Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease

BACKGROUND Three-vessel disease(TVD)with a SYNergy between PCI with TAXus and cardiac surgery(SYNTAX)score of≥23 is one of the most severe types of coronary artery disease.We aimed to take advantage of machine learning to help in de-cision-making and prognostic evaluation in such patients.METHODS We analyzed 3786 patients who had TVD with a SYNTAX score of≥23,had no history of previous revascularization,and underwent either coronary artery bypass grafting(CABG)or percutaneous coronary intervention(PCI)after enrollment.The patients were randomly assigned to a training group and testing group.The C4.5 decision tree algorithm was applied in the training group,and all-cause death after a median follow-up of 6.6 years was regarded as the class label.RESULTS The decision tree algorithm selected age and left ventricular end-diastolic diameter(LVEDD)as splitting features and divided the patients into three subgroups:subgroup 1(age of≤67 years and LVEDD of≤53 mm),subgroup 2(age of≤67 years and LVEDD of>53 mm),and subgroup 3(age of>67 years).PCI conferred a patient survival benefit over CABG in sub-group 2.There was no significant difference in the risk of all-cause death between PCI and CABG in subgroup 1 and subgroup 3 in both the training data and testing data.Among the total study population,the multivariable analysis revealed significant dif-ferences in the risk of all-cause death among patients in three subgroups.CONCLUSIONS The combination of age and LVEDD identified by machine learning can contribute to decision-making and risk assessment of death in patients with severe TVD.The present results suggest that PCI is a better choice for young patients with severe TVD characterized by left ventricular dilation.

The effect of carbon nanotubes on osteogenic functions of adipose-derived mesenchymal stem cells in vitro and bone formation in vivo compared with that of nano-hydroxyapatite and the possible mechanism

It has been well recognized that the development and use of artificial materials with high osteogenic ability is one of the most promising means to replace bone grafting that has exhibited various negative effects.The biomimetic features and unique physiochemical properties of nanomaterials play important roles in stimulating cellular functions and guiding tissue regeneration.But efficacy degree of some nanomaterials to promote specific tissue formation is still not clear.We hereby comparatively studied the osteogenic ability of our treated multiwalled carbon nanotubes(MCNTs)and the main inorganic mineral component of natural bone,nano-hydroxyapatite(nHA)in the same system,and tried to tell the related mechanism.In vitro culture of human adiposederived mesenchymal stem cells(HASCs)on the MCNTs and nHA demonstrated that although there was no significant difference in the cell adhesion amount between on the MCNTs and nHA,the cell attachment strength and proliferation on the MCNTs were better.Most importantly,the MCNTs could induce osteogenic differentiation of the HASCs better than the nHA,the possible mechanism of which was found to be that the MCNTs could activate Notch involved signaling pathways by concentrating more proteins,including specific bone-inducing ones.Moreover,the MCNTs could induce ectopic bone formation in vivo while the nHA could not,which might be because MCNTs could stimulate inducible cells in tissues to form inductive bone better than nHA by concentrating more proteins including specific bone-inducing ones secreted from M2 macrophages.Therefore,MCNTs might be more effective materials for accelerating bone formation even than nHA.