Article: A locally optimised machine learning approach to early prognostication of long-term neurological outcomes after out-of-hospital cardiac arrest.
2024 Volume 10, Page(s) 20552076241234746
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 ... ...
| 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. |
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| Language | English |
| Publishing date | 2024-04-15 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2819396-9 |
| ISSN | 2055-2076 |
| ISSN | 2055-2076 |
| DOI | 10.1177/20552076241234746 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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