Figure 2 | Scientific Reports

Figure 2

From: Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species

Figure 2

Machine learning of cardiovascular risk factors including the platelet lipidome facilitates sub-phenotyping of CAD patients. (A) Medoid clustering with the corresponding standardized level (z scores) of the feature risk variables (LVEF, left ventricular ejection fraction; HDL, high-density lipoprotein; LDL, low-density lipoprotein; triglycerides; HbA1c; LPE, lysophosphatidylethanolamines; CAR, acylcarnitines; platelet aggregation). Remarkably, patients summarized in cluster 5 mainly showed aberrant platelet function and enhanced platelet CAR concentrations, whereas cluster 6 was exclusively characterized by increased LPE concentrations. (B) Number of patients with CAD by cluster according to conventional risk parameter with color indicating cut-off values of individual measurements. In addition, alongside median platelet LPE and CAR concentrations, median area under the curve (AUC) from merged collagen-, arachidonic acid-, adenosine diphosphate-, and thrombin-induced platelet aggregation was depicted by cluster to identify patients with platelet hyperreactivity and aberrant platelet lipid signatures. Error bars were constructed based on interquartile range (IQR).

Back to article page