AI Article Synopsis
- Researchers tested a machine learning-based multibiomarker panel to predict major adverse cardiovascular events (MACE) in patients with suspected myocardial infarction (MI).
- The study involved 748 patients, assessing a prognostic panel made up of four biomarkers, with a focus on MACE occurrence within one year.
- The model showed a high predictive ability with an area under the curve of 0.86 and a 99.4% negative predictive value at the optimal cut-off, indicating that a high prognostic score correlates with an increased risk for MACE.
Article Abstract
In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).
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| http://dx.doi.org/10.2217/bmm-2019-0584 | DOI Listing |
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