A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest

Abstract

Background: Out-of-hospital cardiac arrest (OHCA) represents a major burden for society and health care, with an average incidence in adults of 67 to 170 cases per 100,000 person-years in Europe and in-hospital survival rates of less than 10%. Patients and practitioners would benefit from a prognostication tool for long-term good neurological outcomes.

Objective: We aim to develop a machine learning (ML) pipeline on a local database to classify patients according to their neurological outcomes and identify prognostic features.

Methods: We collected clinical and biological data consecutively from 595 patients who presented OHCA and were routed to a single regional cardiac arrest centre in the south of France. We applied recursive feature elimination and ML analyses to identify the main features associated with a good neurological outcome, defined as a Cerebral Performance Category score less than or equal to 2 at six months post-OHCA.

Results: We identified 12 variables 24 h after admission, capable of predicting a six-month good neurological outcome. The best model (extreme gradient boosting) achieved an AUC of 0.96 and an accuracy of 0.92 in the test cohort.

Conclusion: We demonstrated that it is possible to build accurate, locally optimised prediction and prognostication scores using datasets of limited size and breadth. We proposed and shared a generic machine-learning pipeline which allows external teams to replicate the approach locally.

Keywords: Machine learning; OHCA; neurological outcome; prognostication.

Figure 1.

Flow chart of the patient included in the study.^a

Figure 2.

Receiver operating curves and precision-recall curves for the four models in the test cohort^a,b; confusion matrix for neurological outcome prediction in (b) training cohort and (c) test cohort for the best model (xGBoost).

Figure 3.

Mean SHAP value for the top 12 features of the XGBoost model.

Figure 4.

SHAP dependence contribution plots, illustrating the link between individual feature values and SHAP values for the prediction of a good neurological outcome. For each feature, we identified the thresholds which differentiate between positive and negative SHAP values (vertical red lines). The grey histograms represent the distribution of patients for each feature value.