BACKGROUND: Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. METHODS: The myocardial-ischaemic-injury-index (MI3) algorithm was validated in a prespecified exploratory analysis using data from a multi-centre randomised trial done in Scotland, UK that included consecutive patients with suspected acute coronary syndrome undergoing serial high-sensitivity cardiac troponin I ...
Background: The accuracy of current prediction tools for ischaemic and bleeding events after an acut...
BACKGROUND: We aimed to derive and externally validate a 0/2-h algorithm using the high-sensitivity ...
Background A significant number of variables are obtained when characterizing patients suspected wit...
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infa...
Cardiovascular disease affects more than half of people in the United Kingdom and remains the most c...
Background Chest pain is a common presentation in the emergency department (ED). Variation in high s...
International audienceThis study proposes machine learning-based models to automatically evaluate th...
BACKGROUND The safety of the European Society of Cardiology (ESC) 0/1-h algorithm for rapid rule-...
Background: Patients have an estimated mortality of 15–20% within the first year following myocardia...
Abstract Aims Heart failure (HF) is one of the common adverse cardiovascular events after acute myoc...
Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for...
BACKGROUND: Early and accurate detection of short-term major adverse cardiac events (MACE) in patien...
Background: Early and accurate detection of short-term major adverse cardiac events (MACE) in patien...
Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, ...
BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-B...
Background: The accuracy of current prediction tools for ischaemic and bleeding events after an acut...
BACKGROUND: We aimed to derive and externally validate a 0/2-h algorithm using the high-sensitivity ...
Background A significant number of variables are obtained when characterizing patients suspected wit...
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infa...
Cardiovascular disease affects more than half of people in the United Kingdom and remains the most c...
Background Chest pain is a common presentation in the emergency department (ED). Variation in high s...
International audienceThis study proposes machine learning-based models to automatically evaluate th...
BACKGROUND The safety of the European Society of Cardiology (ESC) 0/1-h algorithm for rapid rule-...
Background: Patients have an estimated mortality of 15–20% within the first year following myocardia...
Abstract Aims Heart failure (HF) is one of the common adverse cardiovascular events after acute myoc...
Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for...
BACKGROUND: Early and accurate detection of short-term major adverse cardiac events (MACE) in patien...
Background: Early and accurate detection of short-term major adverse cardiac events (MACE) in patien...
Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, ...
BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-B...
Background: The accuracy of current prediction tools for ischaemic and bleeding events after an acut...
BACKGROUND: We aimed to derive and externally validate a 0/2-h algorithm using the high-sensitivity ...
Background A significant number of variables are obtained when characterizing patients suspected wit...