Background: Prehospital care has changed in recent decades. Advanced assessments and decisions are made early in the care chain. Patient assessments form the basis of a decision relating to prehospital treatment and the level of care. This development imposes heavy demands on the ability of emergency medical service (EMS) clinicians properly to assess the patient. EMS clinicians have a number of assessment instruments and triage systems available to support their decisions. Many of these instruments are based on vital signs and can sometimes miss timesensitive conditions. With this commentary, we would like to start a discussion to agree on definitions of temporal states in the prehospital setting and ways of recognising patients with time-sensitive conditions in the most optimal way. Main body: There are several articles discussing the identification and management of time-sensitive conditions. In these articles, neither definitions nor terminology have been uniform. There are a number of problems associated with the definition of time-sensitive conditions. For example, intoxication can be minor but also life threatening, depending on the type of poison and dose. Similarly, diseases like stroke and myocardial infarction can differ markedly in terms of severity and the risk of life-threatening complications. Another problem is how to support EMS clinicians in the early recognition of these conditions. It is well known that many of them can present without a deviation from normal in vital signs. It will most probably be impossible to introduce specific decision support tools for every individual time-sensitive condition. However, there may be information in the type and intensity of the symptoms patients present. In future, biochemical markers and machine learning support tools may help to identify patients with time-sensitive conditions and predict mortality at an earlier stage. Conclusion: It may be of great value for prehospital clinicians to be able to describe time-sensitive conditions. Today, neither definitions nor terminology are uniform. Our hope is that this commentary will initiate a discussion on the issue aiming at definitions of time-sensitive conditions in prehospital care and how they should be recognised in the most optimal fashion.

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ObjectivesTo describe contemporary characteristics and diagnoses in prehospital patients with chest pain and to identify factors suitable for the early recognition of high-risk and low-risk conditions.DesignProspective observational cohort study.SettingTwo centre study in a Swedish county emergency medical services (EMS) organisation.ParticipantsUnselected inclusion of 2917 patients with chest pain contacting the EMS due to chest pain during 2018.Primary outcome measuresLow-risk or high-risk condition, that is, occurrence of time-sensitive diagnosis on hospital discharge.ResultsOf included EMS missions, 68% concerned patients with a low-risk condition without medical need of acute hospital treatment in hindsight. Sixteen per cent concerned patients with a high-risk condition in need of rapid transport to hospital care. Numerous variables with significant association with low-risk or high-risk conditions were found. In total high-risk and low-risk prediction models shared six predictive variables of which ST-depression on ECG and age were most important. Previously known risk factors such as history of acute coronary syndrome, diabetes and hypertension had no predictive value in the multivariate analyses. Some aspects of the symptoms such as pain intensity, pain in the right arm and paleness did on the other hand appear to be helpful. The area under the curve (AUC) for prediction of low-risk candidates was 0.786 and for high-risk candidates 0.796. The addition of troponin in a subset increased the AUC to >0.8 for both.ConclusionsA majority of patients with chest pain cared for by the EMS suffer from a low-risk condition and have no prognostic reason for acute hospital care given their diagnosis on hospital discharge. A smaller proportion has a high-risk condition and is in need of prompt specialist care. Building models with good accuracy for prehospital identification of these groups is possible. The use of risk stratification models could make a more personalised care possible with increased patient safety.