The unsupervised machine learning to analyze the use strategy of statins for ischaemic stroke patients with elevated transaminase.

ABSTRACT

BACKGROUND AND PURPOSE: Statins could elevate hepatic transaminase in ischemic stroke patients. There needed to be more evidence on which method stopped statins or adjusting the dose of statins was better for patients. And no evidence showed which way more suit for some patients. METHODS: We collected ischaemic stroke patients with elevated hepatic transaminase when they take statins. The outcome was a recurrent stroke rate, transaminase value after stopping or adjusted, mortality, and favorable functional outcome (FFO). We compare outcome events between the stopped group and the adjustment group. We grouped all patients by unsupervised machine learning and analyzed data characters by the different groups. RESULTS: The patients stopping statins had a higher stroke recurrence and rate of FFO (mRS 0-2), a lower mean value of transaminase, and mortality. By difference unsupervised machine learning group, the km2 group had the lowest stroke recurrence (p = 0.046), lowest mortality (p = 0.049), and highest FFO (p = 0.023). The patients of the km2 group were younger (p < 0.001), more male (p < 0.001), had lesser National Institutes of Health Stroke Scale (NIHSS) scores (p < 0.001), and had slightly higher values of blood pressure (p = 0.002). The group of unsupervised machine learning could improve models' performance. CONCLUSION: For ischemic patients with elevated hepatic transaminase, stopping statins temporarily was a better choice of treatment strategy. These patients who were younger, male, with a lesser NIHSS score at admission and a slightly higher blood lipid value at admission, could have had a better prognosis.