Then a shared decision is made whether admission is necessary and, if so, which hospital is most appropriate. To evaluate safety, all patients left at home and their general practitioners (GPs) are contacted for 30-day adverse events. ETHICS AND DISSEMINATION: The study is approved by the LUMC's Medical Ethics Committee. Patients are asked for consent for contacting their GPs. The main results of this trial will be disseminated in one paper. DISCUSSION: The HART-c study evaluates the efficacy and feasibility of a prehospital triage method that combines prehospital patient assessment and direct consultation of a cardiologist who has access to live-monitored data, hospital data and real-time hospital admission capacity. We expect this triage method to substantially reduce unnecessary ED visits.