Clinical Significance of Early Echocardiographic Changes after Resuscitated Out-of-Hospital Cardiac Arrest

A machine learning algorithm to predict a culprit lesion after out of hospital cardiac arrest

BACKGROUND

We aimed to develop a machine learning algorithm to predict the presence of a culprit lesion in patients with out-of-hospital cardiac arrest (OHCA).

METHODS

We used the King's Out-of-Hospital Cardiac Arrest Registry, a retrospective cohort of 398 patients admitted to King's College Hospital between May 2012 and December 2017. The primary outcome was the presence of a culprit coronary artery lesion, for which a gradient boosting model was optimized to predict. The algorithm was then validated in two independent European cohorts comprising 568 patients.

RESULTS

A culprit lesion was observed in 209/309 (67.4%) patients receiving early coronary angiography in the development, and 199/293 (67.9%) in the Ljubljana and 102/132 (61.1%) in the Bristol validation cohorts, respectively. The algorithm, which is presented as a web application, incorporates nine variables including age, a localizing feature on electrocardiogram (ECG) (≥2 mm of ST change in contiguous leads), regional wall motion abnormality, history of vascular disease and initial shockable rhythm. This model had an area under the curve (AUC) of 0.89 in the development and 0.83/0.81 in the validation cohorts with good calibration and outperforms the current gold standard-ECG alone (AUC: 0.69/0.67/0/67).

CONCLUSIONS

A novel simple machine learning-derived algorithm can be applied to patients with OHCA, to predict a culprit coronary artery disease lesion with high accuracy.

British Cardiovascular Interventional Society Consensus Position Statement on Out-of-Hospital Cardiac Arrest 1: Pathway of Care

Out-of-hospital cardiac arrest (OHCA) affects 80,000 patients per year in the UK; despite improvements in care, survival to discharge remains lower than 10%. NHS England and several societies recommend all resuscitated OHCA patients be directly transferred to a cardiac arrest centre (CAC). However, evidence is limited that all patients benefit from transfer to a CAC, and there are significant organisational, logistic and financial implications associated with such change in policies. Furthermore, there is significant variability in interventional cardiovascular practices for OHCA. Accordingly, the British Cardiovascular Interventional Society established a multidisciplinary group to address variability in practice and provide recommendations for the development of cardiac networks. In this position statement, we recommend: the formal establishment of dedicated CACs; a pathway of conveyance to CACs; and interventional practice to standardise our approach. Further research is needed to understand the role of CACs and which interventions benefit patients with OHCA to support wide-scale changes in networks of care across the UK.