The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: The development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene

Abstract

One of the main problems currently facing the delivery of safe and effective emergency care is excess demand, which causes congestion at different time points in a patient’s journey. The modern case-mix of prehospital patients is broad and complex, diverging from the traditional ‘time critical accident and emergency’ patients. It now includes many low-acuity patients and those with social care and mental health needs. In the ambulance service, transport decisions are the hardest to make and paramedics decide to take more patients to the ED than would have a clinical benefit. As such, this study asked the following research questions: In adult patients attending the ED by ambulance, can prehospital information predict an avoidable attendance? What is the simulated transportability of the model derived from the primary outcome? A linked dataset of 101,522 ambulance service and ED ambulance incidents linked to their respective ED care record from the whole of Yorkshire between 1st July 2019 and 29th February 2020 was used as the sample for this study. A machine learning method known as XGBoost was applied to the data in a novel way called Internal-External Cross Validation (IECV) to build the model. The results showed great discrimination with a C-statistic of 0.81 (95%CI 0.79–0.83) and excellent calibration with an O:E ratio was 0.995 (95% CI 0.97–1.03), with the most important variables being a patient’s mobility, their physiological observations and clinical impression with psychiatric problems, allergic reactions, cardiac chest pain, head injury, non-traumatic back pain, and minor cuts and bruising being the most important. This study has successfully developed a decision-support model that can be transformed into a tool that could help paramedics make better transport decisions on scene, known as the SINEPOST model. It is accurate, and spatially validated across multiple geographies including rural, urban, and coastal. It is a fair algorithm that does not discriminate new patients based on their age, gender, ethnicity, or decile of deprivation. It can be embedded into an electronic Patient Care Record system and automatically calculate the probability that a patient will have an avoidable attendance at the ED, if they were transported. This manuscript complies with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement (Moons KGM, 2015).

Artificial Intelligence: its Future and Impact on Acute Medicine

“…The applications of AI in this domain hold immense possibilities, with notable focus areas including triage, providing suggestions for diagnostic workup, supporting clinical decisionmaking concerning medications and interventions, and offering accurate prognoses of disease progression or outcomes 6 . Over the last few years, we have seen the first examples of such tools being developed and tested within research environments, such as a risk prediction model to support ambulance transport decisions 7 , an AI triage tool in the emergency department (ED) 8 , a screening tool for early sepsis detection 9 , a blood culture stewardship tool 10 , a machine learning tool for predicting ciprofloxacin resistance 11 , an AI audit system to minimize prescription errors 12 , a tool for estimating time to emergency readmissions 13 , and a model to predict admission to the neuro intensive care unit directly from the ED 14 . Furthermore, there is potential in harnessing AI tools before patients reach the ED or an acute care setting, leveraging data from devices and smartwatches for remote patient monitoring, alerting in a timely manner, and triage 15 .…”